Master Python programming, data structures, algorithms, and object-oriented programming concepts.

Learn linear algebra, calculus, probability, and statistics essential for understanding ML algorithms.

Get familiar with Jupyter Notebooks, Git, virtual environments, and package management.

Master pandas, NumPy for data cleaning, preprocessing, and exploratory data analysis.

Learn to create meaningful visualizations using Matplotlib, Seaborn, and Plotly.

Understand relational databases, SQL queries, and data storage for AI applications.

Learn supervised, unsupervised learning algorithms and their practical applications.

Understand cross-validation, metrics, hyperparameter tuning, and model selection.

Master feature selection, extraction, and transformation techniques for better model performance.

Understand neural network architectures, backpropagation, and deep learning fundamentals.

Learn CNNs, image processing, object detection, and computer vision applications.

Explore text processing, RNNs, transformers, and language model applications.

Learn to deploy ML models using Docker, cloud platforms, and API frameworks.

Build automated ML pipelines, model versioning, and continuous integration workflows.

Implement model monitoring, performance tracking, and maintenance strategies.

Work with GPT, fine-tuning, prompt engineering, and LLM applications.

Explore RL algorithms, agent-based systems, and game AI applications.

Stay updated with latest research, contribute to open source, and build innovative solutions.