Pre-Requisites

Basic Python Programming

Python Image Processing

Introduction to digital image processing, Basic operations: Crop, Scale, Rotate, Flip, Contrast, Brightness, Color adjustments, Edge detection, Blur, Sharpening.

Data Visualization (Matplotlib)

Scatter Plot, Line Plot, Bar Chart, Histogram, Box Plot, Chart styling, Subplots.

Python for Data Analysis

NumPy

Array creation, indexing, slicing, broadcasting, universal functions, transposition, aggregations, mathematical operations, array IO.

Pandas

Basics: Series, DataFrame, Reading CSV/Excel/JSON.

Cleaning: Missing values, replacing, conversions.

Selection: loc, iloc, Boolean filtering.

Export: CSV, Excel, JSON, SQL.

Module 1 – Introduction to ML

Python for ML, NumPy & Pandas, Applications of ML, Supervised vs Unsupervised Learning, Math for ML – Vectors, Matrices, Linear Algebra, Trends & visualization with Python.

Module 2 – Regression

Linear Regression, Multiple Regression, Polynomial Regression, Model evaluation, Train/Test split, Regularization, Overfitting vs Underfitting.

Module 3 – Classification

KNN, Decision Trees, Logistic Regression, Support Vector Machines, Naive Bayes, Metrics – Accuracy, Precision, Recall, F1, Confusion Matrix, Bootstrapping.

Module 4 – Unsupervised Learning

K-Means, Hierarchical Clustering, DBSCAN, PCA (Dimensionality Reduction), Feature Selection, Anomaly Detection, Evaluating clustering performance.

Module 5 – Advanced ML (Supervised + Unsupervised)

Cross-Validation (K-Fold), Ensemble models – Bagging, Boosting, Random Forest, Hyperparameter tuning, Grid Search, Random Search, Handling imbalanced data, Real-world ML model building workflow.