Generative-AI Integrated Advanced Data Science and AI Course

Especially Designed For Working Professionals

Generative AI Integrated Advanced Data Science & AI Course

Generative-AI Integrated
Curriculum

In Collaboration with

Microsoft Partnered Advanced Data Science and AI Course

Learn From IIT, NIT and Top MNC Professionals

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Live Interactive Classes

510+

Hiring Partners

100%

Guaranteed Job Referrals

79%

Avg. Salary Hike

Generative-AI Integrated Advanced Data Science and AI Course

Especially Designed For Working Professionals

Generative AI Integrated Advanced Data Science & AI Course

Generative AI-Integrated Curriculum

In Collaboration with

Microsoft Partnered Advanced Data Science and AI Course
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Advanced Data Science and AI Course Overview

Advanced Data Science and AI Course Overview

The Advanced Data Science and AI Course is designed for working professionals, offering 480+ hours of live training, 28+ industry projects, and a 3-year flexible subscription. This Generative AI integrated course provides dual certification, 100% job assistance, and personalised mentorship from experts, ensuring you gain practical skills and industry recognition to advance your career in data science and AI.

The Advanced Data Science and AI Course is designed for working professionals, offering 480+ hours of live training, 28+ industry projects, and a 3-year flexible subscription. This Generative AI integrated course provides dual certification, 100% job assistance, and personalised mentorship from experts, ensuring you gain practical skills and industry recognition to advance your career in data science and AI.

Advanced Data Science and AI Course Key Skills and Features

Advanced Data Science and AI Course Key Skills and Features

Features Built on Industry Insights for Unmatched Success!

Key Program Features

Key Skills Covered

Key Program Features

Key Skills Covered

Skills Covered

Dual Certified Advanced Data Science and AI Program

Dual Certified Advanced Data Science and AI Program

Advanced Data Science & AI Course Certification

1stepGrow Project Experience Certificate

Gain Competitive Edge With Real-World Projects

Advanced Data Science & AI Course Microsoft Asure AI Fundamentals Certification

Microsoft AI Certification

Boost Your Career With Microsoft Certification

Advanced Data Science & AI Course Certification

1stepGrow Project Experience Certificate

Gain Competitive Edge With Real-World Projects

Advanced Data Science & AI Course Microsoft Asure AI Fundamentals Certification

Microsoft AI Certification

Boot Your Career With Microsoft Certification

Who This Advanced Data Science and AI Program Is For?

Who This Advanced Data Science and AI Program Is For?

Education

Graduates from computer science, mathematics, or a related field

Advanced Data Science Course Work Experience Qualification

Work experience

Professionals with experience in technology background

Advanced Data Science Course Career Stage Qualification

Career stage

Early to mid-career professionals seeking career leap

Aspirations

Ambitious individuals aiming for hands-on experience

Get Your Dream Job With Highest Possible Pay

Harness Extensive Industry Network of 510+ Companies with Relevant Skills

Get Your Dream Job With Highest Possible Pay

Harness Extensive Industry Network of 510+ Companies with Relevant Skills

Access to job openings and referrals from leading firms

Unlimited job support with resume
building

Upgrade profile with industry relevant
projects

Network with professionals and experts
in the field

Access to job openings and referrals from leading firms

Unlimited job support with resume building

Upgrade profile with industry relevant projects

Network with professionals and experts in the field

Syllabus | Advanced Certification in Data Science and AI Course

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Syllabus | Advanced Certification in Data Science and AI Course

Explore a meticulously designed syllabus that blends fundamental knowledge with cutting-edge techniques. Our Advanced Certification in Data Science and AI Course offers an extensive curriculum that immerses you in practical applications.

Explore a meticulously designed syllabus that blends fundamental knowledge with cutting-edge techniques. Our Advanced Certification in Data Science and AI Course offers an extensive curriculum that immerses you in practical applications.

UNIT 1: Orientation (8 Hours)

This unit serves as a primer for data science, introducing key tools and concepts. It’s designed to equip non-programmers with foundational Python skills, facilitating a deeper understanding and practical application throughout the course.

 

Module 1: Introduction To Data Science, Analytics & Artificial Intelligence

  • Introduction to tools, key concepts, and definitions
  • Real-time project applications in different domains
  • Practical applications of data science in various industries

 

Module 2: Fundamentals of Programming

  • Introduction to Python tools
  • Installation of Python
  • Python Fundamentals

 

Tools Covered: Python, Anaconda, Jupyter, Google Colab

 

Module 3: Fundamentals of Statistics

  • Importance and Use of Statistics in Data Science
  • Descriptive Statistics & Predictive Statistics
  • Learn how predictive Statistics connects with Machine Learning

 

Note:

Module 2 and Module 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

UNIT 2: Portfolio Building (6 hours)

This unit provides an extensive roadmap for building a robust portfolio in data science. You’ll master GitHub, a version control system, for efficient collaboration and project management. Additionally, you’ll harness LinkedIn‘s power for networking and career advancement.

 

Module 1: Git & GitHub (VCS)

  • Introduction to Version Control Systems
  • Installing and Configuring Git
  • Git Essentials
  • Branching and Merging
  • GitHub Essentials
  • Collaborating on GitHub
  • Forking repositories
  • Creating pull requests
  • Best Practices and Workflows

 

Class Hands-On: Initiate, collaborate, and work on a real-time project

 

Tools Covered: Git, GitHub

 

Module 2: LinkedIn Profile building

  • Introduction to LinkedIn as a Professional Networking Platform
  • Crafting a Compelling LinkedIn Profile
  • Leveraging LinkedIn Features for Engagement
  • Growing Your Network on LinkedIn
  • Increasing Followers and Engagement
  • Enhancing Professional Branding on LinkedIn
  • Leveraging LinkedIn for Career Advancement

UNIT 3: Python for Data Science & AI (42 Hours)

This Python course introduces fundamental to advanced concepts tailored for data science and AI applications. Learn Python step by step from basics to advanced. Learn all libraries, functions, and modules to perform data science projects by analyzing and building ML & AI models using Python.

 

Module 1: Core Python Programming

  • Python Environment
  • Data types & Operators
  • Operators & Loop controls

 

Project: Build a simple calculator

 

Module 2: Advanced Python Programming

  • Functions & Modules
  • Regular Expressions (RegEx)
  • File Handling & Exception Handling
  • Generators & Decorators

 

Class Hands-on:

25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, reg-ex, and exception handling

 

Module 3: Web Scraping using Python

  • Introduction to Web Scraping
  • Web Requests & HTTP
  • Parsing HTML with Beautiful Soup

 

Project: Scrape and Analyze Data from a Website (2-3 Projects)

 

Module 4: OOPs in Python

  • Classes and Objects
  • Encapsulation, Inheritance, and Polymorphism
  • Abstraction and Interfaces
  • Method Overriding and Overloading
  • Class Variables and Instance Variables

 

Module 5: Python For Data Analytics

  • Data Analysis using NumPy (Array Operations)
  • Data Analysis using Pandas (On Dataframes)
  • Data Visualization using Matplotlib
  • Data Visualization using Seaborn

 

Tools Covered: NumPy, Pandas, MatplotLib, Seaborn, Beautiful Soup

 

EDA Project (Create Insights using Data Analytics)

 2 Full-Length Projects on Data Analytics using Pandas, MatplotLib & Seaborn to analyze Data to Gain Insights and Identify Patterns.

UNIT 4: Data Structures & Algorithms in Python (40 Hours)

  • Introduction to Data Structures
  • Arrays & Linked List
  • Stacks & Queues
  • Dictionary & Hashing
  • Trees and Binary Search Trees
  • Traversal Algorithms in Trees
  • Graphs & Graph Representation
  • Traversal algorithms in Graphs
  • Searching & Sorting
  • Greedy Algorithm
  • Pattern Searching
  • Time Complexity Analysis

UNIT 5: Statistics & Machine Learning (60 Hours)

This course provides a comprehensive overview of statistical concepts and machine learning techniques, along with their practical applications. You will learn machine learning algorithms, explore various case studies to understand real-world applications and build models to reinforce your learning.

