Applied Machine Learning in Python – Self Paced

Master Applied Machine Learning with Python – from supervised & unsupervised learning to deep learning. Learn evaluation, optimization, and deployment of real-world ML models using Python.

Unlock the power of Applied Machine Learning with Python and build real-world intelligent systems that can learn, predict, and adapt. This comprehensive course is designed to give you both the theoretical foundation and hands-on experience needed to master machine learning algorithms, model evaluation, and optimization techniques using Python.

The course begins with the fundamentals of machine learning and gradually advances to supervised learning techniques such as K-Nearest Neighbors, Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, and Gradient Boosting. You’ll also dive into unsupervised learning, clustering, dimensionality reduction, and manifold learning.

Special focus is given to evaluation methods including confusion matrices, ROC curves, cross-validation, precision-recall analysis, and model calibration. You’ll learn how to overcome challenges like overfitting, underfitting, and data leakage, while optimizing models for performance across different metrics.

In the final modules, you’ll explore neural networks, deep learning basics, and practical applications in classification and regression. With Python libraries like NumPy, Pandas, scikit-learn, and Matplotlib, you’ll gain the tools to analyze, visualize, and model datasets effectively.

By the end of this course, you will be equipped with the skills to design, evaluate, and deploy robust machine learning models for real-world business, research, and technology applications.

📖 Course Modules

1. Fundamentals of Machine Learning

Introduction, key concepts & ML landscape
Python tools for machine learning

2. Supervised Machine Learning – Part 1 & 2

K-Nearest Neighbors: Classification & Regression
Linear Regression (Least Squares, Ridge, Lasso, Polynomial)
Logistic Regression & Linear Classifiers (SVMs, Kernel SVMs)
Multi-class classification

3. Evaluation & Model Selection

Overfitting, underfitting & cross-validation
Confusion matrices, precision-recall, ROC curves
Regression evaluation metrics
Model calibration & optimization

4. Decision Trees & Ensemble Methods

Decision Trees & One-Hot Encoding
Random Forest & Gradient Boosted Trees

5. Neural Networks & Deep Learning

Basics of neural networks
Deep learning concepts & applications

6. Advanced Topics

Data leakage & handling pitfalls
Dimensionality reduction & manifold learning
Clustering techniques

🌟 Why Choose This Course?

Covers both theory and applied machine learning with Python
Hands-on practice with real-world datasets & Python libraries
Learn supervised, unsupervised, and deep learning techniques
Includes model evaluation, tuning & optimization methods
Ideal for students, professionals, and researchers aiming to become Data Scientists, ML Engineers, or AI Specialists