Course Description:

This 12-week course provides a comprehensive introduction to the field of machine learning, covering both theoretical and practical aspects of machine learning. Students will learn the fundamentals of machine learning, including supervised, unsupervised, and reinforcement learning, and will develop practical skills in preparing and engineering features for machine learning models.

Through a combination of lectures, assignments, and projects, students will learn about supervised learning algorithms, including linear regression, logistic regression, decision trees, and random forests, as well as unsupervised learning algorithms, including K-means clustering, hierarchical clustering, and principal component analysis (PCA). Students will also learn about model evaluation and tuning, including techniques for evaluating model performance, cross-validation, and hyperparameter tuning.

In the final weeks of the course, students will explore advanced topics in machine learning, including neural networks and deep learning. Throughout the course, students will work on practical projects and assignments to apply their knowledge and skills in machine learning, and will complete a final project that demonstrates their understanding of machine learning concepts and algorithms.

By the end of this course, students will have a comprehensive understanding of machine learning concepts and algorithms, as well as practical experience in applying them to real-world problems. This course is ideal for anyone looking to start a career in machine learning or enhance their skills in this field.

Course Outline:

Week 1-2: Introduction to Machine Learning

- Overview of machine learning concepts

- Types of machine learning (Supervised, Unsupervised, Reinforcement)

- Introduction to Python for machine learning

WHAT’S INCLUDED IN THIS MACHINE LEARNING TRAINING COURSE?

World-Class Training Sessions from Experienced Instructors

DURATION |12 WEEKS | 3 TIMES IN A WEEK | 1-2 HOURS | VIRTUAL AND PHYSICAL |KARU & BWARI ABUJA