Machine Learning Fundamentals

(1,543 reviews)

$199.00

Learn the core concepts of machine learning with this comprehensive course. Master algorithms, data preprocessing, feature engineering, and model evaluation using Python and industry-standard libraries.

What You'll Learn:

Supervised and unsupervised learning
Classification and regression algorithms
Data preprocessing and feature engineering
Model evaluation and validation techniques
Working with scikit-learn and pandas
Real-world ML project implementation

Duration: 12 hours

4,891 students enrolled

Level: Intermediate

Lifetime access

Course Overview

Machine learning is transforming industries and creating new opportunities across the globe. This comprehensive course provides you with a solid foundation in machine learning concepts, algorithms, and practical implementation using Python. You'll learn not just the theory, but also how to apply these techniques to solve real-world problems.

Starting with the fundamentals of supervised and unsupervised learning, you'll progressively advance through various algorithms including linear regression, logistic regression, decision trees, random forests, support vector machines, and clustering techniques. Each concept is explained clearly with visual examples and hands-on coding exercises that reinforce your understanding.

Course Curriculum

This course is divided into ten comprehensive modules:

Module 1: Introduction to machine learning and Python setup
Module 2: Data preprocessing and exploration
Module 3: Supervised learning - Regression
Module 4: Supervised learning - Classification
Module 5: Feature engineering and selection
Module 6: Model evaluation and validation
Module 7: Ensemble methods and boosting
Module 8: Unsupervised learning and clustering
Module 9: Dimensionality reduction techniques
Module 10: Capstone project and deployment

Who Should Take This Course

This course is designed for aspiring data scientists, software developers, analysts, and anyone with basic Python knowledge who wants to break into the field of machine learning. Some programming experience is recommended but not required.