- Learn Python: Start with basic Python programming concepts, including OOP and data structures.
- Master machine learning basics: Understand the ML process, explore algorithms like linear regression and gradient descent, and use tools like scikit-learn.
- Practice data preprocessing: Learn feature extraction, scaling, and encoding techniques for building efficient models.
- Tackle practical projects: Work on real-world projects, such as auto insurance prediction or customer segmentation with K-means.
- Explore deep learning: Gain expertise in convolutional neural networks (CNNs) through hands-on projects like image colorization and road sign recognition.