Software developers lead the class. Industry-Relevant Curriculum Online and Offline Training Available Internship with Live Projects 100% Placement Support Until You Get Placed

This short-term Machine Learning course is designed for beginners and professionals who want to quickly understand the fundamentals of machine learning and its practical applications. The course begins with an introduction to machine learning concepts, types of machine learning (supervised, unsupervised, and reinforcement learning), and the workflow of a typical machine learning project. Learners will also get an overview of Python libraries commonly used for ML, such as NumPy, pandas, and scikit-learn.

The course then covers essential programming and statistical concepts required for machine learning, including data preprocessing, feature selection, and model evaluation. Students will learn how to implement simple algorithms like linear regression, logistic regression, decision trees, and clustering techniques. Practical exercises will help learners understand how to train models, make predictions, and evaluate their performance.

TFinally, the course introduces advanced topics such as neural networks, basic deep learning concepts, and model deployment considerations. By the end of the course, participants will have a solid understanding of machine learning principles, be able to build and evaluate basic models, and be prepared to explore advanced machine learning and AI projects.