 

Module 1: Statistics & Probability

  • Fundamentals of Math, Probability & Statistics
  • Descriptive vs inferential statistics
  • Types of data, Sample and Population
  • Descriptive Statistics
  • Handling outliers & missing values in data
  • Discrete and continuous probability distributions
  • Normal distribution and central limit theorem
  • Linear Algebra, Sampling and Estimation
  • Hypothesis Testing Workflow
  • Confusion Matrix, Performance Metrics
  • P-values, Z Scores, Confidence Level
  • Significance Level, Sampling Techniques
  • Parametric Tests: T-test, Z-test, F-test, ANOVA test
  • Non-Parametric Tests: Chi-square test, Man Whitney U Test & Wilcoxon Rank Sum Test
  • Regression & Classification Analysis

 

Class Hands-on:

Problem-solving for central tendency, ANOVA, central limit theorem & hypothesis testing Case study

 

Module 2: Machine Learning

  • Set Theory
  • Data Preprocessing
  • Traditional coding vs Machine learning
  • Supervised and unsupervised learning
  • Model evaluation
  • Exploratory Data Analysis
  • Data Analysis & Visualisation
  • Feature Engineering
  • Machine learning model building & evaluation
    • Linear Regression Model & Evaluation
    • L1 & L2 Regularization (Lasso and Ridge Regression)
    • Logistic Regression Model & Evaluation
    • K Nearest Neighbours (KNN) & Evaluation
    • Decision Tree Classifier & Regressor
    • Random Forest Classifier & Regressor
    • Naive Bayes Classifier
  • Overfitting, bias-variance tradeoff
  • Cross-validation

 

Project:

  • EDA for Weight Prediction task from Height (Regression task)
  • 1 project each for Regression & Classification

 

Module 3: Advanced Machine Learning

  • Clustering & K-means
  • K-Means Clustering Model
  • Ensemble approach
  • Bootstrapping + Aggregation = Bagging
  • Bagging vs Boosting
  • Hyperparameter Tuning for GridSearchCV
  • XGBoost Explanatory Model Building
  • Boosting Ensemble Models
  • Adaptive Boosting (AdaBoost)
  • Handling Imbalanced Dataset
    • Resampling (Oversampling & Undersampling)
    • Oversampling Technique (SMOTE)
  • Gradient Boosting
  • CatBoost
  • LightGBM
  • Support Vector Classifier (SVC) & Support Vector Machines (SVM)
  • Principal Component Analysis (PCA)
    • Use of Dimensionality Reduction Technique
    • Difference with Feature Selection Techniques
  • Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Hyperparameter Tuning

 

Tools Covered: Pandas, Matplotlib, Sk Learn, LightGBM

 

Class Projects:

  • Project with practical application of Regression, Classification, and Clustering algorithms using Machine Learning concepts.
  • Case studies in various domains (e.g., healthcare, finance, marketing, supply chain, etc.) like:
  • Spam Mail Classifier using Naive Bayes Algorithm
  • Detect car Insurance Fraud Claims
  • Heart disease detection using ML

 

Note: All Machine Learning Algorithms will be covered in depth with real-time projects & case studies for each algorithm. Once Machine learning is completed, the Capstone Project will be released for the batch.

UNIT 6: Time-Series Analysis (12 Hours)

The Time Series Analysis course will help you learn how to model, forecast, and analyze time-based data that contains date and time parameters. These techniques are used for predictions in various industries. Eg. Stock Price Prediction,  ECG Anomaly Detection, Earthquake Prediction, Inflation Rate Prediction, Migration Prediction, Rainfall Prediction, Internet Traffic Prediction, Energy Demand Forecasting, etc.

 

Module 1: Time-Series Data Analysis 

  • Introduction to time series data
  • Linear Regression Vs ARIMA model
  • Time series visualization and exploration
  • Time series decomposition
  • Stationarity and its tests
  • Autoregressive (AR) Models
  • Moving Average (MA) Models
  • Autoregressive Integrated Moving Average (ARIMA) Models
  • Seasonal ARIMA (SARIMA) models
  • Exponential smoothing methods

 

Class Projects:

  • Project to predict the number of customers of an Airline organization using Time Series Model ARIMA & SARIMAX
  • Financial Market Stock Price analysis and forecasting
  • Sales data forecasting to understand trend and seasonality

 

Tools Covered: SciKit Learn, Pandas, Matplotlib

UNIT 7: NLP - Natural Language Processing (16 Hours)

The NLP specialization will help you gain experience in techniques such as; text preprocessing, sentiment analysis, and building text-based models. These concepts will further help us build Machine Learning & AI models like Grammarly, ChatGPT, and Alexa.

 

Module 1: NLP

  • Introduction to Natural Language Processing
  • Text Preprocessing
  • Text Embedding Techniques
  • Word2Vec Text Embedding
  • Topic modeling (LDA, LSA)
  • Named Entity Recognition (NER)
  • Part-of-Speech Tagging (POS Tagging)
  • Transformer architecture and BERT model
  • Text classification models

 

Class Projects:

  • To classify an email as spam or not spam
  • Social media sentiment analysis
  • Translation & summarization of News
  • Generate optimized title/headline
  • Case Study on Recommendation Engine

 

Tools Covered: NLTK, Spacy, BERT

UNIT 8: Deep Learning & Reinforcement Learning (24 Hours)

Deep Learning, is a subset of machine learning that focuses on training neural networks to build a model by studying hierarchical patterns and features from the input data. On the other hand, in reinforcement Learning, you will learn to build a sequential model that interacts with the environment to achieve a goal by receiving real-time feedback.

 

Module 1: Deep Learning 

  • Introduction to Deep Learning
  • Forward Propagation in ANN
  • Backpropagation in ANN
  • ReLU vs Leaky ReLU
  • Exploding Gradient Problem
  • Stochastic Gradient Descent (SGD) Optimizer
  • Artificial Neural Network (ANN)
  • L1 & L2 Regularization in ANN
  • Loss Functions for Regression (MSE, RMSE, MAE, Huber Loss)
  • Loss functions for classification (Cross Entropy Loss)
  • Weight Initialisation Techniques
  • Recurrent Neural Network (RNN)
  • Vanishing Gradient Problem in RNN
  • Long Short Term Memory (LSTM) Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Generative Adversarial Networks (GAN)
  • Autoencoders & Variational Autoencoders (VAEs)
  • Optimization Techniques for Deep Learning
  • Hyperparameter Tuning

 

Class Projects

  • Diabetes detection using Artificial Neural Network (ANN)
  • Fake News Classification using LSTM Network
  • Sentiment analysis for social media & customer reviews
  • Stock Price Forecasting using LSTM Neural Network
  • Applications in Information Retrieval & Recommendation Systems
  • Heart Disease Detection project

 

Tools Covered: Tensorflow, Keras, PyTorch

 

Module 2: Reinforcement Learning 

  • Fundamentals of Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Monte Carlo Methods
  • Temporal Difference Learning
  • Q-Learning and SARSA
  • Policy Gradient Methods
  • Multi-Agent & Hierarchical Reinforcement Learning
  • Reinforcement Learning with Deep Learning
  • Deep Q-Networks (DQN)
  • Transfer Learning & Lifelong learning and Fine-tuning

 

Class Projects:

  • Dynamic Pricing Strategies in E-commerce
  • Optimizing Supply Chain Logistics
  • Personalized Healthcare Treatment Planning
  • Reinforcement Learning-Based Autonomous Driving

UNIT 9: Computer Vision (12 Hours)

In this unit, we’ll delve into computer vision for image analysis. We’ll explore image classification, object detection, and segmentation in computer vision using deep-learning architectures like CNNs.

 

Module 1: Computer Vision 

  • Introduction to Computer Vision
  • Convolutional Neural Network (CNN)
  • Difference between CNN and other neural networks
  • Concept of CNN architectures
  • Introduction to OpenCV
  • Image Processing using OpenCV
  • Deep CNN
  • Capturing videoframes
  • Object Tracking using HSV colorspace range
  • Image Thresholding techniques
  • Canny Edge Detection Algorithm & Implementation
  • Hough Line & Circle Transform
  • Image classification & segmentation using OpenCV
  • Identifying Contours using OpenCV
  • Object Detection in OpenCV

 

Class Project:

  • Tomato Leaf Disease Classification using OpenCV Inception V3
  • Objects/Persons Tracking using OpenCV
  • Road Lane Detection using OpenCV
  • Face & Eye detection using OpenCV

 

Tools Used: Tensorflow, Keras, Open CV

UNIT 10: Generative AI & Prompt Engineering (28 Hours)

In this unit, we’ll delve into generative AI and prompt engineering tools. Generative AI will introduce us to large language models, GANs, and autoregressive models for creating new content. At the same time, prompt engineering tools will help us craft effective prompts for guiding AI models, particularly language models like GPT.

 

Module 1: Generative AI and Large Language Models 

  • Introduction to Generative AI
  • Traditional AI vs Generative AI
  • Regular Model Building vs Generation
  • Introduction to Transformer Architecture 
  • Embedding component (Word Embedding & Positional Embedding)
  • BERT (Encoder-Decoder Architecture) vs GPT (Decoder Architecture)
  • Introduction to Generative Pretrained Transformers (GPT) – Text Generation: Word Generation, Sentence Generation
  • ChatGPT (GPT-3.5-Turbo & GPT-4 model)
  • Open Source Large Language Models (LLMs)
  • Huggingface Open LLM Leaderboard
  • LLM Benchmarking datasets
  • Prompts, Contexts, and Structure of Prompts
  • Retrieval Augmented Generation (RAG) Workflow
  • Langchain implementation of RAG
  • Fine-tuning: Concepts of Text Embeddings, Text Similarity 
  • Generation vs Chat Generation
  • Text Generation Model vs Chat Model
  • Reinforcement Learning Human Feedback (RLHF) loop
  • Image Generation: Generative Adversarial Networks (GANs)
  • Auto Encoders & Variational Autoencoders

 

Tools Covered: Open AI, BERT, Huggingface 

 

Class Project:

  • Fake news classification using LSTM
  • Domain-specific (eg: Healthcare) Chatbot using Gen AI
  • Chatbot using Meta/Llama-2 LLM
  • Context-based chatbot using RAG workflow – Indexing a PDF file on Pinecone Vector Database, Implementation using Langchain library

 

Module 2: Prompt Engineering 

  • Exploring prompt tools
  • Understanding prompt tools & their architecture
  • Future advancement in AI and Large Language tools
  • Overview of tools like (GPT, Dall E, Midjourney Etc.)

