Machine Learning is one of the most in-demand skills in today’s world, and the best way to learn it is through practical projects. This beginner-friendly course will guide you step by step through the essential algorithms of Machine Learning using Python, while working on real-world datasets like Titanic, Wine Data, and Bank Marketing.

We’ll start from the basics — understanding how models work, how to evaluate their performance, and how to improve them. Then, we’ll move into hands-on projects covering a wide range of algorithms:

• Logistic Regression with the Titanic dataset

• Naive Bayes with Wine classification

• Decision Trees with Bank Marketing data

• Random Forests for ensemble learning

• Gradient Descent methods (Batch, Stochastic, Mini-Batch)

• Linear Regression projects with real datasets

• K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)

• Boosting techniques and Unsupervised Learning (Clustering, PCA)

By the end of this course, you will:

• Understand the intuition behind core ML algorithms

• Be able to implement them from scratch and with Python libraries

• Know how to choose the right model and evaluate its performance

• Gain confidence through practical, project-based learning

No advanced math is required — just basic Python knowledge and a willingness to learn. Whether you’re a student, programmer, or professional looking to upskill, this course will give you a solid foundation to start your Machine Learning journey.