AI

Data Science with Gen AI

This is an advanced 6-month course that takes you from basic to advanced topics in Data Science, Machine Learning, and Generative AI, with practical, hands-on projects and real-world use cases.

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Data Science with Gen AI
Duration6 months
Lessons200
Levelintermediate
ModeLive Online

What you will learn

  • Data Science Foundations
  • Machine Learning Basics
  • Deep Learning
  • Generative AI Concepts
  • OpenAI API Integration
  • Building AI Models

Corporate training outcomes

  • Completion certificate
  • Practical assignments and project work
  • Mentor support and progress tracking
  • Custom batch options for teams
  • Yes

Requirements

  • Laptop with good internet
  • Passionate about learning
  • No prior experience needed
  • Willing to dedicate time
  • Curious about emerging AI technologies

Curriculum

Python Programming Fundamentals
  • Introduction to the Course and Dashboard Overview
  • Data Industry Overview and Its Significance
  • Lab Introduction for Practical Learning
  • Overview of the Support System for Assistance
  • Community and Networking Introduction
  • Understanding Python and Comparing it with Other Languages
  • Exploring Python Objects: Numbers, Booleans, and Strings
  • Container Objects in Python: Lists, Tuples, and Dictionaries
  • The Mutability of Objects in Python
  • Understanding Operators: Arithmetic, Bitwise, Comparison, and Assignment Operators
  • Operator Precedence and Associativity in Python
  • Conditional Statements (If, Else, Elif)
  • Loops: Understanding While and For Loops
  • Break and Continue Statements, Using the Range Function
  • Basic Data Structures in Python: Lists, Tuples, Sets, Dictionaries
  • String Handling: Basic Operations and Methods
  • Splitting and Joining Strings
  • String Formatting Functions and String Interpolation
  • List Methods and List Operations
  • Working with Lists as Stacks and Queues
  • List Comprehensions in Python
  • Tuples, Sets, and Dictionary Methods
  • Dictionary Comprehensions and Their Applications
  • Understanding Dictionary View Objects
  • Functions in Python: Basics and Parameter Passing
  • Iterators in Python: How They Work
  • Working with Generator Functions
  • Understanding Lambda Functions and Their Uses
  • Applying Map, Reduce, and Filter Functions
  • OOPs Concepts: Introduction to Object-Oriented Programming
  • Class Creation and Understanding Classes in Python
  • Pillars of OOPs: Inheritance, Polymorphism, Encapsulation, and Abstraction
  • Decorators: How They Work and Why We Use Them
  • Class Methods, Static Methods, and Their Use Cases
  • Special (Magic/Dunder) Methods in Python
  • Property Decorators: Getters, Setters, and Delete Methods
  • Working with Files: Reading and Writing Data
  • Buffered Reading and Writing in Files
  • Exploring Other File Methods
  • Logging and Debugging: Tools for Error Handling
  • Modules and Import Statements in Python
  • Exception Handling with Try-Except
  • Creating Custom Exception Classes
  • Common Exceptions in Python
  • Best Practices for Exception Handling
  • Introduction to Multithreading and Its Benefits
  • Understanding Multiprocessing in Python
Python Projects
  • Practical Python Projects Overview: Up to 5 Projects Covered
Data Analysis with Pandas
  • Introduction to Pandas Series
  • Introduction to Pandas DataFrame
  • Understanding Pandas Panel Data
  • Basic Functionalities of Pandas
  • Reading Data from Various File Formats
  • Reindexing Data in Pandas
  • Iteration in Pandas for Efficient Data Manipulation
  • Sorting Data in Pandas
  • Text Data Operations in Pandas and Customization
  • Indexing and Selecting Data in Pandas
  • Statistical Functions in Pandas
  • Applying Window Functions in Pandas
  • Working with Date and Time Data in Pandas
  • Time Delta Operations in Pandas
  • Handling Categorical Data in Pandas
  • Data Visualization with Pandas
  • Utilizing Pandas Tools for Data Analysis
Numerical Data Handling with Numpy
  • Introduction to Numpy Ndarray Objects
  • Numpy Data Types and Their Importance
  • Array Attributes in Numpy
  • Numpy Array Creation Methods
  • Creating Arrays from Existing Data in Numpy
  • Working with Ranges and Numerical Arrays
  • Indexing and Slicing in Numpy Arrays
  • Advanced Indexing in Numpy
  • Broadcasting in Numpy: What It Is and How It Works
