Foundations of Machine Learning
Understand the basics of ML, including supervised, unsupervised, and reinforcement learning techniques.

Python for Machine Learning
Gain expertise in Python, the go-to programming language for ML, and libraries like NumPy, pandas, and scikit-learn.

Data Preprocessing & Feature Engineering
Learn to clean, preprocess, and transform raw data into formats suitable for machine learning models.

ML Algorithms
Master popular algorithms such as linear regression, decision trees, support vector machines, and ensemble models like random forests.

Deep Learning with Neural Networks
Explore deep learning frameworks, including TensorFlow and PyTorch, and build neural networks for tasks like image recognition and natural language processing.

Model Evaluation and Optimization
Learn techniques for evaluating model performance and optimizing hyperparameters for accuracy and efficiency.

Project-Based Learning
Work on real-world projects like predictive modeling, recommendation systems, and sentiment analysis to showcase your skills.