 

ChatGPT: Prompt for text Generation (Natural Language Processing)

  • Introduction to NLP concept and role in GPT tools
  • ChatGPT and its architecture
  • Hands-on with ChatGPT / Microsoft Copilot prompt for Text Generation
  • Tuning ChatGPT for desired output and application

 

Dall E / Midjourney: Prompt for image Generation

  • Introduction to image generation using prompt
  • Exploring Midjourney / Dall E 2 & 3 / Gencraft prompt for Image generation
  • Tuning prompt for the desired output
  • Ethical consideration for AI-generated images

 

Synthesia for Video Generation & Slides AI for PPT creation

  • Learning prompt with Slides AI (from Google) / Simplified.com for PPT generation
  • Using prompt on Synthesia / Invideo AI for Video Generation

 

Tools Covered: ChatGPT, Midjourney, Dall E, MS Copilot, Synthesia, Invideo AI, Slides AI

UNIT 11: Database Management (40 Hours)

Learn practically data mining, optimizing query performance, and ensuring data integrity on SQL. Advanced topics include NoSQL databases like MongoDB, distributed systems, and data warehousing, preparing students for diverse data roles.

 

Module 1: SQL – Structured Query Language 

  • Introduction to SQL
  • SQL & RDBMS
  • SQL Syantax and data types
  • CRUD operations in SQL
  • Retrieving Data with SQL
  • Filtering, sorting & formatting query results
  • Advanced SQL Queries
  • Database Design and Normalization
  • Advanced Database Concepts
  • Stored Procedures
  • Integrating SQL with Python for Data

 

Hands-on practice:

  • Joins, Sub-queries, Aggregation query
  • Views, Filtering, Sorting
  • Group By and Having clause

 

Module 2: MongoDB 

  • Introduction to MongoDB
  • MongoDB essentials
  • Structure of MongoDB
  • Advanced MongoDB Queries
  • Integrating MongoDB with Python for Data

 

Tools Covered: MySQL, SQL Server, MongoDB

UNIT 12: Data Visualization & Analytics (36 Hours)

This unit consists of two of the most prominently used tools for data visualization & analytics: Power BI and Tableau. You will learn to create interactive dashboards, reports, and visualizations to analyze and communicate insights effectively.

 

Module 1: Power BI 

  • Introduction to Power BI
  • Data Preparation and Modeling
  • Clean, transform & load data in Power BI
  • Data Visualization Techniques
  • Advanced Analytics in Power BI
  • Designing Interactive Dashboards
  • Power Query
  • Design Power BI Reports
  • Connecting Power BI to SQL
  • Create, Share, and Collaborate on Power BI Dashboards

 

Class Project & Assignments:

Project 1: Education Institute’s student data analysis

Project 2: Sales Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

 

Module 2: Tableau

  • Introduction to Tableau
  • Connecting Tableau to data sources
  • Data Types in Tableau
  • Data Preparation and Transformation
  • Building Visualizations in Tableau
  • Advanced Analytics in Tableau
  • Tableau Dashboards and Storytelling
  • Connecting Tableau to SQL
  • Tableau Online to collaborate, share & publish dashboards

 

Class Project & Assignments:

Project 1: Supermarket data analysis

Project 2: Covid Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

– Deployment of Predictive model in Tableau

 

Tools Covered: Power BI, Tableau, Excel

 

Module 3: Excel for Analytics 

  • Introduction to Excel for Analytics
  • Basic Formulas & Function
  • Data Preparation and Cleaning
  • Charts & Graphs in Excel
  • Data Analysis Techniques in Excel
  • PivotTables and PivotCharts for data summarization
  • Data visualization techniques in Excel
  • Excel’s data analysis add-ins

UNIT 13: Big Data Analytics (48 Hours)

In this unit, you will delve into two of the big data analytics tools Spark, Hadoop, and Kafka, the key components of modern data processing ecosystems. You will learn to harness Spark’s distributed computing power, Hadoop’s storage and processing capabilities, and Kafka’s real-time data streaming for scalable data processing & analysis.

 

Module 1: Apache Hadoop 

  • Overview of Big Data and Distributed Computing
  • The Hadoop ecosystem and its components
  • Architecture: HDFS and MapReduce
  • Setting Up Hadoop Environment
  • Managing files and directories in HDFS
  • Performing HDFS operations
  • MapReduce paradigm: mapper, reducer, and shuffle phases
  • Running and monitoring MapReduce jobs on Hadoop clusters
  • YARN and Hadoop Ecosystem
  • Hadoop ecosystem projects: Hive, Pig, HBase, etc.
  • SQOOP (SQL in HADOOP)
  • Integrating Hadoop with other Big Data technologies

 

Module 2: Apache Spark 

  • Overview of Apache Spark and its features
  • Spark architecture: RDDs, DAGs, and transformations/actions
  • Introduction to Spark ecosystem components
  • Setting Up Spark Environment
  • Managing Spark clusters with Apache Mesos or Hadoop YARN
  • Understanding RDDs: creation, transformation, and actions
  • Spark SQL and DataFrames
  • Querying structured data with SQL and DataFrame operations
  • Interoperability between RDDs and DataFrames
  • Spark Streaming for real-time data processing
  • Integrating Spark Streaming with Kafka
  • Spark MLlib: machine learning library for Spark
  • Building and training machine learning models with MLlib
  • Performing analytics tasks with Spark MLlib

 

Module 3: Apache Kafka

  • Overview of Apache Kafka and its Features
  • Kafka architecture: topics, partitions, and replication
  • Ecosystem components: producers, consumers, and brokers
  • Setting Up Kafka Environment
  • Managing Kafka brokers, topics, and partitions
  • Kafka command-line tools and administrative interfaces
  • Producers and Consumers
  • Transformations, aggregations, and windowing operations with Kafka Streams
  • Integrating Kafka Connect with databases, file systems, and other data sources

 

Tools Covered: Spark, Hadoop, Kafka

UNIT 14: Cloud Deployment of ML & AI Models (32 Hours)

In this cloud deployment unit, you will learn to deploy machine learning and AI models using AWS and Azure, two leading cloud platforms. You’ll gain proficiency in deploying, scaling, and managing models in the cloud environments through practical exercises.

 

Module 1: AWS

  • Introduction to Cloud Deployment for ML and AI Models
  • AWS cloud platform and its services for model deployment
  • Understanding deployment architectures and best practices
  • AWS IAM (Identity and Access Management)
  • Elastic Compute Cloud (Amazon EC2)
  • Elastic Block Storage (EBS) and Elastic File System (EFS)
  • Model Deployment with AWS
  • Model Deployment using Python on AWS using Flask
  • Model Deployment using Python on AWS using Django

 

Module 2: Azure

  • Azure cloud platform and its services for model deployment
  • Understanding deployment architectures and best practices
  • Fundamental Principles of Machine Learning on Azure
  • Model Deployment on Azure
  • Model Deployment using Python on Azure using Flask
  • Model Deployment using Python on Azure using Django

 

Tools Covered: AWS, EC2, S3, ECS, Sagemaker, Lambda, Azure, Azure ML, Flask, Django

UNIT 15: MLOps & Machine Learning Pipeline (60 Hours)

This unit includes MLOps, or Machine Learning Operations, the practice of streamlining and automating the lifecycle of machine learning models. You will learn MLOps using any of MLFlow, Kubeflow & TFX to integrate machine learning models into production environments efficiently and reliably.

 

Module 1: MLFlow 

  • Introduction to MLOps and MLflow
  • MLflow: architecture, components, and key features
  • Experiment Tracking with MLflow
  • MLflow Projects
  • Packaging and Deploying Models with MLFlow
  • Scalable Machine Learning Workflows with MLflow and Apache Spark
  • Continuous Integration and Continuous Deployment (CI/CD) with MLflow
  • Monitoring and Model Performance Management
  • Collaboration and Reproducibility

 

Module 2: Kubeflow 

  • Introduction to MLOps and Kubeflow
  • Kubeflow: architecture, components, and key features
  • Setting up the Kubeflow Environment
  • Building Machine Learning Pipelines with Kubeflow Pipelines
  • Training and Experimentation with Kubeflow Katib
  • Model Serving with Kubeflow Serving
  • Model Monitoring and Management with Kubeflow Metadata
  • Continuous Integration and Continuous Deployment (CI/CD) with Kubeflow

 

Module 3: TensorFlow Extended (TFX)

  • Introduction to MLOps and TensorFlow Extended (TFX)
  • TensorFlow Extended (TFX): architecture, components, and key features
  • Setting up the TFX Environment
  • Data Validation and Preprocessing with TFX Data Validation and TFX Transform
  • Model Training with TFX Trainer
  • Model Evaluation with TFX Model Analysis
  • Model Serving with TFX Serving
  • Model Monitoring and Management with TFX Metadata and TFX ML Metadata
  • Continuous Integration and Continuous Deployment (CI/CD) with TFX

 

Tools Covered: MLFlow, Kubeflow, TFX

UNIT 16: Project Management - Agile, Scrum & Jira (20 Hours)

In this unit, students will master the principles and practices of planning, organizing, executing, and controlling projects to achieve specific goals within constraints. Utilizing project management tools such as Asana, Trello, or Jira enhances efficiency in task management, collaboration, and tracking progress.

