Resolving Python for Machine Learning with Scikit-Learn

How to solve the most common maths errors in Machine Learning using Python?

Learn Python's best practices to become a proficient programmer who can learn any concept independently and transform it into exponential knowledge for Machine Learning.

See the table of contents here.

❓ Focus on solving code errors

❌ If they explain Machine Learning with already-executed tutorials where they don't have any errors, you won't know how to solve them.

1. Furthermore, they make you believe you won't get errors while coding.
2. Nothing further from reality: programmers spend most of their time solving errors.
3. Therefore, the sooner you learn how to solve them, the more time you'll save in your learning journey.

✅ Every time we introduce you to a new concept, we teach you how to solve all the possible errors as if you'd write the same code in another use case.

🧠 Learn 1 pattern for 100 concepts

❌ Most of the courses focus on teaching many concepts separately (as if they don't have any relationship with each other), which makes you believe that:

1. All the concepts have the same importance.
2. You need to learn every single detail of any concept (a lot of brain effort 🤯)

Nothing could be further from reality. For example, in Machine Learning (ML):

1. It's enough to understand that any ML model computes the best numbers for a mathematical equation to make the best possible predictions compared to the actual data.
2. Every time you want to develop an ML model in Python code, you follow the same procedure: fit, predict and score (see article).
3. Therefore, instead of learning 100 ML models separately, you learn the pattern behind them and save a lot of time in your learning journey.

✅ In this course, we focus on connecting the dots between all ML concepts so that you can establish a clear hierarchical mental model after you work out all the exercises.

Therefore, you won't copy-paste the code from past examples. Instead, you'll save time by writing them directly from your mind.

🏋️ Hands-on exercises to learn efficiently

1. 🤯 You won't find theoretical slides with abstract mathematical formulas you won't understand.
2. 👨‍💻 Instead, you will learn Machine Learning by writing the code and interpreting the results on practical exercises.
3. 🤔 Many people believe they know Machine Learning by making sense of the tutorials they watch.
4. 🤷‍♂️ Nevertheless, companies won't hire you because you "understand".
5. 🧠 They will hire you because you can develop Machine Learning code that creates value for the business. Therefore, you need to nurture your skills by practicing.

✅ This course contains 12 strategically-designed practical exercises that you will solve to acquire Python's best practices on code development for Machine Learning.

🧮 You don't need abstract maths

1. 💭 Because Machine Learning uses abstract maths formulas in the algorithms, we falsely believe that we need to learn algebra, calculus, and probability before getting hands-on with ML development.
2. 🤖 In fact, a Python function calculates an ML model without any formula involved in the code:

3. 🧠 So, instead of focusing on abstract maths formulas, you'd save a lot of time and headaches by writing the code that computes all the maths for you!

✅ In this course, we only explain the required math to understand the output that helps companies create value.