Data Science and Machine Learning with Python - Hands On! | Frank Kane | Skillshare

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

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

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80 Lessons (10h)
• 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

10:26
• 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

5:00
• 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
74 students are watching this class

Data Scientists enjoy one of the top-paying jobs, with an average salary of \$120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including:

• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Multivariate Regression
• Multi-Level Models
• Support Vector Machines
• Reinforcement Learning
• Collaborative Filtering
• K-Nearest Neighbor