A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.
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Kaggle micro-courses
I know it may be weird to start here, many would prefer to start with the heaviest foundations and math videos to fully understand what is happening behind each ML model. But from my perspective starting with something practical and concrete helps to have a better view of the whole picture.
In addition, these micro-courses take around 4 hours/each to complete so meeting those little goals up front adds an extra motivational boost.
Kaggle micro-course: Python
If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.
Link: https://www.kaggle.com/learn/python
Kaggle micro-course: Pandas
Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.
Link: https://www.kaggle.com/learn/pandas
Kaggle micro-course: Data Visualization
Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.
Link: https://www.kaggle.com/learn/data-visualization
Kaggle micro-course: Intro to Machine Learning
This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.
Link:https://www.kaggle.com/learn/intro-to-machine-learning
Kaggle micro-course: Intermediate Machine Learning
This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.
Link:https://www.kaggle.com/learn/intermediate-machine-learning
Book: Data Science from Scratch
At this point we will momentarily separate ourselves from pandas, scikit-learn and other Python libraries to learn in a practical way what is happening “behind” these algorithms.
This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.
If you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums and explore new methods that you find in other participants solutions.
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