Machine Learning Algorithms: From Basics to Implementation

About this path

This path guides you through the main machine learning algorithms, starting with the fundamentals and moving to practical implementations. You will learn linear and logistic regression, decision trees, random forests, Naive Bayes, support vector machines, K-nearest neighbors, and clustering. The later lessons introduce neural networks, including CNNs and RNNs, and show real-world applications. The material comes from a curated YouTube playlist by Simplilearn, so you can follow along at your own pace.

What you will learn

• Understand the core concepts of machine learning and the difference between supervised, unsupervised, and reinforcement learning.
• Implement and interpret linear and logistic regression models in Python and R.
• Build and evaluate decision trees and random forests for classification and regression tasks.
• Apply Naive Bayes, SVM, and KNN algorithms to real datasets.
• Explain how convolutional and recurrent neural networks work and where they are used.
• Perform hierarchical clustering and understand its applications.

Who this is for

Beginners who want a structured free introduction before deeper practice.

Curriculum