AI & Machine Learning

Python for AI & Data Science

Level: Beginner →Intermediate
1 Month

What You'll Learn

01. Python Programming for AI
  • Python Fundamentals
  • Variables, Data Types & Operators
  • Conditions & Loops
  • Functions & Modules
  • Lists, Tuples, Dictionaries & Sets
  • Exception Handling
  • OOPs
02. NumPy
  • NumPy Arrays
  • Array Operations
  • Indexing & Slicing
  • Mathematical Operations
  • Statistical Functions
03. Pandas — Data Analysis
  • Series & DataFrames
  • Importing CSV/Excel Data
  • Data Selection & Filtering
  • Data Cleaning
  • Missing Values
  • Data Transformation
  • Grouping & Aggregation
04. Data Visualization
  • Matplotlib
  • Seaborn
  • Charts & Graphs
  • Data Distribution
  • Correlation Visualization
05. Statistics for AI/ML
  • Mean, Median & Mode
  • Variance & Standard Deviation
  • Probability Basics
  • Correlation
  • Data Distribution
06. Practical Projects
  • Student Dataset Analysis
  • Sales Dataset Analysis
  • EV Dataset Visualization

Machine Learning

Level: Intermediate → Advanced
2 Month

What You'll Learn

01. Machine Learning Fundamentals
  • What is Machine Learning?
  • AI vs ML vs Deep Learning
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Training & Testing Data
  • Features & Target Variables
02. Data Preparation
  • Data Cleaning
  • Handling Missing Values
  • Encoding Categorical Data
  • Feature Scaling
  • Train-Test Split
  • Feature Selection
03. Regression
  • Linear Regression
  • Multiple Linear Regression
  • Regression Evaluation
  • Mean Squared Error
  • R² Score
  • Project - House Price Prediction
04. Classification
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • Classification Metrics
  • Confusion Matrix
  • Accuracy, Precision, Recall & F1 Score
  • Project: Customer / Student Classification
05. Unsupervised Learning
  • Clustering Concepts
  • K-Means Clustering
  • Hierarchical Clustering
  • Customer Segmentation
06. Model Evaluation & Improvement
  • Cross Validation
  • Overfitting & Underfitting
  • Bias & Variance
  • Hyperparameter Tuning
  • Model Comparison

Advanced AI & Deep Learning

Level: Advanced → AI Developer
2 Months

What You'll Learn

01. Deep Learning Fundamentals
  • What is Deep Learning?
  • Neural Networks
  • Neurons & Layers
  • Activation Functions
  • Forward & Backpropagation
  • Loss Functions
  • Optimizers
  • Epochs & Batch Size
02. TensorFlow & Keras
  • TensorFlow Fundamentals
  • Keras
  • Building Neural Networks
  • Model Training
  • Validation & Testing
  • Model Evaluation
  • Saving & Loading Models
03. Computer Vision
  • Image Processing Basics
  • Image Classification
  • CNN — Convolutional Neural Networks
  • Transfer Learning
  • Object Detection Concepts
  • Practical: Image Classification Project
04. Natural Language Processing
  • NLP Fundamentals
  • Text Cleaning
  • Tokenization
  • Stop Words
  • Text Classification
  • Sentiment Analysis
  • Introduction to Transformers
  • Practical: Sentiment Analysis Project
05. Generative AI Fundamentals
  • Generative AI Concepts
  • Large Language Models (LLMs)
  • Prompt Engineering
  • AI APIs
  • OpenAI API
  • Building AI-powered applications
  • Responsible AI & AI Safety Basics
06. Final AI Projects
  • Image Classification System
  • Sentiment Analysis Application
  • AI Chatbot
  • AI-powered Application using API
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