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

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

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

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69 Videos (9h 2m)
• Introduction

2:44
• Getting What you Need

2:36
• Installing Enthought Canopy

6:51
• Python Basics, Part 1

15:58
• Python Basics, Part 2

9:41
• Running Python Scripts

3:55
• Introducing the Pandas Library

10:14
• Types of Data

6:58
• Mean, Median, Mode

5:26
• Using mean, median, and mode in Python

8:30
• Variation and Standard Deviation

11:12
• Probability Density Function; Probability Mass Function

3:27
• Common Data Distributions

7:45
• Percentiles and Moments

12:32
• A Crash Course in matplotlib

13:46
• Covariance and Correlation

11:31
• Exercise: Conditional Probability

10:16
• Exercise Solution: Conditional Probability

2:18
• Bayes' Theorem

5:23
• Linear Regression

11:01
• Polynomial Regression

8:04
• Multivariate Regression

9:52
• Multi-Level Models

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

8:57
• Using Train/Test to Prevent Overfitting

5:47
• Bayesian Methods: Concepts

3:59
• Implementing a Spam Classifier with Naive Bayes

8:05
• K-Means Clustering

7:23
• Clustering People by Income and Age

5:14
• Measuring Entropy

3:09
• Decision Trees: Concepts

8:43
• Decision Trees: Predicting Hiring Decisions

9:47
• Ensemble Learning

5:59
• Support Vector Machines (SVM) Overview

4:27
• Using SVM to Cluster People

5:36
• User-Based Collaborative Filtering

7:57
• Item-Based Collaborative Filtering

8:15
• Finding Movie Similarities

9:08
• Improving the Results of Movie Similarities

7:59
• Making Movie Recommendations to People

10:22
• Improving the Recommender's Results

5:29
• K-Nearest-Neighbors: Concepts

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

12:29
• Dimensionality Reduction; Principal Component Analysis

5:44
• PCA Example with the Iris Data Set

9:05
• Data Warehousing; ETL and ELT

9:05
• Reinforcement Learning

12:44

6:15
• K-Fold Cross Validation

10:54
• Data Cleaning and Normalization

7:10
• Cleaning Web Log Data

10:56
• Normalizing Numerical Data

3:22
• Detecting Outliers

7:00
• Installing Spark - Part 1

7:02
• Installing Spark - Part 2

13:29
• Spark Introduction

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

11:42
• Introducing MLLib

5:09
• Decision Trees in Spark

16:00
• K-Means Clustering in Spark

11:07
• TF / IDF

6:43
• Searching Wikipedia with Spark

8:11
• Using the Spark 2.0 DataFrame API for MLLib

7:57
• A/B Testing Concepts

8:23
• T-Tests and P-Values

5:59
• Hands-On with T-Tests

6:03
• Determining How Long to Run an Experiment

3:24
• A/B Test Gotchas

9:26
• Where to Go From Here

2:59
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