 

Module 1: Introduction to Project Management

  • Importance of project management
  • Project life cycle and phases
  • Feasibility studies and project selection criteria
  • Project Planning & Execution
  • Performance measurement & Metrics
  • Agile Project Management
  • Project Management Tools
  • Project management templates

 

Module 2: Agile & Scrum

  • Introduction to Agile Methodologies
  • Benefits & Challenges of Agile Implementation
  • Understanding the Agile methodology and principles
  • Scrum Framework Overview
  • Scrum roles, events & artifacts
  • Daily Scrum and Task Management
  • Agile Planning and Estimation
  • Sprint Execution and Delivery
  • Scrum Master Role and Responsibilities
  • Agile Execution and Monitoring
  • Agile Metrics and Reporting
  • Adaptation and Continuous Improvement
  • Agile tools and software (Eg. Jira, Trello, Asana)

 

Module 3: Jira

  • Introduction to Jira
  • Jira projects, issues, and workflows
  • Jira interface and project navigation
  • Creating and Managing Projects
  • Task Management and Collaboration
  • Managing Issues and Workflows
  • Configuring Agile Boards (Scrum & Kanban)
  • Reporting and Dashboards
  • Integrating Jira with other Tools and Systems

 

Tools Covered: Agile. Scrum, Jira, Kanban

Program Highlights

UNIT 1: Orientation (8 Hours)

This unit serves as a primer for data science, introducing key tools and concepts. It’s designed to equip non-programmers with foundational Python skills, facilitating a deeper understanding and practical application throughout the course.

 

Module 1: Introduction To Data Science, Analytics & Artificial Intelligence

  • Introduction to tools, key concepts, and definitions
  • Real-time project applications in different domains
  • Practical applications of data science in various industries

 

Module 2: Fundamentals of Programming

  • Introduction to Python tools
  • Installation of Python
  • Python Fundamentals

 

Tools Covered: Python, Anaconda, Jupyter, Google Colab

 

Module 3: Fundamentals of Statistics

  • Importance and Use of Statistics in Data Science
  • Descriptive Statistics & Predictive Statistics
  • Learn how predictive Statistics connects with Machine Learning

 

Note:

Module 2 and Module 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

UNIT 2: Portfolio Building (6 hours)

This unit provides an extensive roadmap for building a robust portfolio in data science. You’ll master GitHub, a version control system, for efficient collaboration and project management. Additionally, you’ll harness LinkedIn‘s power for networking and career advancement.

 

Module 1: Git & GitHub (VCS)

  • Introduction to Version Control Systems
  • Installing and Configuring Git
  • Git Essentials
  • Branching and Merging
  • GitHub Essentials
  • Collaborating on GitHub
  • Forking repositories
  • Creating pull requests
  • Best Practices and Workflows

 

Class Hands-On: Initiate, collaborate, and work on a real-time project

 

Tools Covered: Git, GitHub

 

Module 2: LinkedIn Profile building

  • Introduction to LinkedIn as a Professional Networking Platform
  • Crafting a Compelling LinkedIn Profile
  • Leveraging LinkedIn Features for Engagement
  • Growing Your Network on LinkedIn
  • Increasing Followers and Engagement
  • Enhancing Professional Branding on LinkedIn
  • Leveraging LinkedIn for Career Advancement

UNIT 3: Python for Data Science & AI (42 Hours)

This Python course introduces fundamental to advanced concepts tailored for data science and AI applications. Learn Python step by step from basics to advanced. Learn all libraries, functions, and modules to perform data science projects by analyzing and building ML & AI models using Python.

 

Module 1: Core Python Programming

  • Python Environment
  • Data types & Operators
  • Operators & Loop controls

 

Project: Build a simple calculator

 

Module 2: Advanced Python Programming

  • Functions & Modules
  • Regular Expressions (RegEx)
  • File Handling & Exception Handling
  • Generators & Decorators

 

Class Hands-on:

25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, reg-ex, and exception handling

 

Module 3: Web Scraping using Python

  • Introduction to Web Scraping
  • Web Requests & HTTP
  • Parsing HTML with Beautiful Soup

 

Project: Scrape and Analyze Data from a Website (2-3 Projects)

 

Module 4: OOPs in Python

  • Classes and Objects
  • Encapsulation, Inheritance, and Polymorphism
  • Abstraction and Interfaces
  • Method Overriding and Overloading
  • Class Variables and Instance Variables

 

Module 5: Python For Data Analytics

  • Data Analysis using NumPy (Array Operations)
  • Data Analysis using Pandas (On Dataframes)
  • Data Visualization using Matplotlib
  • Data Visualization using Seaborn

 

Tools Covered: NumPy, Pandas, MatplotLib, Seaborn, Beautiful Soup

 

EDA Project (Create Insights using Data Analytics)

 2 Full-Length Projects on Data Analytics using Pandas, MatplotLib & Seaborn to analyze Data to Gain Insights and Identify Patterns.

UNIT 4: Data Structures & Algorithms in Python (40 Hours)

  • Introduction to Data Structures
  • Arrays & Linked List
  • Stacks & Queues
  • Dictionary & Hashing
  • Trees and Binary Search Trees
  • Traversal Algorithms in Trees
  • Graphs & Graph Representation
  • Traversal algorithms in Graphs
  • Searching & Sorting
  • Greedy Algorithm
  • Pattern Searching
  • Time Complexity Analysis

UNIT 5: Statistics & Machine Learning (60 Hours)

This course provides a comprehensive overview of statistical concepts and machine learning techniques, along with their practical applications. You will learn machine learning algorithms, explore various case studies to understand real-world applications and build models to reinforce your learning.

 

Module 1: Statistics & Probability

  • Fundamentals of Math, Probability & Statistics
  • Descriptive vs inferential statistics
  • Types of data, Sample and Population
  • Descriptive Statistics
  • Handling outliers & missing values in data
  • Discrete and continuous probability distributions
  • Normal distribution and central limit theorem
  • Linear Algebra, Sampling and Estimation
  • Hypothesis Testing Workflow
  • Confusion Matrix, Performance Metrics
  • P-values, Z Scores, Confidence Level
  • Significance Level, Sampling Techniques
  • Parametric Tests: T-test, Z-test, F-test, ANOVA test
  • Non-Parametric Tests: Chi-square test, Man Whitney U Test & Wilcoxon Rank Sum Test
  • Regression & Classification Analysis

 

Class Hands-on:

Problem-solving for central tendency, ANOVA, central limit theorem & hypothesis testing Case study

 

Module 2: Machine Learning

  • Set Theory
  • Data Preprocessing
  • Traditional coding vs Machine learning
  • Supervised and unsupervised learning
  • Model evaluation
  • Exploratory Data Analysis
  • Data Analysis & Visualisation
  • Feature Engineering
  • Machine learning model building & evaluation
    • Linear Regression Model & Evaluation
    • L1 & L2 Regularization (Lasso and Ridge Regression)
    • Logistic Regression Model & Evaluation
    • K Nearest Neighbours (KNN) & Evaluation
    • Decision Tree Classifier & Regressor
    • Random Forest Classifier & Regressor
    • Naive Bayes Classifier
  • Overfitting, bias-variance tradeoff
  • Cross-validation

 

Project:

  • EDA for Weight Prediction task from Height (Regression task)
  • 1 project each for Regression & Classification

 

Module 3: Advanced Machine Learning

  • Clustering & K-means
  • K-Means Clustering Model
  • Ensemble approach
  • Bootstrapping + Aggregation = Bagging
  • Bagging vs Boosting
  • Hyperparameter Tuning for GridSearchCV
  • XGBoost Explanatory Model Building
  • Boosting Ensemble Models
  • Adaptive Boosting (AdaBoost)
  • Handling Imbalanced Dataset
    • Resampling (Oversampling & Undersampling)
    • Oversampling Technique (SMOTE)
  • Gradient Boosting
  • CatBoost
  • LightGBM
  • Support Vector Classifier (SVC) & Support Vector Machines (SVM)
  • Principal Component Analysis (PCA)
    • Use of Dimensionality Reduction Technique
    • Difference with Feature Selection Techniques
  • Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Hyperparameter Tuning

 

Tools Covered: Pandas, Matplotlib, Sk Learn, LightGBM

 

Class Projects:

  • Project with practical application of Regression, Classification, and Clustering algorithms using Machine Learning concepts.
  • Case studies in various domains (e.g., healthcare, finance, marketing, supply chain, etc.) like:
  • Spam Mail Classifier using Naive Bayes Algorithm
  • Detect car Insurance Fraud Claims
  • Heart disease detection using ML

 

Note: All Machine Learning Algorithms will be covered in depth with real-time projects & case studies for each algorithm. Once Machine learning is completed, the Capstone Project will be released for the batch.

UNIT 6: Time-Series Analysis (12 Hours)

The Time Series Analysis course will help you learn how to model, forecast, and analyze time-based data that contains date and time parameters. These techniques are used for predictions in various industries. Eg. Stock Price Prediction,  ECG Anomaly Detection, Earthquake Prediction, Inflation Rate Prediction, Migration Prediction, Rainfall Prediction, Internet Traffic Prediction, Energy Demand Forecasting, etc.

