Part 5/8:
At this stage, working with local models, libraries like Langchain and Langraph, and creating basic AI applications can demystify the technology and strengthen your coding skills. Tools like Streamlit can also help in constructing user interfaces and data dashboards, thus providing practical exposure to building real-world AI applications.
Step Four: Learn Core Machine Learning Fundamentals
After getting your hands dirty with applications, it’s crucial to delve into core ML principles. Understanding the foundational algorithms remains relevant because they often deliver superior results without the complexity of large language models (LLMs). Focus on machine learning techniques like regression, classification, and clustering, using libraries such as Scikit-learn.