Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size7.4 GB
  • Uploaded Bytutsnode
  • Downloads66
  • Last checkedJan. 21st '22
  • Date uploadedJan. 20th '22
  • Seeders 29
  • Leechers40
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Infohash : E2FCF9B1067A33F8903BC443F1B4420E020A12A1


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

Files:

Programming Foundations of Classification and Regression LiveLessons, Part 1 [TutsNode.com] - Programming Foundations of Classification and Regression LiveLessons, Part 1
  • 10 - 2.3 Linear Combinations.mp4 (464.4 MB)
  • 30 - 5.4 Linear Regression, Part 1.mp4 (451.8 MB)
  • 25 - 4.4 Scripts.mp4 (402.5 MB)
  • 12 - 2.5 Geometry, Part 2.mp4 (379.8 MB)
  • 33 - 6.1 Optimization, Part 1.mp4 (373.6 MB)
  • 19 - 3.4 Train Test Splitting and Fitting k-NN.mp4 (363.9 MB)
  • 20 - 3.5 Naive Bayes.mp4 (349.4 MB)
  • 11 - 2.4 Geometry, Part 1.mp4 (291.9 MB)
  • 27 - 5.1 Setup and the Diabetes Dataset.mp4 (291.9 MB)
  • 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)
  • 29 - 5.3 k-Nearest Neighbors for Regression.mp4 (260.3 MB)
  • 06 - 1.4 Three Things You Can Do with scikit-learn and Friends.mp4 (242.6 MB)
  • 36 - 6.4 Resource Evaluation.mp4 (235.8 MB)
  • 24 - 4.3 Resource Evaluation - Memory.mp4 (229.5 MB)
  • 16 - 3.1 Setup and the Iris Dataset.mp4 (227.1 MB)
  • 34 - 6.2 Optimization, Part 2.mp4 (210.3 MB)
  • 23 - 4.2 Resource Evaluation - Time.mp4 (203.2 MB)
  • 14 - 2.7 When Computers and Math Meet.mp4 (198.0 MB)
  • 09 - 2.2 Distributions.mp4 (197.9 MB)
  • 18 - 3.3 k-Nearest Neighbors.mp4 (189.4 MB)
  • 03 - 1.1 Environment Installation.mp4 (184.8 MB)
  • 28 - 5.2 Measures of Center.mp4 (165.1 MB)
  • 31 - 5.5 Linear Regression, Part 2.mp4 (161.4 MB)
  • 08 - 2.1 Probability.mp4 (144.0 MB)
  • 01 - Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1 (Video Training) - Introduction.mp4 (141.6 MB)
  • 22 - 4.1 Learning Evaluation.mp4 (135.8 MB)
  • 35 - 6.3 Learning Performance.mp4 (129.4 MB)
  • 17 - 3.2 Accuracy.mp4 (128.9 MB)
  • 13 - 2.6 Geometry, Part 3.mp4 (116.6 MB)
  • 37 - Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1 (Video Training) - Summary.mp4 (32.0 MB)
  • 07 - Topics.mp4 (17.3 MB)
  • 02 - Topics.mp4 (14.9 MB)
  • 21 - Topics.mp4 (14.6 MB)
  • 15 - Topics.mp4 (14.5 MB)
  • 26 - Topics.mp4 (13.4 MB)
  • 32 - Topics.mp4 (11.2 MB)
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  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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