# Data Science and Machine Learning with Python - Hands On!

#### Frank Kane, Founder of Sundog Education, ex-Amazon

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80 Lessons (9h 57m)
• 1. Introduction

2:44
• 2. Windows Setup Instructions

10:43
• 3. Mac Setup Instructions

8:17
• 4. Linux Setup Instructions

9:11

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

9:29
• 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. Important Spark Installation Notes

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
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