AI and ML Learning From the Beginning

By: Engineer's Planet

AI and machine learning have changed various sectors and are driving developments in healthcare, transportation, finance, and other fields. As a new engineer, you may be eager to start a career in this exciting area. However, AI/ML is a large field with several fields and ideas. So, where do you begin

Build a strong foundation by learning essential concepts like machine learning algorithms, neural networks, and computer vision through beginner-friendly courses on platforms like Coursera and EdX.

1. Getting Started with AI/ML

Master Python, foundational math (linear algebra, calculus, statistics), data analysis tools (NumPy, Pandas), and coding with algorithms and data structures through platforms like HackerRank and GeeksforGeeks.

2. Essential Skills

Start hands-on projects like logistic regression models, neural networks, or ML web apps. Practice on Kaggle datasets to gain practical experience and transform theoretical knowledge into concrete expertise.

3. Apply Machine Learning Skills

Learn and practice with powerful ML libraries like TensorFlow, PyTorch, Keras, and Scikit-learn. Taking courses on these tools provides reusable modules and optimized routines for faster development.

4. Explore ML Libraries

Develop an intuitive understanding of ML-related math by revisiting concepts like calculus, linear algebra, and probability. Visual resources like 3Blue1Brown can deepen your grasp and make complex ML approaches more accessible.

5. Strengthen Math Foundations

In conclusion, Becoming an AI/ML engineer requires patience, persistence, and continuous learning. Focus on fundamentals, apply knowledge through projects, and maintain curiosity. Lifelong study will prepare you for a rewarding and evolving engineering profession.