Machine Learning with Python

Course Description

This comprehensive Machine Learning course will take you from the basics to advanced techniques using Python. You'll learn how to build, evaluate, and deploy machine learning models for real-world applications.

Through hands-on projects, you'll work with popular libraries like scikit-learn, TensorFlow, and Keras to solve problems in classification, regression, clustering, and more. By the end of the course, you'll have a portfolio of machine learning projects to showcase your skills.

What You'll Learn

• Fundamentals of machine learning and artificial intelligence
• Python programming for data science and ML
• Data preprocessing and feature engineering techniques
• Supervised learning algorithms (linear regression, decision trees, SVM, etc.)
• Unsupervised learning techniques (clustering, dimensionality reduction)
• Neural networks and deep learning fundamentals
• Model evaluation and hyperparameter tuning
• Deploying ML models in production

Course Curriculum

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of ML: Supervised, Unsupervised, Reinforcement
• Python for ML: NumPy, Pandas, Matplotlib

Module 2: Data Preprocessing

• Handling missing data
• Feature scaling and normalization
• Categorical data encoding

Module 3: Supervised Learning

• Linear and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines

Module 4: Model Evaluation

• Train-test split
• Cross-validation
• Performance metrics

Module 5: Unsupervised Learning

• Clustering algorithms (K-Means, Hierarchical)
• Principal Component Analysis
• Anomaly detection

Module 6: Neural Networks

• Introduction to TensorFlow and Keras
• Building and training neural networks
• Convolutional Neural Networks