Introduction

Machine Learning Fundamentals is a comprehensive 5-day course designed for professionals seeking to gain a foundational understanding of machine learning concepts and techniques. Participants will engage with core algorithms and practical applications, empowering them with the skills needed to implement machine learning solutions in real-world scenarios.

Objectives

• Understand the core concepts and definitions of machine learning.
• Familiarize with various types of machine learning algorithms.
• Learn to preprocess and visualize datasets effectively.
• Gain hands-on experience with popular machine learning tools and libraries.
• Apply machine learning techniques to solve real-world problems.

Course Outlines

Day 1: Introduction to Machine Learning

• Overview of Machine Learning and its applications
• Key definitions and terminology
• Supervised vs Unsupervised Learning
• History and evolution of Machine Learning
• Challenges and future trends

Day 2: Data Preprocessing and Visualization

• Understanding datasets and feature selection
• Data cleaning and handling missing values
• Normalization and standardization
• Exploratory Data Analysis (EDA) techniques
• Visualization tools and libraries

Day 3: Supervised Learning Techniques

• Linear Regression and its applications
• Classification algorithms: Logistic Regression, Decision Trees
• Model evaluation metrics and techniques
• Overfitting and underfitting concepts
• Hands-on with popular supervised learning libraries

Day 4: Unsupervised Learning Techniques

• Clustering algorithms: K-means, Hierarchical Clustering
• Dimensionality Reduction techniques: PCA
• Anomaly detection methods
• Applications and limitations of unsupervised learning
• Practical exercises with unsupervised learning tools

Day 5: Practical Applications and Advanced Topics

• Introduction to Neural Networks and Deep Learning
• Reinforcement Learning basics
• Case studies on machine learning applications
• Deployment and productionization of models
• Future scope and career paths in Machine Learning