Fundamentals of AI & ML

About this course

This course provides a solid foundation in Artificial Intelligence and Machine Learning, starting from core mathematical concepts like linear algebra and statistics. You will explore supervised and unsupervised learning, neural networks, and the practical implementation of algorithms using Python and industry-standard libraries like Scikit-Learn and TensorFlow. Designed specifically for Indian engineering students across all branches, this course bridges the gap between academic theory and industry requirements. Whether you are a beginner looking to build your first model or an aspiring data scientist preparing for competitive campus placements, this curriculum offers the right balance of conceptual depth and hands-on clarity. By the end of this course, you will be able to build predictive models, analyze complex datasets, and deploy machine learning solutions to solve real-world problems. You will gain a competitive edge in technical interviews and be well-prepared for high-paying roles in the rapidly evolving Indian tech landscape, equipped with a portfolio of functional AI projects.

What you'll cover

• 1Introduction to AI, ML, and Data Science Ecosystem
• 2Essential Mathematics: Linear Algebra and Statistics
• 3Python for Data Analysis with NumPy and Pandas
• 4Linear Regression and Gradient Descent
• 5Logistic Regression and Classification Metrics
• 6Decision Trees and Ensemble Learning
• 7Support Vector Machines and K-Nearest Neighbors
• 8Unsupervised Learning: Clustering and PCA
• 9Fundamentals of Neural Networks
• 10Introduction to Deep Learning with TensorFlow
• 11Model Evaluation and Hyperparameter Tuning
• 12Capstone Project: Building an End-to-End ML Application