Machine learning is an exciting and rapidly growing field, but it can be intimidating for those who are just starting out. With the vast amount of information available, it can be challenging to know where to begin. Before jumping headfirst into machine learning, it’s essential to take a step back and lay a solid foundation. In this article, we’ll discuss what you should do before learning machine learning and provide references to help you get started.
Brush Up on Your Mathematics
Machine learning is all about using mathematical algorithms to extract insights from data. To understand these algorithms, you need a good grasp of fundamental mathematical concepts. At a minimum, you should have a solid understanding of linear algebra, calculus, and probability theory. These topics are covered in most undergraduate math programs, but if you need a refresher, there are plenty of resources available online.
For a crash course in linear algebra, check out the Khan Academy’s Linear Algebra series. For calculus, try the Calculus I and Calculus II courses on Coursera. Finally, for probability theory, MIT has an excellent online course called Introduction to Probability.
Learn a Programming Language
Machine learning involves writing code to train models and make predictions. You’ll need to learn a programming language to do this effectively. While there are many programming languages used in machine learning, Python is the most popular. It’s easy to learn, has a simple syntax, and comes with a vast array of libraries and tools for machine learning.
If you’re new to programming, the Python for Data Science Handbook by Jake VanderPlas is an excellent place to start. It covers the basics of Python programming and how to use Python for data analysis and machine learning. If you’re already familiar with programming, you might want to try the free Python courses on Codecademy.
Understand the Basics of Data Science
Data science is the process of extracting insights from data. It involves collecting, cleaning, analyzing, and visualizing data to uncover patterns and trends. Machine learning is one of the tools used in data science to make predictions and identify relationships between variables.
To learn data science, you’ll need to be comfortable with tools like Excel, SQL, and Pandas. Excel is a ubiquitous tool for data analysis, while SQL is used to query databases. Pandas is a Python library for data manipulation and analysis.
For a comprehensive introduction to data science, check out the book Data Science from Scratch by Joel Grus. It covers the basics of Python programming, data analysis, and machine learning.
Choose a Learning Path
There are many resources available for learning machine learning, including online courses, books, and tutorials. It’s essential to choose a learning path that fits your learning style and experience level. If you’re new to machine learning, you might want to start with a beginner course or book. If you’re more experienced, you can try a more advanced course or book.
Some popular online courses for learning machine learning include:
- Andrew Ng’s Machine Learning Course on udemy
- Fast.ai’s Practical Deep Learning for Coders Course
- MIT’s Introduction to Deep Learning Course
For books, some popular options include:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
- Deep Learning with Python by Francois Chollet
Practice, Practice, Practice
Machine learning is a practical field, and the only way to get better is by practicing. Start by working on small projects and gradually move on to more complex ones. Kaggle is an excellent platform for practicing machine learning, as it provides a wide range of datasets and competitions to participate in.
To get started with Kaggle, check out their Getting Started section
