Course Curriculum

• 1. Python (Core & Advanced)
  • Libraries for Data Science & AI/ML:
    • NumPy – Numerical Computing
    • Pandas – Data Manipulation & Analysis
    • SciPy – Scientific Computing
    • Matplotlib – Data Visualization
    • Seaborn – Statistical Visualization
    • TensorFlow – Deep Learning Framework
    • PyTorch – Deep Learning Framework
• 2. Databases
  • SQL - MySQL
  • NoSQL - MongoDB
• 3. Power BI
• 4. Mathematics for Machine Learning (ML)
  • Linear Algebra
  • Advanced Linear Algebra
  • Calculus
  • Probability
  • Statistics
• 5. Probability and Statistics for Machine Learning (ML)
• 6. Feature Engineering and Model Evaluation
• 7. Advanced Machine Learning Algorithms
• 8. Model Tuning and Optimization
• 9. Neural Networks
• 10. Deep Learning
• 11. Convolutional Neural Networks CNNs
• 12. Recurrent Neural Networks RNNs
• 13. Sequence Modelling
• 14. Transformers and Attention Mechanisms
• 15. Computer Vision
• 16. Transfer Learning and Fine Tuning
• 17. Machine Learning Algorithms and Implementations
• 18. Generative AI
• 19. Large Language Models(LLMs)
• 20. Prompt Engineering
• 21. AI Tools: ChatGPT, Copilot, Google Gemini
• 22. Hands-on Projects (Any Five)
  • Calculator
  • Spam Email Detector
  • Handwritten Digit Recognition
  • Predict House Prices
  • Predict Car Prices
  • Image Classifier
  • Sentiment Analysis
  • Movie Recommendation System
  • Book Recommendation System
  • Movie Rating Prediction
  • Weather Forecasting
  • Stock Price Prediction
  • Text Summarizer
  • Fake Product Review Detection
  • Resume Scanner
  • Disease Prediction
  • Credit Card Fraud Detection
  • Healthcare Data Analysis
  • Banking Fraud Detection
  • Customer Segmentation
  • NLP Chatbot Development
  • Image Classification Project
  • Recommendation Systems
  • Generative AI Project using LLMs

• 1. Python (Core & Advanced)
  • Core Python
    • Data Types
      • Numbers (Integer, Float, Complex)
      • Strings and String Operations
      • Lists (Indexing, Slicing, Methods)
      • Tuples (Immutable Sequences)
      • Dictionaries (Key-Value Pairs, Methods)
      • Sets (Unique Elements, Set Operations)
    • Control Structures
      • If-Else Statements
      • For and While Loops
      • Comprehensions (List, Dictionary, Set)
    • Functions
      • Defining and Calling Functions
      • Function Arguments (Positional, Keyword, Default)
      • Anonymous Functions (Lambda)
      • Variable Scope (Local, Global)
    • Object-Oriented Programming
      • Classes and Objects
      • Constructors and Methods
      • Encapsulation and Abstraction
      • Inheritance and Polymorphism
  • Advanced Python
    • Modules and Packages
    • Exception Handling
    • Iterators and Generators
    • Decorators
    • Context Managers (with statement)
    • File Handling
      • Reading and Writing Files
      • Working with CSV Files
      • Working with JSON Files
  • Python Libraries for Data Science and AI/ML
    • NumPy
      • Arrays and Array Operations
      • Broadcasting
      • Linear Algebra Functions
      • Random Number Generation
    • Pandas
      • Series and DataFrames
      • Data Cleaning and Handling Missing Values
      • Filtering and Sorting Data
      • GroupBy and Aggregations
      • Merging and Joining Datasets
    • SciPy
      • Scientific Computations
      • Optimization
      • Statistics
      • Signal Processing
    • Matplotlib
      • Line, Bar, Scatter Plots
      • Subplots
      • Styling and Customization
    • Seaborn
      • Heatmaps
      • Pairplots
      • Distribution Plots
    • TensorFlow
      • Tensors and Operations
      • Building Neural Networks
      • Model Training and Evaluation
      • Deploying Models
    • PyTorch
      • Tensors and Autograd
      • Defining Neural Networks
      • Training and Optimization
      • Transfer Learning
• 2. Databases
  • SQL (MySQL)
    • Database Basics
      • Creating Databases and Tables
      • Understanding Data Types
    • Data Manipulation
      • INSERT, UPDATE, DELETE
      • SELECT Queries
      • Filtering Data with WHERE
    • Advanced SQL
      • Joins (INNER, LEFT, RIGHT, FULL)
      • Subqueries
      • Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
    • Database Optimization
      • Indexes
      • Views
      • Stored Procedures
      • Transactions and ACID Properties
  • NoSQL (MongoDB)
    • Introduction to NoSQL Databases
    • Collections and Documents
    • CRUD Operations
      • Create Documents (Insert)
      • Read Documents (Find Queries)
      • Update Documents
      • Delete Documents
