| 0 | 607.4 KB | ||
| 01 - Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1 (Video Training) - Introduction.mp4 | 141.6 MB | ||
| 1 | 161.3 KB | ||
| 2 | 534.4 KB | ||
| 02 - Topics.mp4 | 14.9 MB | ||
| 03 - 1.1 Environment Installation.mp4 | 184.8 MB | ||
| 3 | 241.7 KB | ||
| 4 | 397.6 KB | ||
| 04 - 1.2 Three Things You Can do with NumPy and matplotlib.mp4 | 283.1 MB | ||
| 05 - 1.3 Three Things You Can Do with Pandas.mp4 | 263.9 MB | ||
| 5 | 128.1 KB | ||
| 6 | 612.1 KB | ||
| 06 - 1.4 Three Things You Can Do with scikit-learn and Friends.mp4 | 242.6 MB | ||
| 07 - Topics.mp4 | 17.3 MB | ||
| 7 | 56.4 KB | ||
| 8 | 147.3 KB | ||
| 08 - 2.1 Probability.mp4 | 144 MB | ||
| 9 | 892.2 KB | ||
| 09 - 2.2 Distributions.mp4 | 197.9 MB | ||
| 10 | 84.4 KB | ||
| 10 - 2.3 Linear Combinations.mp4 | 464.4 MB | ||
| 11 | 687 KB | ||
| 11 - 2.4 Geometry, Part 1.mp4 | 291.9 MB | ||
| 12 - 2.5 Geometry, Part 2.mp4 | 379.8 MB | ||
| 12 | 369 KB | ||
| 13 - 2.6 Geometry, Part 3.mp4 | 116.6 MB | ||
| 13 | 160.7 KB | ||
| 14 - 2.7 When Computers and Math Meet.mp4 | 198 MB | ||
| 14 | 548.9 KB | ||
| 15 | 881.9 KB | ||
| 15 - Topics.mp4 | 14.5 MB | ||
| 16 | 735.1 KB | ||
| 16 - 3.1 Setup and the Iris Dataset.mp4 | 227.1 MB | ||
| 17 - 3.2 Accuracy.mp4 | 128.9 MB | ||
| 17 | 803 KB | ||
| 18 - 3.3 k-Nearest Neighbors.mp4 | 189.4 MB | ||
| 19 - 3.4 Train Test Splitting and Fitting k-NN.mp4 | 363.9 MB | ||
| 19 | 52.8 KB | ||
| 20 | 626.7 KB | ||
| 20 - 3.5 Naive Bayes.mp4 | 349.4 MB | ||
| 21 - Topics.mp4 | 14.6 MB | ||
| 21 | 243.6 KB | ||
| 22 - 4.1 Learning Evaluation.mp4 | 135.8 MB | ||
| 22 | 903.6 KB | ||
| 23 | 633.3 KB | ||
| 23 - 4.2 Resource Evaluation - Time.mp4 | 203.2 MB | ||
| 24 - 4.3 Resource Evaluation - Memory.mp4 | 229.5 MB | ||
| 25 - 4.4 Scripts.mp4 | 402.5 MB | ||
| 25 | 441.3 KB | ||
| 26 | 252.2 KB | ||
| 26 - Topics.mp4 | 13.4 MB | ||
| 27 - 5.1 Setup and the Diabetes Dataset.mp4 | 291.9 MB | ||
| 27 | 653.2 KB | ||
| 28 | 74.6 KB | ||
| 28 - 5.2 Measures of Center.mp4 | 165.1 MB | ||
| 29 - 5.3 k-Nearest Neighbors for Regression.mp4 | 260.3 MB | ||
| 29 | 361.3 KB | ||
| 30 - 5.4 Linear Regression, Part 1.mp4 | 451.8 MB | ||
| 30 | 32.2 KB | ||
| 31 - 5.5 Linear Regression, Part 2.mp4 | 161.4 MB | ||
| 31 | 668.1 KB | ||
| 32 | 137.1 KB | ||
| 32 - Topics.mp4 | 11.2 MB | ||
| 33 - 6.1 Optimization, Part 1.mp4 | 373.6 MB | ||
| 34 - 6.2 Optimization, Part 2.mp4 | 210.3 MB | ||
| 35 - 6.3 Learning Performance.mp4 | 129.4 MB | ||
| 36 - 6.4 Resource Evaluation.mp4 | 235.8 MB | ||
| 37 - Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1 (Video Training) - Summary.mp4 | 32 MB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
| 33 | 416.5 KB | ||
| 34 | 496.7 KB | ||
| 35 | 599.9 KB | ||
| ▲ 73 total files | |||
Description
Code-along sessions move you from introductory machine learning concepts to concrete code.
Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond nodding along in discussion to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.
You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques.
Skill Level
Beginner
Learn How To
Build and apply simple classification and regression models
Evaluate learning performance with train-test splits
Evaluate learning performance with metrics tailored to classification and regression
Evaluate the resource usage of your learning models
Who Should Take This Course
If you are becoming familiar with the basic concepts of machine learning and you want an experienced hand to help you turn those concepts into running code, this course is for you. If you have some coding knowledge but want to see how Python can drive basic machine learning models and practice, this course is for you.
Course Requirements
A basic understanding of programming in Python (variables, basic control flow, simple scripts)
Released 2/2020
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.9 GB | freecoursewb | 4 weeks | 23 | 5 | |
| 799.8 MB | xHOBBiTx | 1 year | 18 | 2 | |
| 704.7 MB | freecoursewb | 1 year | 15 | 3 | |
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Udemy - Foundations Of Programming - Unlock The Basics For Beginners Posted by
freecoursewb in Other
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364.7 MB | freecoursewb | 2 years | 14 | 3 |
| 1.5 GB | freecoursewb | 2 years | 5 | 0 |
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