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.