  • Iterating Over Arrays in Numpy
  • Manipulating Numpy Arrays
  • Binary Operations in Numpy Arrays
  • String Operations in Numpy Arrays
  • Performing Mathematical Operations with Numpy
  • Arithmetic Functions in Numpy
  • Statistical Operations in Numpy
  • Sorting, Searching, and Counting in Numpy
  • Numpy Byte Swapping: An Overview
  • Copies and Views in Numpy
  • Working with Matrices Using Numpy
  • Linear Algebra Operations in Numpy
Data Visualization Techniques
  • Introduction to Matplotlib for Visualization
  • Exploring Seaborn for Advanced Plotting
  • Interactive Visualizations with Plotly
SQL and Databases
  • SQL Query Fundamentals: SELECT, INSERT, UPDATE, DELETE
  • SQL Functions and Stored Procedures
  • Primary and Foreign Keys in SQL
  • Window Functions and Their Applications
  • Partitioning and Handling Joins in SQL
  • Advanced Joins, Unions, Indexing, and CTE
  • Using Triggers and Case Statements in SQL
  • Understanding Normal Forms and Pivoting in Databases
NoSQL Databases (MongoDB)
  • Introduction to MongoDB and Basic Operations
  • Creating Databases and Collections in MongoDB
  • Inserting and Querying Data in MongoDB
  • Sorting and Limiting Data in MongoDB
  • Deleting and Dropping Collections in MongoDB
  • Updating Documents in MongoDB
Statistics for Data Science
  • Overview of Basic Statistical Terms
  • Types of Statistics and Data
  • Levels of Measurement in Statistics
  • Measures of Central Tendency: Mean, Median, Mode
  • Measures of Dispersion: Variance, Standard Deviation
  • Understanding Random Variables and Their Role
  • Skewness and its Interpretation
  • Covariance and Correlation Analysis
  • Probability Density and Distribution Functions
  • Types of Probability Distributions: Binomial, Poisson, Normal
  • Normal Distribution and Its Significance
  • Z-Stats and Their Application in Data Analysis
  • Central Limit Theorem Explained
  • Estimation Techniques in Statistics
  • Hypothesis Testing Concepts
Advanced Statistics for Data Science
  • Understanding Hypothesis Testing Mechanisms
  • P-Value Interpretation in Hypothesis Testing
  • T-Stats and Their Role in Statistical Inference
  • Comparing T-Stats and Z-Stats
  • When to Use T-Tests vs Z-Tests
  • Types of Errors in Hypothesis Testing: Type 1 and Type 2
  • Bayesian Statistics and Bayes Theorem
  • Confidence Intervals and Their Importance
  • Interpreting Confidence Levels and Margins of Error
  • Chi-Square Tests and Applications in Data Science
Feature Engineering and Data Preprocessing
  • Handling Missing Data and Imbalanced Data
  • Identifying and Dealing with Outliers
  • Feature Scaling Techniques
  • Data Encoding Methods
  • Feature Selection Methods: Backward Elimination, Forward Elimination
  • Recursive Feature Elimination Techniques
  • Covariance and Correlation in Feature Engineering
  • Variance Inflation Factor (VIF)
Exploratory Data Analysis (EDA)
  • Analyzing Bike Sharing Trends and Data
  • Sentiment Analysis of Movie Reviews
  • Customer Segmentation and Cross-Selling Opportunities
  • Analyzing Wine Types and Their Quality
  • Music Trends Analysis and Recommendation
  • Forecasting Stock Prices and Commodities
Machine Learning Fundamentals
  • Understanding AI, ML, DL, and DS
  • Types of ML Techniques: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning
Supervised Machine Learning
  • Simple Linear Regression with Hands-on Implementation
  • Multiple Linear Regression and Evaluation
  • Performance Metrics: MSE, MAE, RMSE
  • Logistic Regression and its Evaluation Metrics
  • Support Vector Machines: Classifiers and Regressors
  • Naive Bayes Classifier Implementation
  • K-Nearest Neighbors (KNN) Classification and Regression
  • Decision Trees and Random Forest Models
  • Gradient Boosting and Adaboost Classifiers
  • XGBoost Classifier and Regressor Implementations
Unsupervised Machine Learning
  • Introduction to Clustering Algorithms
  • K-Means, Hierarchical, and DBSCAN Clustering
  • Evaluating Clustering Performance: Silhouette Score, Davies-Bouldin Index
Time Series Analysis
  • ARIMA Modeling for Time Series Forecasting
  • Autocorrelation and Partial Autocorrelation
  • Handling Seasonal Time-Dependent Components
Natural Language Processing (NLP)
  • NLP Roadmap for Machine Learning
  • Tokenization and Text Preprocessing Techniques