 

Module 1: Time-Series Data Analysis 

  • Introduction to time series data
  • Linear Regression Vs ARIMA model
  • Time series visualization and exploration
  • Time series decomposition
  • Stationarity and its tests
  • Autoregressive (AR) Models
  • Moving Average (MA) Models
  • Autoregressive Integrated Moving Average (ARIMA) Models
  • Seasonal ARIMA (SARIMA) models
  • Exponential smoothing methods

 

Class Projects:

  • Project to predict the number of customers of an Airline organization using Time Series Model ARIMA & SARIMAX
  • Financial Market Stock Price analysis and forecasting
  • Sales data forecasting to understand trend and seasonality

 

Tools Covered: SciKit Learn, Pandas, Matplotlib

UNIT 7: NLP - Natural Language Processing (16 Hours)

The NLP specialization will help you gain experience in techniques such as; text preprocessing, sentiment analysis, and building text-based models. These concepts will further help us build Machine Learning & AI models like Grammarly, ChatGPT, and Alexa.

 

Module 1: NLP

  • Introduction to Natural Language Processing
  • Text Preprocessing
  • Text Embedding Techniques
  • Word2Vec Text Embedding
  • Topic modeling (LDA, LSA)
  • Named Entity Recognition (NER)
  • Part-of-Speech Tagging (POS Tagging)
  • Transformer architecture and BERT model
  • Text classification models

 

Class Projects:

  • To classify an email as spam or not spam
  • Social media sentiment analysis
  • Translation & summarization of News
  • Generate optimized title/headline
  • Case Study on Recommendation Engine

 

Tools Covered: NLTK, Spacy, BERT

UNIT 8: Deep Learning & Reinforcement Learning (24 Hours)

Deep Learning, is a subset of machine learning that focuses on training neural networks to build a model by studying hierarchical patterns and features from the input data. On the other hand, in reinforcement Learning, you will learn to build a sequential model that interacts with the environment to achieve a goal by receiving real-time feedback.

 

Module 1: Deep Learning 

  • Introduction to Deep Learning
  • Forward Propagation in ANN
  • Backpropagation in ANN
  • ReLU vs Leaky ReLU
  • Exploding Gradient Problem
  • Stochastic Gradient Descent (SGD) Optimizer
  • Artificial Neural Network (ANN)
  • L1 & L2 Regularization in ANN
  • Loss Functions for Regression (MSE, RMSE, MAE, Huber Loss)
  • Loss functions for classification (Cross Entropy Loss)
  • Weight Initialisation Techniques
  • Recurrent Neural Network (RNN)
  • Vanishing Gradient Problem in RNN
  • Long Short Term Memory (LSTM) Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Generative Adversarial Networks (GAN)
  • Autoencoders & Variational Autoencoders (VAEs)
  • Optimization Techniques for Deep Learning
  • Hyperparameter Tuning

 

Class Projects

  • Diabetes detection using Artificial Neural Network (ANN)
  • Fake News Classification using LSTM Network
  • Sentiment analysis for social media & customer reviews
  • Stock Price Forecasting using LSTM Neural Network
  • Applications in Information Retrieval & Recommendation Systems
  • Heart Disease Detection project

 

Tools Covered: Tensorflow, Keras, PyTorch

 

Module 2: Reinforcement Learning 

  • Fundamentals of Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Monte Carlo Methods
  • Temporal Difference Learning
  • Q-Learning and SARSA
  • Policy Gradient Methods
  • Multi-Agent & Hierarchical Reinforcement Learning
  • Reinforcement Learning with Deep Learning
  • Deep Q-Networks (DQN)
  • Transfer Learning & Lifelong learning and Fine-tuning

 

Class Projects:

  • Dynamic Pricing Strategies in E-commerce
  • Optimizing Supply Chain Logistics
  • Personalized Healthcare Treatment Planning
  • Reinforcement Learning-Based Autonomous Driving

UNIT 9: Computer Vision (12 Hours)

In this unit, we’ll delve into computer vision for image analysis. We’ll explore image classification, object detection, and segmentation in computer vision using deep-learning architectures like CNNs.

 

Module 1: Computer Vision 

  • Introduction to Computer Vision
  • Convolutional Neural Network (CNN)
  • Difference between CNN and other neural networks
  • Concept of CNN architectures
  • Introduction to OpenCV
  • Image Processing using OpenCV
  • Deep CNN
  • Capturing videoframes
  • Object Tracking using HSV colorspace range
  • Image Thresholding techniques
  • Canny Edge Detection Algorithm & Implementation
  • Hough Line & Circle Transform
  • Image classification & segmentation using OpenCV
  • Identifying Contours using OpenCV
  • Object Detection in OpenCV

 

Class Project:

  • Tomato Leaf Disease Classification using OpenCV Inception V3
  • Objects/Persons Tracking using OpenCV
  • Road Lane Detection using OpenCV
  • Face & Eye detection using OpenCV

 

Tools Used: Tensorflow, Keras, Open CV

UNIT 10: Generative AI & Prompt Engineering (28 Hours)

In this unit, we’ll delve into generative AI and prompt engineering tools. Generative AI will introduce us to large language models, GANs, and autoregressive models for creating new content. At the same time, prompt engineering tools will help us craft effective prompts for guiding AI models, particularly language models like GPT.

 

Module 1: Generative AI and Large Language Models 

  • Introduction to Generative AI
  • Traditional AI vs Generative AI
  • Regular Model Building vs Generation
  • Introduction to Transformer Architecture 
  • Embedding component (Word Embedding & Positional Embedding)
  • BERT (Encoder-Decoder Architecture) vs GPT (Decoder Architecture)
  • Introduction to Generative Pretrained Transformers (GPT) – Text Generation: Word Generation, Sentence Generation
  • ChatGPT (GPT-3.5-Turbo & GPT-4 model)
  • Open Source Large Language Models (LLMs)
  • Huggingface Open LLM Leaderboard
  • LLM Benchmarking datasets
  • Prompts, Contexts, and Structure of Prompts
  • Retrieval Augmented Generation (RAG) Workflow
  • Langchain implementation of RAG
  • Fine-tuning: Concepts of Text Embeddings, Text Similarity 
  • Generation vs Chat Generation
  • Text Generation Model vs Chat Model
  • Reinforcement Learning Human Feedback (RLHF) loop
  • Image Generation: Generative Adversarial Networks (GANs)
  • Auto Encoders & Variational Autoencoders

 

Tools Covered: Open AI, BERT, Huggingface 

 

Class Project:

  • Fake news classification using LSTM
  • Domain-specific (eg: Healthcare) Chatbot using Gen AI
  • Chatbot using Meta/Llama-2 LLM
  • Context-based chatbot using RAG workflow – Indexing a PDF file on Pinecone Vector Database, Implementation using Langchain library

 

Module 2: Prompt Engineering 

  • Exploring prompt tools
  • Understanding prompt tools & their architecture
  • Future advancement in AI and Large Language tools
  • Overview of tools like (GPT, Dall E, Midjourney Etc.)

 

ChatGPT: Prompt for text Generation (Natural Language Processing)

  • Introduction to NLP concept and role in GPT tools
  • ChatGPT and its architecture
  • Hands-on with ChatGPT / Microsoft Copilot prompt for Text Generation
  • Tuning ChatGPT for desired output and application

 

Dall E / Midjourney: Prompt for image Generation

  • Introduction to image generation using prompt
  • Exploring Midjourney / Dall E 2 & 3 / Gencraft prompt for Image generation
  • Tuning prompt for the desired output
  • Ethical consideration for AI-generated images

 

Synthesia for Video Generation & Slides AI for PPT creation

  • Learning prompt with Slides AI (from Google) / Simplified.com for PPT generation
  • Using prompt on Synthesia / Invideo AI for Video Generation

 

Tools Covered: ChatGPT, Midjourney, Dall E, MS Copilot, Synthesia, Invideo AI, Slides AI

UNIT 11: Database Management (40 Hours)

Learn practically data mining, optimizing query performance, and ensuring data integrity on SQL. Advanced topics include NoSQL databases like MongoDB, distributed systems, and data warehousing, preparing students for diverse data roles.

 

Module 1: SQL – Structured Query Language 

  • Introduction to SQL
  • SQL & RDBMS
  • SQL Syantax and data types
  • CRUD operations in SQL
  • Retrieving Data with SQL
  • Filtering, sorting & formatting query results
  • Advanced SQL Queries
  • Database Design and Normalization
  • Advanced Database Concepts
  • Stored Procedures
  • Integrating SQL with Python for Data

 

Hands-on practice:

  • Joins, Sub-queries, Aggregation query
  • Views, Filtering, Sorting
  • Group By and Having clause

 

Module 2: MongoDB 

  • Introduction to MongoDB
  • MongoDB essentials
  • Structure of MongoDB
  • Advanced MongoDB Queries
  • Integrating MongoDB with Python for Data

 

Tools Covered: MySQL, SQL Server, MongoDB

UNIT 12: Data Visualization & Analytics (36 Hours)

This unit consists of two of the most prominently used tools for data visualization & analytics: Power BI and Tableau. You will learn to create interactive dashboards, reports, and visualizations to analyze and communicate insights effectively.

