Don't overfit your learning path.
❌ Forget about the idea that you will master Machine Learning by just watching online tutorials or courses.
✅ You need to practice applying the Resolving Python Method.
❌ It's not about what you practice, but how you practice.
✅ PREVIEW the Practical Exercises we propose in our course to understand what we mean by how you must practice to become an independent Python developer ↓
Syllabus
#00 | Machine Learning & the Scikit-Learning Library
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#01 | The Linear Regression & Supervised Regression Models
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#02 | The Decision Tree & Supervised Classification Models
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#03 | Train Test Split for Model Selection
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- 1. Read Article
- 2. Load & Preprocess the Data
- 3. Build Machine Learning Models: Decision Tree, Support Vector Machines & Logistic Regression
- 4. Thinking Process to Create a Function
- 5. Understand the Need for Train Test Split
- 6. Reautomate Process with Train Test Split
- 7. Compare All Machine Learning Models in a DataFrame
- 8. Practical Exercise 3
#04 | Overfitting & Hyperparameter Tuning with Cross Validation
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days
days
after you enroll
#05 | The k-Means & Unsupervised Clustering Models
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days
days
after you enroll
#06 | Principal Component Analysis (PCA) for Dimensionality Reduction
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days
days
after you enroll