Comprehensive Machine Learning: From Fundamentals to AI Event, 01.Jun.2026

Course Description

This intensive 5-day course provides a comprehensive introduction to machine learning, covering fundamental concepts, popular algorithms, and practical applications. Participants will gain hands-on experience with Python-based tools and frameworks, learning to build, evaluate, and deploy machine learning models for real-world problems.

Learning Objectives

• Understand core machine learning concepts and algorithms
• Gain proficiency in using Python for data analysis and model development
• Learn to build and evaluate supervised and unsupervised learning models
• Explore neural networks and deep learning techniques
• Develop skills to implement end-to-end machine learning projects

Course Modules

Day 1: Introduction to Machine Learning and Python

• Overview of machine learning and its applications
• Python fundamentals for data science
• Data preprocessing and exploratory data analysis
• Introduction to scikit-learn library

Day 2: Supervised Learning - Classification and Regression

• Linear and logistic regression
• Decision trees and random forests
• Support Vector Machines (SVM)
• Model evaluation and validation techniques

Day 3: Unsupervised Learning and Dimensionality Reduction

• Clustering algorithms (K-means, hierarchical clustering)
• Principal Component Analysis (PCA)
• Feature selection and engineering
• Anomaly detection techniques

Day 4: Neural Networks and Deep Learning

• Artificial Neural Networks (ANN) fundamentals
• Deep learning architectures
• Convolutional Neural Networks (CNN) for image processing
• Recurrent Neural Networks (RNN) for sequence data

Day 5: Advanced Topics and Project Implementation

• Ensemble methods and boosting algorithms
• Natural Language Processing (NLP) basics
• Introduction to reinforcement learning
• End-to-end machine learning project implementation

Practical Wins for Participants

• Build a predictive model for customer churn analysis
• Develop an image classification system using deep learning
• Create a recommendation engine using collaborative filtering
• Implement a sentiment analysis tool for social media data