 

Module 1: Power BI 

  • Introduction to Power BI
  • Data Preparation and Modeling
  • Clean, transform & load data in Power BI
  • Data Visualization Techniques
  • Advanced Analytics in Power BI
  • Designing Interactive Dashboards
  • Power Query
  • Design Power BI Reports
  • Connecting Power BI to SQL
  • Create, Share, and Collaborate on Power BI Dashboards

 

Class Project & Assignments:

Project 1: Education Institute’s student data analysis

Project 2: Sales Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

 

Module 2: Tableau

  • Introduction to Tableau
  • Connecting Tableau to data sources
  • Data Types in Tableau
  • Data Preparation and Transformation
  • Building Visualizations in Tableau
  • Advanced Analytics in Tableau
  • Tableau Dashboards and Storytelling
  • Connecting Tableau to SQL
  • Tableau Online to collaborate, share & publish dashboards

 

Class Project & Assignments:

Project 1: Supermarket data analysis

Project 2: Covid Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

– Deployment of Predictive model in Tableau

 

Tools Covered: Power BI, Tableau, Excel

 

Module 3: Excel for Analytics 

  • Introduction to Excel for Analytics
  • Basic Formulas & Function
  • Data Preparation and Cleaning
  • Charts & Graphs in Excel
  • Data Analysis Techniques in Excel
  • PivotTables and PivotCharts for data summarization
  • Data visualization techniques in Excel
  • Excel’s data analysis add-ins

UNIT 13: Big Data Analytics (48 Hours)

In this unit, you will delve into two of the big data analytics tools Spark, Hadoop, and Kafka, the key components of modern data processing ecosystems. You will learn to harness Spark’s distributed computing power, Hadoop’s storage and processing capabilities, and Kafka’s real-time data streaming for scalable data processing & analysis.

 

Module 1: Apache Hadoop 

  • Overview of Big Data and Distributed Computing
  • The Hadoop ecosystem and its components
  • Architecture: HDFS and MapReduce
  • Setting Up Hadoop Environment
  • Managing files and directories in HDFS
  • Performing HDFS operations
  • MapReduce paradigm: mapper, reducer, and shuffle phases
  • Running and monitoring MapReduce jobs on Hadoop clusters
  • YARN and Hadoop Ecosystem
  • Hadoop ecosystem projects: Hive, Pig, HBase, etc.
  • SQOOP (SQL in HADOOP)
  • Integrating Hadoop with other Big Data technologies

 

Module 2: Apache Spark 

  • Overview of Apache Spark and its features
  • Spark architecture: RDDs, DAGs, and transformations/actions
  • Introduction to Spark ecosystem components
  • Setting Up Spark Environment
  • Managing Spark clusters with Apache Mesos or Hadoop YARN
  • Understanding RDDs: creation, transformation, and actions
  • Spark SQL and DataFrames
  • Querying structured data with SQL and DataFrame operations
  • Interoperability between RDDs and DataFrames
  • Spark Streaming for real-time data processing
  • Integrating Spark Streaming with Kafka
  • Spark MLlib: machine learning library for Spark
  • Building and training machine learning models with MLlib
  • Performing analytics tasks with Spark MLlib

 

Module 3: Apache Kafka

  • Overview of Apache Kafka and its Features
  • Kafka architecture: topics, partitions, and replication
  • Ecosystem components: producers, consumers, and brokers
  • Setting Up Kafka Environment
  • Managing Kafka brokers, topics, and partitions
  • Kafka command-line tools and administrative interfaces
  • Producers and Consumers
  • Transformations, aggregations, and windowing operations with Kafka Streams
  • Integrating Kafka Connect with databases, file systems, and other data sources

 

Tools Covered: Spark, Hadoop, Kafka

UNIT 14: Cloud Deployment of ML & AI Models (32 Hours)

In this cloud deployment unit, you will learn to deploy machine learning and AI models using AWS and Azure, two leading cloud platforms. You’ll gain proficiency in deploying, scaling, and managing models in the cloud environments through practical exercises.

 

Module 1: AWS

  • Introduction to Cloud Deployment for ML and AI Models
  • AWS cloud platform and its services for model deployment
  • Understanding deployment architectures and best practices
  • AWS IAM (Identity and Access Management)
  • Elastic Compute Cloud (Amazon EC2)
  • Elastic Block Storage (EBS) and Elastic File System (EFS)
  • Model Deployment with AWS
  • Model Deployment using Python on AWS using Flask
  • Model Deployment using Python on AWS using Django

 

Module 2: Azure

  • Azure cloud platform and its services for model deployment
  • Understanding deployment architectures and best practices
  • Fundamental Principles of Machine Learning on Azure
  • Model Deployment on Azure
  • Model Deployment using Python on Azure using Flask
  • Model Deployment using Python on Azure using Django

 

Tools Covered: AWS, EC2, S3, ECS, Sagemaker, Lambda, Azure, Azure ML, Flask, Django

UNIT 15: MLOps & Machine Learning Pipeline (60 Hours)

This unit includes MLOps, or Machine Learning Operations, the practice of streamlining and automating the lifecycle of machine learning models. You will learn MLOps using any of MLFlow, Kubeflow & TFX to integrate machine learning models into production environments efficiently and reliably.

 

Module 1: MLFlow 

  • Introduction to MLOps and MLflow
  • MLflow: architecture, components, and key features
  • Experiment Tracking with MLflow
  • MLflow Projects
  • Packaging and Deploying Models with MLFlow
  • Scalable Machine Learning Workflows with MLflow and Apache Spark
  • Continuous Integration and Continuous Deployment (CI/CD) with MLflow
  • Monitoring and Model Performance Management
  • Collaboration and Reproducibility

 

Module 2: Kubeflow 

  • Introduction to MLOps and Kubeflow
  • Kubeflow: architecture, components, and key features
  • Setting up the Kubeflow Environment
  • Building Machine Learning Pipelines with Kubeflow Pipelines
  • Training and Experimentation with Kubeflow Katib
  • Model Serving with Kubeflow Serving
  • Model Monitoring and Management with Kubeflow Metadata
  • Continuous Integration and Continuous Deployment (CI/CD) with Kubeflow

 

Module 3: TensorFlow Extended (TFX)

  • Introduction to MLOps and TensorFlow Extended (TFX)
  • TensorFlow Extended (TFX): architecture, components, and key features
  • Setting up the TFX Environment
  • Data Validation and Preprocessing with TFX Data Validation and TFX Transform
  • Model Training with TFX Trainer
  • Model Evaluation with TFX Model Analysis
  • Model Serving with TFX Serving
  • Model Monitoring and Management with TFX Metadata and TFX ML Metadata
  • Continuous Integration and Continuous Deployment (CI/CD) with TFX

 

Tools Covered: MLFlow, Kubeflow, TFX

UNIT 16: Project Management - Agile, Scrum & Jira (20 Hours)

In this unit, students will master the principles and practices of planning, organizing, executing, and controlling projects to achieve specific goals within constraints. Utilizing project management tools such as Asana, Trello, or Jira enhances efficiency in task management, collaboration, and tracking progress.

 

Module 1: Introduction to Project Management

  • Importance of project management
  • Project life cycle and phases
  • Feasibility studies and project selection criteria
  • Project Planning & Execution
  • Performance measurement & Metrics
  • Agile Project Management
  • Project Management Tools
  • Project management templates

 

Module 2: Agile & Scrum

  • Introduction to Agile Methodologies
  • Benefits & Challenges of Agile Implementation
  • Understanding the Agile methodology and principles
  • Scrum Framework Overview
  • Scrum roles, events & artifacts
  • Daily Scrum and Task Management
  • Agile Planning and Estimation
  • Sprint Execution and Delivery
  • Scrum Master Role and Responsibilities
  • Agile Execution and Monitoring
  • Agile Metrics and Reporting
  • Adaptation and Continuous Improvement
  • Agile tools and software (Eg. Jira, Trello, Asana)

 

Module 3: Jira

  • Introduction to Jira
  • Jira projects, issues, and workflows
  • Jira interface and project navigation
  • Creating and Managing Projects
  • Task Management and Collaboration
  • Managing Issues and Workflows
  • Configuring Agile Boards (Scrum & Kanban)
  • Reporting and Dashboards
  • Integrating Jira with other Tools and Systems

 

Tools Covered: Agile. Scrum, Jira, Kanban

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      What are the prerequisites for the Advanced Data Science and AI Course?

      The Advanced Certification in Data Science and AI Course is designed to be beginner-friendly, starting from the basics. While prior programming knowledge isn’t mandatory, a passion for learning and a foundational grasp of Applied Mathematics or Statistics will enhance your experience. Ideal candidates have technical exposure or hold degrees like B.E., B.Tech, BCA, MCA, or M.Tech. Get ready to dive into cutting-edge technologies and transform your career with our comprehensive, industry-focused training!

      What will I learn in the Advanced Data Science and AI Course?

