Scikit-Learn Pro 2024: Master ML with Python
Focused View
1:28:18
1 - Introduction to Scikitlearn.mp4
02:43
1 - Practice Test 01.html
2 - Lesson 01.html
3 - Coding Exercises.mp4
06:21
4 - Data Preprocessing.mp4
02:45
5 - Lesson 02.html
6 - Coding Exercises.mp4
06:02
7 - Supervised Learning Regression.mp4
02:41
8 - Lesson 03.html
9 - Coding Exercises.mp4
06:11
10 - Supervised Learning Classification.mp4
02:49
11 - Lesson 04.html
12 - Coding Exercises.mp4
06:09
13 - Model Evaluation and Selection.mp4
02:43
14 - Lesson 05.html
15 - Coding Exercises.mp4
05:46
16 - Unsupervised Learning Clustering.mp4
02:34
17 - Lesson 06.html
18 - Coding Exercises.mp4
05:20
19 - Dimensionality Reduction.mp4
02:41
20 - Lesson 07.html
21 - Coding Exercises.mp4
05:47
22 - Ensemble Learning.mp4
02:32
23 - Lesson 08.html
24 - Coding Exercises.mp4
06:39
25 - Advanced Topics Model Interpretation.mp4
02:50
26 - Lesson 09.html
27 - Coding Exercises.mp4
06:14
2 - Practice Test 02.html
28 - Final Project EndtoEnd Machine Learning Pipeline.mp4
02:41
29 - Lesson 10.html
30 - Coding Exercises.mp4
06:50
More details
Course Overview
Master machine learning with Python's Scikit-learn through hands-on coding exercises, covering everything from data preprocessing to model deployment in real-world applications.
What You'll Learn
- Implement supervised and unsupervised learning algorithms
- Preprocess data and optimize model performance
- Build complete ML pipelines with cross-validation
Who This Is For
- Aspiring data scientists building ML skills
- Python developers expanding into machine learning
- Professionals applying ML to industry problems
Key Benefits
- Practical coding challenges with real datasets
- Comprehensive coverage of Scikit-learn features
- End-to-end ML project experience
Curriculum Highlights
- Data Preprocessing & Feature Engineering
- Supervised Learning Techniques
- Model Evaluation & Deployment
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Category
- language english
- Training sessions 20
- duration 1:28:18
- Release Date 2025/05/10
