# 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.

- Furthermore, they make you believe you
**won't get errors while coding**. - Nothing further from reality: programmers spend most of their time
**solving errors**. - 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:

- All the concepts have the same importance.
- 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):

- 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. - Every time you want to develop an ML model in Python code, you follow
**the same procedure: fit, predict and score**(see article). - 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**

- 🤯 You won't find theoretical slides with
**abstract mathematical formulas**you won't understand. - 👨💻 Instead, you will
**learn Machine Learning by writing the code**and interpreting the results on practical exercises. - 🤔 Many people believe they know Machine Learning by making sense of the tutorials they watch.
- 🤷♂️ Nevertheless,
**companies won't hire you because you "understand"**. - 🧠 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**

- 💭 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. - 🤖 In fact, a Python function calculates an ML model without any formula involved in the code:

- 🧠 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.

**5**sales