  • Named Entity Recognition and POS Tagging
  • Word Embeddings: Word2Vec and GloVe
  • Building NLP Models Using TF-IDF, BOW, and N-Grams
Advanced Machine Learning Projects
  • End-to-End Projects: Fault Prediction, Review Scraping, Price Prediction
  • Real-World Projects Using ML for Industrial Applications
Interview Preparation for Data Science
  • Resume Writing Tips and Interview Prep for Python, Stats, and ML
  • Common Interview Questions: Python, Stats, ML Deep Dives
  • Mock Interviews and Question Discussion
Deep Learning
  • Introduction to Deep Learning
  • Why Deep Learning Is Becoming Popular
  • Perceptron Intuition
  • Artificial Neural Network (ANN) Working
  • Backpropagation in ANN
  • Chain Rule of Derivatives
  • Vanishing Gradient Problem
  • Exploding Gradient Problem
  • Different Activation Functions
  • Different Types of Loss Functions
  • Different Types of Optimizers
  • Weight Initialization Techniques
  • Dropout Layer
  • Batch Normalization
  • Visualization of Neural Networks
  • Colab Pro Setup
  • TensorFlow Installation 2.0
  • TensorFlow 2.0 Neural Network Creation
  • Netron (Model Visualizer)
  • PyTorch Installation
  • PyTorch Neural Network Creation
  • Deep Learning with Computer Vision (CV)
  • CNN Fundamentals
  • CNN Explained in Detail - CNNexplainer, Tensor Space
  • Various CNN-based Architectures
  • Training CNN from Scratch
  • Building Web Apps for CNN
  • Various CNN Architectures with Research Papers and Mathematics
  • Deep Learning with Natural Language Processing (NLP)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM)
  • Bi-directional LSTM (BiLSTM)
  • Stacked LSTM
  • GRU Implementation
  • Sequence-to-Sequence (Seq2Seq)
  • Encoders and Decoders
  • Attention Mechanism
  • Attention Neural Networks
  • Self-Attention
  • Introduction to Transformers
  • BERT Model
  • GPT-2 Model
Generative AI
  • Generative AI
  • What is Generative AI?
  • Why are Generative Models Required?
  • Understanding Generative Models and Their Significance
  • Generative AI vs Discriminative Models
  • Recent Advancements and Research in Generative AI
  • Generative AI End-to-End Project Lifecycle
  • Key Applications of Generative Models
OpenAI
  • Introduction to OpenAI
  • What is OpenAI API and How to Generate OpenAI API Key?
  • Installation of OpenAI Package
  • Experiment in the OpenAI Playground
  • How to Setup Your Python Environment for OpenAI
  • Working with OpenAI API and Python Code
  • Use Cases of GPT Models in OpenAI
  • Fine-tuning OpenAI Models
Prompt Engineering
  • Prompt Design: Various Techniques to Improve Output Quality
  • Best Practices in OpenAI Prompt Engineering
  • Understanding OpenAI's GPT-3 and GPT-4
  • Common Use Cases in OpenAI: Chatbots, Summarizers, Sentiment Analysis
  • How GPT-3 and GPT-4 Work
  • Setting Up OpenAI API with LangChain
  • LangChain: How It Works and Its Applications
  • Creating OpenAI Chatbots with LangChain
  • Managing Long-Form Content Generation in OpenAI with LangChain
Vector Database with Python for LLM Use Cases
  • Vector Database with Python for LLM Use Cases
  • Understanding Vector Databases for AI Applications
  • Setting Up Pinecone with OpenAI
  • Setting Up Weaviate Vector DB for AI Applications
  • Vector Search and Use Cases in NLP
  • How LangChain Interacts with Vector Databases for Real-Time Applications
Building AI Chat Agent with LangChain and OpenAI
  • Building AI Chat Agent with LangChain and OpenAI
  • Steps to Building an Intelligent AI Chatbot
  • Understanding Agent Frameworks in LangChain for Use in OpenAI-based Chatbots
  • Integration of Chatbot with LangChain Models
  • Managing Memory in LangChain Chatbots
Weaviate Vector Database
  • Weaviate Vector Database Setup and Use Cases
  • Integrating Weaviate Vector DB into Chatbots
  • Understanding Vector Search and Its Benefits
  • Building Chatbots Using LangChain and Weaviate
  • Practical Implementations of Chatbots with Weaviate
Pinecone Vector Database
  • Pinecone Vector Database Setup and Use Cases
  • Integrating Pinecone Vector DB for Chatbot Applications
  • Working with Vector Database Search for NLP Tasks
  • Integrating Pinecone with LangChain for Real-Time Responses
  • Best Practices in Pinecone for AI Applications
Hands-on with LangChain
  • Hands-on with LangChain for Building AI Chatbots
  • Advanced Techniques in LangChain for Building Conversational Agents