IntermediateLearning Path

Ready to take your AI skills to the next level? This intermediate learning path covers more advanced topics like NLP, computer vision, and AI projects to help you grow your expertise.

Step 1: Recap of Basic AI Concepts

Review the basic AI concepts you learned in the beginner path to prepare for more advanced topics in this learning path.

Step 2: Data Preprocessing & Feature Engineering

Learn how to clean and preprocess data, and create new features that improve the performance of your machine learning models.

Step 3: Advanced Linear Algebra & Probability

Dive deeper into linear algebra and probability theory to understand the mathematical foundations of machine learning algorithms.

Step 4: Deep Dive into Machine Learning Algorithms

Explore advanced machine learning algorithms like decision trees, random forests, and gradient boosting to improve your model performance.

Step 5: Natural Language Processing Basics

Learn the fundamentals of natural language processing (NLP) to process and analyze human language data using machine learning techniques.

Step 6: Computer Vision Basics

Explore the basics of computer vision to understand how machines can interpret and analyze visual information from the world around us.

Step 7: Intermediate AI Projects

Apply your knowledge to more challenging AI projects like sentiment analysis, image classification, or chatbot development to build real-world AI applications