Data Science and Machine Learning with Python - Hands On!

Frank Kane, Founder of Sundog Education, ex-Amazon

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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
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Project Description

Unzip the attached SkillShareProject.zip file, and open up the FinalProject-SkillShare.ipynb file for your final challenge!

Your task is to apply machine learning to classifying masses found in mammograms as benign or malignant. You'll use a variety of classification techniques covered in this course, and see how they compare in their accuracy.

When you're done, my solution is in the FinalProjectSolution-SkillShare.ipynb notebook. No peeking before you're done!

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