Python for AI/ML

• Variables & Data Types
• Control Structures
• Functions & Modules
• Data Structures (Lists, Dicts)

Project: Data Preprocessing Script

AI/ML Foundations

• AI vs ML vs DL
• Supervised & Unsupervised Learning
• Data Exploration
• Basic Statistics

Project: Exploratory Data Analysis

• Python libraries: pandas, numpy, openpyxl, json, csv
• Data Cleaning Techniques
• Data Transformation
• Working with Dates and Times
• Working with Dates and Times

Hands-On Projects & Case Studies

Basic algebra (equations, functions)

• Matrices and Matrix Operations
• Algebra
• Probability
• Discriptive, Inferential Statistics

Project: Face Recognition using PCA

ML Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• K-Nearest Neighbors (KNN)
• Model Evaluation Metrics

Project: Predictive Model

Linear & logistic Regression

• Decision Tree
• K-Nearest Neighbors
• TIme series Forecasting
• Classification Reports

Project: Personalized Study Time Predictor

Cluster Evaluation Techniques

• Feature Transformation Techniques
• Association Rule Learning
• Working with Real-World Datasets
• Limitations & Ethical Considerations

Project: Movie Recommendation Clustering

Artificial Neural Networks (ANN)

• Model Building and Evaluation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning and Fine-Tuning

Project: Image classification using CNN (e.g., Cats vs Dogs)

• Transfer Learning and Fine-Tuning
• Deploying Deep Learning Models
• Tokenization and word embeddings
• Model serving with TensorFlow Serving or Flask

Project: Real-time object detection with pre-trained models

Scikit-learn Fundamentals

• Building Neural Networks
• Model Validation
• Working with Real Datasets
• K-Nearest Neighbors (KNN)
• . Integration with Other Tools

Project: Customer Churn Prediction

Model Evaluation and Validation

• Classification Metrics and Evaluation
• Support Vector Machines (SVM)
• Neural Networks Basics
• Capstone Project and Review

Project: classify spam emails

Problem Formulation & Proposal

• Data Collection & Exploration
• CNModel Refinement & Validation
• Deployment & Reportingg
• Presentation & Submission

Project: stock prediction

Project Ideation & Scoping

• Modeling & Evaluation
• Time Series Forecasting (ARIMA, Prophet)
• Model serialization
• Performance Optimization

Project: adaptive learning

• Baseline Modeling
• Checkpoint Review
• Documentation & Dashboards
• Full Rubric Scoring

Project: Predicting