    • Indexes in MongoDB
    • Aggregation Framework
    • Data Modelling in NoSQL
• 3. Power BI
  • Data Import and Cleaning
  • Data Transformation with Power Query
  • Creating Interactive Dashboards
  • DAX Functions and Advanced Analytics
  • Integration with SQL and Python
• 4. Mathematics for Machine Learning
  • Linear Algebra
    • Vectors and Matrices
    • Matrix Multiplication
    • Determinants
    • Eigenvalues and Eigenvectors
  • Advanced Linear Algebra
    • Singular Value Decomposition
    • Principal Component Analysis
  • Calculus
    • Derivatives and Integrals
    • Partial Derivatives
    • Gradients and Gradient Descent
  • Probability
    • Random Variables
    • Probability Distributions (Normal, Binomial, Poisson)
    • Bayes’ Theorem
  • Statistics
    • Descriptive Statistics (Mean, Median, Mode, Variance)
    • Inferential Statistics
    • Hypothesis Testing
    • Correlation and Regression
• 5. Probability and Statistics for Machine Learning
  • Sampling Methods
  • Central Limit Theorem
  • Confidence Intervals
  • ANOVA and Chi-Square Tests
• 6. Feature Engineering and Model Evaluation
  • Data Preprocessing
    • Handling Missing Data
    • Encoding Categorical Variables
    • Scaling and Normalization
  • Feature Selection
    • Filter Methods
    • Wrapper Methods
    • Embedded Methods
  • Model Evaluation Metrics
    • Accuracy
    • Precision
    • Recall
    • F1-Score
    • ROC Curve and AUC
    • Confusion Matrix
• 7. Advanced Machine Learning Algorithms
  • Decision Trees
  • Random Forests
  • Support Vector Machines
  • Gradient Boosting
    • XGBoost
    • LightGBM
    • CatBoost
  • Clustering
    • K-Means
    • DBSCAN
    • Hierarchical Clustering
  • Dimensionality Reduction
    • Principal Component Analysis
    • t-SNE
• 8. Model Tuning and Optimization
  • Hyperparameter Tuning
    • Grid Search
    • Random Search
    • Bayesian Optimization
  • Cross-Validation Techniques
  • Regularization
    • L1 Regularization
    • L2 Regularization
    • Dropout
• 9. Neural Networks
  • Perceptrons
  • Feedforward Networks
  • Backpropagation Algorithm
• 10. Deep Learning
  • Activation Functions
  • Optimizers
  • Loss Functions
  • Batch Normalization
  • Dropout
• 11. Convolutional Neural Networks
  • Convolution Layers
  • Pooling Layers
  • Image Classification
  • Object Detection
  • Image Segmentation
• 12. Recurrent Neural Networks
  • Vanilla RNNs
  • Long Short-Term Memory Networks (LSTMs)
  • Gated Recurrent Units (GRUs)
  • Time-Series Forecasting
• 13. Sequence Modelling
  • Word Embeddings
    • Word2Vec
    • GloVe
  • Language Models
  • Text Generation
• 14. Transformers and Attention Mechanisms
  • Self-Attention Mechanism
  • Transformer Architecture
  • BERT
  • GPT
• 15. Computer Vision
  • Image Processing with OpenCV
  • Image Classification
  • Object Detection
  • Optical Character Recognition
  • Face Recognition
• 16. Transfer Learning and Fine Tuning
  • Using Pre-trained Models
  • Fine-tuning for Custom Datasets
• 17. Machine Learning Algorithms and Implementations
  • Regression Projects
  • Classification Projects
  • Clustering Projects
  • End-to-End ML Pipelines
• 18. Generative AI
  • Generative Adversarial Networks
    • Generator and Discriminator Models
    • Training GANs
    • Image and Video Generation
  • Diffusion Models
    • Stable Diffusion
    • DALL·E
  • Applications
    • Text Generation
    • Image Generation
    • Audio Generation
• 19. Large Language Models
  • Introduction to LLMs
    • GPT
    • LLaMA
    • Falcon
    • Mistral
  • Fine-tuning and Customization
  • Building AI Assistants and Chatbots
• 20. Prompt Engineering
  • Principles of Effective Prompting
  • Zero-shot Learning
  • One-shot Learning
  • Few-shot Learning
  • Applications
    • Content Generation
    • Chatbots
    • Process Automation
• 21. AI Tools: ChatGPT, Copilot, Google Gemini
• 22. Hands-on Projects (Any Five)
  • Calculator
  • Spam Email Detector
  • Handwritten Digit Recognition
  • Predict House Prices
  • Predict Car Prices
  • Image Classifier
  • Sentiment Analysis
  • Movie Recommendation System
  • Book Recommendation System
  • Movie Rating Prediction
  • Weather Forecasting
  • Stock Price Prediction
  • Text Summarizer
  • Fake Product Review Detection
  • Resume Scanner
  • Disease Prediction
  • Credit Card Fraud Detection
  • Healthcare Data Analysis
  • Banking Fraud Detection
  • Customer Segmentation
  • NLP Chatbot Development
  • Image Classification Project
  • Recommendation Systems
  • Generative AI Project using LLMs