      Our Advanced Certification in Data Science and AI Program is designed to equip you with a robust skill set across key areas in data science and AI. You’ll master:

      • Python Programming & Web Scraping
      • Advanced Statistics & Machine Learning
      • Natural Language Processing (NLP)
      • Deep Learning, Reinforcement Learning & Computer Vision
      • SQL, MongoDB & Data Visualization with Power BI & Tableau
      • Big Data Technologies: Hadoop, Spark & Kafka
      • Cloud Deployment on AWS & Azure
      • Data Pipeline & MLOps
      • Project Management with Agile & Scrum

      For a detailed breakdown of the curriculum, check out our syllabus!

      Can I enroll in the course if I have a non-technical background with no programming knowledge?

      Definitely, individuals with a non-technical background can enroll in the Advanced Data Science and AI Course. While some basic technical knowledge and familiarity with programming concepts would be beneficial, the course is designed to accommodate learners from diverse backgrounds and equip them with the necessary skills to succeed in the field of data science and artificial intelligence.

      How can I learn Data Science if I'm not eligible for this program?

      If you lack eligibility for Advanced Data Science and AI course due to a lack of data exposure or technical know-how and still wish to learn Data Science, we offer a course in Data Science & Machine Learning for Non-Programmers that can help you achieve your goal of learning Data Science and pursuing a career in the field.

      How many students are there in one batch?

      If you lack eligibility for Advanced Data Science and AI course due to a lack of data exposure or technical know-how and still wish to learn Data Science, we offer a course in Data Science & Machine Learning for Non-Programmers that can help you achieve your goal of learning Data Science and pursuing a career in the field.

      What are the benefits of a 3 year subscription to the program?

      Students enrolled in the Advanced Certification in Data Science and AI Program enjoy a 3-year subscription, providing access to live class support, mentorship from industry experts, and job referrals. This extended access ensures continuous learning and support, helping students stay updated with industry trends and enhancing their career prospects.

      What is the duration of Advanced Data Science and AI Course ?

      The Advanced Data Science and AI Course spans approximately 14 months (480 hours). This includes live training sessions, hands-on projects, and interview preparation. The program offers flexibility with weekday and weekend batches:

      • Weekday Batch: 12 months, Monday to Friday, 2 hours/day
      • Weekend Batch: 14 months, Saturday and Sunday, 3 to 4 hours/day

      How does smaller batch size help in better learning?

      Smaller batch sizes create a more personalised and interactive learning environment. They allow for:

      • Focused Attention: Students receive individual attention and can resolve queries more effectively.
      • Customised Pace: Trainers can adjust the course pace based on the needs of the group.
      • Enhanced Engagement: Increased interaction leads to a more engaging and supportive learning experience.

      Will I learn cloud deployment and big data technologies in this course?

      Yes, the Advanced Certification in Data Science and AI Program includes comprehensive coverage of cloud deployment technologies, such as AWS and Azure, as well as big data technologies like Hadoop, Spark and Kafka. This ensures you acquire a robust skill set in managing large-scale data and cloud-based environments, aligning with current industry standards.

      How will this course help me in my career?

      Yes, the Advanced Certification in Data Science and AI Program includes comprehensive coverage of cloud deployment technologies, such as AWS and Azure, as well as big data technologies like Hadoop, Spark and Kafka. This ensures you acquire a robust skill set in managing large-scale data and cloud-based environments, aligning with current industry standards.

      Are there any assessments or exams during the course?

      Yes, the Advanced Data Science and  AI Course includes periodic assessments and exams to gauge your understanding and progress. These evaluations are integrated into the course to ensure you master the concepts, provide feedback for improvement, and prepare you effectively for real-world applications.

      Can I switch from the weekday batch to the weekend batch or vice versa?

      Yes, we understand that scheduling conflicts can occur. If you need to switch between batches, our support team is here to help. Just contact us, and we’ll assist you in making the necessary batch transfer arrangements, depending on seat availability in the desired batch.

      Is there any practical training involved in the course?

      Absolutely! The Advanced Data Science and AI Program places a strong emphasis on practical training. You will work on real-world projects, gaining hands-on experience in applying data science and AI techniques to solve complex problems. This practical exposure will enhance your skills and build your confidence in handling industry-relevant projects.

      What are real-time projects and how do they help?

      Real-time projects in the Advanced Certification in Data Science and AI Program are based on actual industry data, with sensitive information anonymized for confidentiality. These projects allow students to apply concepts and algorithms to real datasets, gaining practical experience. The course includes 24 real-time industry projects across various scenarios, offering hands-on practice with the tools and techniques learned, and preparing students for real-world challenges.

      What are domain specialisations and why are they important?

      Domain specializations provide industry-specific training through capstone projects and mentorship tailored to particular fields. These projects, sourced from various industries, allow students to apply their skills in real-world contexts. Mentors guide students in understanding the projects within the relevant industry framework. This targeted training enhances students’ knowledge, making them more proficient and increasing their chances of clearing job interviews.

      How many Capstone projects are part of this program?

      The program includes up to 4 end-to-end Capstone projects. These projects enable students to apply their knowledge and gain practical experience by working on real-world scenarios, providing invaluable hands-on learning opportunities.

      Can a student choose his mentor for the program?

      At 1stepGrow, we assign specialised mentors for each subject to ensure that all queries are addressed effectively. If a batch is not satisfied with a mentor’s training method, students can raise their concerns on the student forum. The management will then address the issue and assign a different mentor to the batch if needed.

      Can I get access to industry experts or mentors during the course?

      At 1stepGrow, we assign specialised mentors for each subject to ensure that all queries are addressed effectively. If a batch is not satisfied with a mentor’s training method, students can raise their concerns on the student forum. The management will then address the issue and assign a different mentor to the batch if needed.

      How do I resolve my queries outside the class ?

      To resolve queries outside of class, you can use our dedicated student forum. This platform allows you to connect with both trainers and fellow students, where you can post your questions and get answers or assistance, ensuring continuous support and collaboration.

      How are the doubt solving sessions conducted?

      Doubt-solving sessions are integrated into the Advanced Data Science and AI Course, with dedicated time at the end of each module. These sessions ensure that you can address and clarify any questions or concerns, promoting a thorough understanding of the material.

      What does the Job Assistance program in the Advanced Data Science and AI Course offer?

      Doubt-solving sessions are integrated into the Advanced Data Science and AI Course, with dedicated time at the end of each module. These sessions ensure that you can address and clarify any questions or concerns, promoting a thorough understanding of the material.

      Do you provide job assistance after completing the course?

      Yes! Upon completing the Advanced Certification in Data Science and AI Program, you will receive extensive job assistance. Our dedicated placement team supports you with resume building, interview preparation, and connects you with job opportunities at top organisations. We are committed to aiding your career transition and helping you land a fulfilling role in the industry.

      How many job referrals will be provided?

      Yes! Upon completing the Advanced Certification in Data Science and AI Program, you will receive extensive job assistance. Our dedicated placement team supports you with resume building, interview preparation, and connects you with job opportunities at top organisations. We are committed to aiding your career transition and helping you land a fulfilling role in the industry.

      What’s the eligibility for a job assistance program at 1stepGrow?

      To qualify for job assistance from 1stepGrow, you must meet these criteria:

      • Achieve a score of 70% or higher on all assessment tests.
      • Complete and submit all assignments on time.
      • Submit real-time project work.
      • Successfully complete and submit at least 2 Capstone projects.

      What Is the Fee for the Advanced Data Science and AI Course?

      The total fee for the Advanced Data Science and AI Course is INR 1,09,000/- plus 18% GST.

      Is there a loan option available for the Advanced Data Science and AI Course?

      Yes, we offer flexible payment options for the Advanced Data Science and AI Course. You can choose to pay the fees in installments through a no-cost EMI (Interest free loan option) plan through our banking partner, with payments of approx. INR 10,719 per month for a 12-month period.To avail of this, you will need to submit your or applicant’s Aadhar, PAN, 3-month salary slip, and other necessary documents.

      What are the different modes of payments available?

      We offer multiple payment methods for your convenience:

      • Unified Payments Interface (UPI)
      • Net Banking
      • Bank Transfer
      • Debit Card
      • Credit Card
      • Visa

      Zero-cost EMI

      Are there any installment options available for course fee payment?

      Yes, we offer flexible installment options to ease the financial commitment. You can choose from various payment plans, including our no-cost EMI option. Contact our admissions team to explore the best payment plan for your needs.

      Is there any scholarship/discount available?

      Yes, 1stepGrow offers a 15% discount for immediate registrations to upto 25% scholarship on test scores. Our counsellors will provide details about any current discounts or scholarships available for the course.

      What is Group Discount?

      Group discounts are designed to make our program more accessible for teams or friends joining together. Here’s how it works:

      • Group of 2: 5% extra discount
      • Group of 3 or more: 10% extra discount

      This helps make learning more affordable when you bring others along.

      Advanced Data Science & AI Course Book Counselling

      Have any questions in mind?

      Talk to our team directly

      Reach out to us and your career guide will get
      in touch with you shortly

      Learn More About Your Learning Options

      All Answers To Your Future Career

      What are the prerequisites for the Advanced Data Science and AI Course?

      The Advanced Certification in Data Science and AI Course is designed to be beginner-friendly, starting from the basics. While prior programming knowledge isn’t mandatory, a passion for learning and a foundational grasp of Applied Mathematics or Statistics will enhance your experience. Ideal candidates have technical exposure or hold degrees like B.E., B.Tech, BCA, MCA, or M.Tech. Get ready to dive into cutting-edge technologies and transform your career with our comprehensive, industry-focused training!

