Machine Learning

Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning and experience.

In other words, machine learning allows computers to learn from data and make data-driven predictions or decisions. Machine learning is a dynamic and rapidly evolving field with vast potential for innovation and impact across various industries and domains.

It continues to be a transformative force in the modern world, with an ever-expanding range of applications and advancements.

This machine learning course is designed to provide a comprehensive introduction to the principles, techniques, and applications of machine learning.

It covers the fundamental concepts, various types of machine learning algorithms, and practical implementation. The course focuses on hands-on experience, allowing students to work on real-world machine learning projects.

This machine learning course provides students with a solid understanding of machine learning concepts, practical skills, and the ability to apply machine learning in various domains. It serves as a stepping stone for further exploration of advanced machine learning topics and specialization in specific areas of interest.

Curriculum:

• Week 1: Introduction to Machine Learning
• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Machine Learning Lifecycle
• Python and Libraries for Machine Learning
• Week 2: Data Pre-processing and Exploration
• Data Cleaning and Transformation
• Data Visualization
• Handling Missing Data
• Feature Selection and Engineering
• Week 3: Supervised Learning
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forest
• K-Nearest Neighbours
• Model Evaluation Metrics
• Week 4: Unsupervised Learning
• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction 
• Week 5: Unsupervised Learning
• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA, t-SNE)
• Anomaly Detection
• Recommendation Systems
• Week 6: Final Projects 
• Students work on machine learning projects of their choice
• Project presentations