Data Science and Machine Learning with Python - Hands On!

Frank Kane, Founder of Sundog Education, ex-Amazon

Play Speed
  • 0.5x
  • 1x (Normal)
  • 1.25x
  • 1.5x
  • 2x
80 Lessons (9h 56m)
    • 1. Introduction

      2:44
    • 2. Windows Setup Instructions

      10:43
    • 3. Mac Setup Instructions

      8:17
    • 4. Linux Setup Instructions

      9:11
    • 5. Please follow me on SkillShare!

      0:16
    • 6. Python Basics, Part 1

      4:59
    • 7. Python Basics, Part 2

      5:17
    • 8. Python Basics, Part 3

      2:46
    • 9. Python Basics, Part 4

      4:02
    • 10. Intro to Pandas

      10:08
    • 11. Types of Data

      6:58
    • 12. Mean, Median, Mode

      5:26
    • 13. Using mean, media, and mode in Python

      8:20
    • 14. Variation and Standard Deviation

      11:12
    • 15. Probability Density Function; Probability Mass Function

      3:27
    • 16. Common Data Distributions

      7:45
    • 17. Percentiles and Moments

      12:32
    • 18. A Crash Course in matplotlib

      13:46
    • 19. Data Visualization with Seaborn

      17:30
    • 20. Covariance and Correlation

      11:31
    • 21. Exercise: Conditional Probability

      16:04
    • 22. Exercise Solution: Conditional Probability

      2:20
    • 23. Bayes' Theorem

      5:23
    • 24. Linear Regression

      11:01
    • 25. Polynomial Regression

      8:04
    • 26. Multiple Regression

      11:26
    • 27. Multi-Level Models

      4:36
    • 28. Supervised vs. Unsupervised Learning, Train / Test

      8:57
    • 29. Using Train/Test to Prevent Overfitting

      5:47
    • 30. Bayesian Methods: Concepts

      3:59
    • 31. Implementing a Spam Classifier with Naive Bayes

      8:05
    • 32. K-Means Clustering

      7:23
    • 33. Clustering People by Income and Age

      5:14
    • 34. Measuring Entropy

      3:09
    • 35. Windows: Installing Graphviz

      0:22
    • 36. Mac: Installing Graphviz

      1:16
    • 37. Linux: Installing Graphviz

      0:54
    • 38. Decision Trees: Concepts

      8:43
    • 39. Decision Trees: Predicting Hiring Decisions

      9:47
    • 40. Ensemble Learning

      5:59
    • 41. Support Vector Machines (SVM) Overview

      4:27
    • 42. Using SVM to Cluster People

      8:38
    • 43. User-Based Collaborative Filtering

      7:57
    • 44. Item-Based Collaborative Filtering

      8:15
    • 45. Finding Movie Similarities

      9:08
    • 46. Improving the Results of Movie Similarities

      7:59
    • 47. Making Movie Recommendations to People

      10:22
    • 48. Improving the Recommender's Results

      5:29
    • 49. K-Nearest-Neighbors: Concepts

      3:44
    • 50. Using KNN to Predict a Rating for a Movie

      12:29
    • 51. Dimensionality Reduction; Principal Component Analysis

      5:44
    • 52. PCA Example with the Iris Data Set

      9:05
    • 53. Data Warehousing; ETL and ELT

      9:05
    • 54. Reinforcement Learning

      12:44
    • 55. Hands-On with Q-Learning

      12:56
    • 56. Bias / Variance Tradeoff

      6:15
    • 57. K-Fold Cross Validation

      10:54
    • 58. Data Cleaning and Normalization

      7:10
    • 59. Cleaning Web Log Data

      10:56
    • 60. Normalizing Numerical Data

      3:22
    • 61. Detecting Outliers

      6:21
    • 62. Installation Notes for Java and Spark

      0:32
    • 63. Installing Spark - Part 1

      6:59
    • 64. Installing Spark - Part 2

      7:20
    • 65. Spark Introduction

      9:10
    • 66. Spark and the Resilient Distributed Dataset (RDD)

      11:42
    • 67. Introducing MLLib

      5:09
    • 68. Decision Trees in Spark

      16:15
    • 69. K-Means Clustering in Spark

      11:23
    • 70. TF / IDF

      6:43
    • 71. Searching Wikipedia with Spark

      8:21
    • 72. Using the Spark 2 DataFrame API for MLLib

      8:07
    • 73. Deploying Models to Production

      8:42
    • 74. A/B Testing Concepts

      8:23
    • 75. T-Tests and P-Values

      5:59
    • 76. Hands-On with T-Tests

      6:03
    • 77. Determining How Long to Run an Experiment

      3:24
    • 78. A/B Test Gotchas

      9:26
    • 79. Where to Go From Here

      2:59
    • 80. Let's Stay in Touch

      0:46
147 students are watching this class
How students rated this class
Leave Review

Watch more to review

We ask our students to watch a few lessons before reviewing to make sure we get quality feedback!

Best Suited for
 --  Beginner Intermediate Advanced Beginner/Intermediate Intermediate/Advanced All Levels

Community Generated

The level is determined by a majority opinion of students who have reviewed this class. The teacher's recommendation is shown until at least 5 student responses are collected.

Be the first!

No ratings just yet—watch a few lessons to be the first to share whether this class met your expectations.

Expectations Met?
  • Exceeded!
    0%
  • Yes
    0%
  • Somewhat
    0%
  • Not really
    0%
Be the first to leave a review in our updated system!
Reviews Archive

In October 2018, we updated our review system to improve the way we collect feedback. Below are the reviews written before that update. You are welcome to edit your old review into the new system!

36 of 38 students recommend this class