      What will I learn in the Advanced Data Science and AI Course?

      Our Advanced Certification in Data Science and AI Program is designed to equip you with a robust skill set across key areas in data science and AI. You’ll master:

      • Python Programming & Web Scraping
      • Advanced Statistics & Machine Learning
      • Natural Language Processing (NLP)
      • Deep Learning, Reinforcement Learning & Computer Vision
      • SQL, MongoDB & Data Visualization with Power BI & Tableau
      • Big Data Technologies: Hadoop, Spark & Kafka
      • Cloud Deployment on AWS & Azure
      • Data Pipeline & MLOps
      • Project Management with Agile & Scrum

      For a detailed breakdown of the curriculum, check out our syllabus!

      Can I enroll in the course if I have a non-technical background with no programming knowledge?

      Definitely, individuals with a non-technical background can enroll in the Advanced Data Science and AI Course. While some basic technical knowledge and familiarity with programming concepts would be beneficial, the course is designed to accommodate learners from diverse backgrounds and equip them with the necessary skills to succeed in the field of data science and artificial intelligence.

      How can I learn Data Science if I'm not eligible for this program?

      If you lack eligibility for Advanced Data Science and AI course due to a lack of data exposure or technical know-how and still wish to learn Data Science, we offer a course in Data Science & Machine Learning for Non-Programmers that can help you achieve your goal of learning Data Science and pursuing a career in the field.

      How many students are there in one batch?

      If you lack eligibility for Advanced Data Science and AI course due to a lack of data exposure or technical know-how and still wish to learn Data Science, we offer a course in Data Science & Machine Learning for Non-Programmers that can help you achieve your goal of learning Data Science and pursuing a career in the field.

      What are the benefits of a 3 year subscription to the program?

      Students enrolled in the Advanced Certification in Data Science and AI Program enjoy a 3-year subscription, providing access to live class support, mentorship from industry experts, and job referrals. This extended access ensures continuous learning and support, helping students stay updated with industry trends and enhancing their career prospects.

      What is the duration of Advanced Data Science and AI Course ?

      The Advanced Data Science and AI Course spans approximately 14 months (480 hours). This includes live training sessions, hands-on projects, and interview preparation. The program offers flexibility with weekday and weekend batches:

      • Weekday Batch: 12 months, Monday to Friday, 2 hours/day
      • Weekend Batch: 14 months, Saturday and Sunday, 3 to 4 hours/day

      How does smaller batch size help in better learning?

      Smaller batch sizes create a more personalised and interactive learning environment. They allow for:

      • Focused Attention: Students receive individual attention and can resolve queries more effectively.
      • Customised Pace: Trainers can adjust the course pace based on the needs of the group.
      • Enhanced Engagement: Increased interaction leads to a more engaging and supportive learning experience.

      Will I learn cloud deployment and big data technologies in this course?

      Yes, the Advanced Certification in Data Science and AI Program includes comprehensive coverage of cloud deployment technologies, such as AWS and Azure, as well as big data technologies like Hadoop, Spark and Kafka. This ensures you acquire a robust skill set in managing large-scale data and cloud-based environments, aligning with current industry standards.

      How will this course help me in my career?

      Yes, the Advanced Certification in Data Science and AI Program includes comprehensive coverage of cloud deployment technologies, such as AWS and Azure, as well as big data technologies like Hadoop, Spark and Kafka. This ensures you acquire a robust skill set in managing large-scale data and cloud-based environments, aligning with current industry standards.

      Are there any assessments or exams during the course?

      Yes, the Advanced Data Science and  AI Course includes periodic assessments and exams to gauge your understanding and progress. These evaluations are integrated into the course to ensure you master the concepts, provide feedback for improvement, and prepare you effectively for real-world applications.

      Can I switch from the weekday batch to the weekend batch or vice versa?

      Yes, we understand that scheduling conflicts can occur. If you need to switch between batches, our support team is here to help. Just contact us, and we’ll assist you in making the necessary batch transfer arrangements, depending on seat availability in the desired batch.

      Is there any practical training involved in the course?

      Absolutely! The Advanced Data Science and AI Program places a strong emphasis on practical training. You will work on real-world projects, gaining hands-on experience in applying data science and AI techniques to solve complex problems. This practical exposure will enhance your skills and build your confidence in handling industry-relevant projects.

      What are real-time projects and how do they help?

      Real-time projects in the Advanced Certification in Data Science and AI Program are based on actual industry data, with sensitive information anonymized for confidentiality. These projects allow students to apply concepts and algorithms to real datasets, gaining practical experience. The course includes 24 real-time industry projects across various scenarios, offering hands-on practice with the tools and techniques learned, and preparing students for real-world challenges.

      What are domain specialisations and why are they important?

      Domain specializations provide industry-specific training through capstone projects and mentorship tailored to particular fields. These projects, sourced from various industries, allow students to apply their skills in real-world contexts. Mentors guide students in understanding the projects within the relevant industry framework. This targeted training enhances students’ knowledge, making them more proficient and increasing their chances of clearing job interviews.

      How many Capstone projects are part of this program?

      The program includes up to 4 end-to-end Capstone projects. These projects enable students to apply their knowledge and gain practical experience by working on real-world scenarios, providing invaluable hands-on learning opportunities.

      Can a student choose his mentor for the program?

      At 1stepGrow, we assign specialised mentors for each subject to ensure that all queries are addressed effectively. If a batch is not satisfied with a mentor’s training method, students can raise their concerns on the student forum. The management will then address the issue and assign a different mentor to the batch if needed.

      Can I get access to industry experts or mentors during the course?

      At 1stepGrow, we assign specialised mentors for each subject to ensure that all queries are addressed effectively. If a batch is not satisfied with a mentor’s training method, students can raise their concerns on the student forum. The management will then address the issue and assign a different mentor to the batch if needed.

      How do I resolve my queries outside the class ?

      To resolve queries outside of class, you can use our dedicated student forum. This platform allows you to connect with both trainers and fellow students, where you can post your questions and get answers or assistance, ensuring continuous support and collaboration.

      How are the doubt solving sessions conducted?

      Doubt-solving sessions are integrated into the Advanced Data Science and AI Course, with dedicated time at the end of each module. These sessions ensure that you can address and clarify any questions or concerns, promoting a thorough understanding of the material.

      What does the Job Assistance program in the Advanced Data Science and AI Course offer?

      Doubt-solving sessions are integrated into the Advanced Data Science and AI Course, with dedicated time at the end of each module. These sessions ensure that you can address and clarify any questions or concerns, promoting a thorough understanding of the material.

      Do you provide job assistance after completing the course?

      Yes! Upon completing the Advanced Certification in Data Science and AI Program, you will receive extensive job assistance. Our dedicated placement team supports you with resume building, interview preparation, and connects you with job opportunities at top organisations. We are committed to aiding your career transition and helping you land a fulfilling role in the industry.

      How many job referrals will be provided?

      Yes! Upon completing the Advanced Certification in Data Science and AI Program, you will receive extensive job assistance. Our dedicated placement team supports you with resume building, interview preparation, and connects you with job opportunities at top organisations. We are committed to aiding your career transition and helping you land a fulfilling role in the industry.

      What’s the eligibility for a job assistance program at 1stepGrow?

      To qualify for job assistance from 1stepGrow, you must meet these criteria:

      • Achieve a score of 70% or higher on all assessment tests.
      • Complete and submit all assignments on time.
      • Submit real-time project work.
      • Successfully complete and submit at least 2 Capstone projects.

      What Is the Fee for the Advanced Data Science and AI Course?

      The total fee for the Advanced Data Science and AI Course is INR 1,09,000/- plus 18% GST.

      Is there a loan option available for the Advanced Data Science and AI Course?

      Yes, we offer flexible payment options for the Advanced Data Science and AI Course. You can choose to pay the fees in installments through a no-cost EMI (Interest free loan option) plan through our banking partner, with payments of approx. INR 10,719 per month for a 12-month period.To avail of this, you will need to submit your or applicant’s Aadhar, PAN, 3-month salary slip, and other necessary documents.

      What are the different modes of payments available?

      We offer multiple payment methods for your convenience:

      • Unified Payments Interface (UPI)
      • Net Banking
      • Bank Transfer
      • Debit Card
      • Credit Card
      • Visa

      Zero-cost EMI

      Are there any installment options available for course fee payment?

      Yes, we offer flexible installment options to ease the financial commitment. You can choose from various payment plans, including our no-cost EMI option. Contact our admissions team to explore the best payment plan for your needs.

      Is there any scholarship/discount available?

      Yes, 1stepGrow offers a 15% discount for immediate registrations to upto 25% scholarship on test scores. Our counsellors will provide details about any current discounts or scholarships available for the course.

      What is Group Discount?

      Group discounts are designed to make our program more accessible for teams or friends joining together. Here’s how it works:

      • Group of 2: 5% extra discount
      • Group of 3 or more: 10% extra discount

      This helps make learning more affordable when you bring others along.

      Have any questions in mind?

      Talk to our team directly

      Reach out to us and your career guide will get
      in touch with you shortly