New to Machine Learning – Where Should I Start?

Hi guys…

I’m new to machine learning and looking for some guidance on how to get started. I’ve heard a lot about its applications and potential, and I’m excited to dive in, but I’m not sure where to begin.

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My advice is to begin with the basics: try out online courses on platforms like Coursera or edX. They have great beginner-friendly content. I also found working on small projects helped me a lot. For example, I built a simple model to predict house prices, and it was a great learning experience. Just start small, keep experimenting, and you’ll gradually get the hang of it

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Start with online courses or tutorials on platforms like Coursera or edX. Focus on foundational concepts like supervised learning, unsupervised learning, and neural networks. Books like “Hands-On Machine Learning” by Aurélien Géron can also be helpful.

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Probably exactly the same way that I did it the first time:

  • Become interested and enthusiastic
  • Watch Statquest videos to understand the thoery
  • Watch freecodecamp videos to brush up on python and use of ml packages like skl and pandas
  • Get a master’s degree in computer science, economics, or statistics to add some institutional authority
  • Solve a problem at work with ML
  • Generate business value and get promoted because of it
  • Play, learn, have fun

Building a Strong Foundation:

  • Math and Statistics: Develop a solid grasp of linear algebra, calculus, and probability. These mathematical concepts are fundamental to understanding machine learning algorithms.
  • Programming: Python is the primary language for machine learning. Learn its syntax, data structures, and key libraries such as NumPy, Pandas, and Matplotlib.
  • Machine Learning Concepts: Understand core concepts like supervised and unsupervised learning, model evaluation, and overfitting.

Online Resources and Courses:

  • Coursera, edX, and Udemy: These platforms offer a broad spectrum of machine learning courses suitable for various skill levels.
  • Kaggle: Explore datasets and participate in competitions to refine your skills and apply what you’ve learned.
  • Google’s Machine Learning Crash Course: Access a free introductory course to get started with machine learning.

Practical Experience:

  • Personal Projects: Begin with small-scale projects to apply your knowledge and build confidence.
  • Kaggle Competitions: Engage in competitions to learn from others, test your skills, and improve your techniques.
  • Open-Source Contributions: Contribute to machine learning projects on open-source platforms to gain hands-on experience and collaborate with others.

Key Tips:

  • Start Small: Begin with simpler projects and gradually tackle more complex problems as you build your skills.
  • Continuous Learning: Stay updated with the latest advancements in machine learning, as the field is rapidly evolving.
  • Experimentation: Explore different algorithms and techniques to determine what works best for your specific use cases.
  • Build a Portfolio: Create a portfolio showcasing your projects to demonstrate your capabilities and attract potential employers or collaborators.