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Machine Learning with scikit-learn LiveLessons

Video Description

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Introduction

Lesson 1: What is Machine Learning?

Learning objectives

1.1 Install

1.2 Understand the ML Libraries (new lesson, title TBD)

1.3 Describe the techniques used in machine learning

1.4 Understand the difference between "deep learning" and other ML techniques

1.5 Understand classification versus regression versus.clustering and over/underfitting

1.6 Perform dimensionality reduction, explain feature engineering, and utilize feature selection

1.7 Distinguish categorical versus ordinal versus continuous variables

1.8 Perform one-hot encoding

1.9 Utilize hyperparameters and grid search

1.10 Understand choose and metrics

Lesson 2: Exploring a Data Set

Learning objectives

2.1 Uncover anomalies and data integrity problems

2.2 Clean and massage your data

2.3 Choose features and a target

2.4 Implement a train/test split and choose model

Lesson 3: Classification

Learning objectives

3.1 Understand feature importances

3.2 Establish cut points in a decision tree

3.3 Utilize a common API

3.4 Use a more encouraging dataset

3.5 Compare multiple classifiers

3.6 Understand more about feature importances

3.7 Use multiclass classification

3.8 Understand prediction probabilities and decision boundaries

Lesson 4: Regression

Learning objectives

4.1 Sample data sets in scikit-learn

4.2 Compare a gaggle of regressors

4.3 Use linear models

4.4 Understand the pitfalls of linear models

4.5 Use non-linear regressors

Lesson 5: Clustering

Learning objectives

5.1 Compare clustering algorithms

5.2 Cluster to test a hypothesis

5.3 Cluster into N classes

5.4 Cluster into an unknown number of categories

5.5 Use density based clustering: DBScan and HDBScan

5.6 Evaluate clustering

Lesson 6: Hyperparameters

Learning objectives

6.1 Explore one hyperparameter

6.2 Explore many hyperparameters

6.3 Use GridsearchCV

Lesson 7: Feature Engineering and Feature Selection

Learning objectives

7.1 Understand a synthetic example

7.2 Understand dimensionality reduction

7.3 Use principal component analysis (PCA)

7.4 Use other decompositions: NMF, LDA, ICA, t-dist

7.5 Implement feature selection: Univariate

7.6 Implement feature selection: Model-based

7.7 Understand dimensionality expansion (polynomial features)

7.8 Use one-hot encoding

7.9 Scale with StandardScaler, RobustScaler, MinMaxScaler, Normalizer, and others

7.10 Bin values with quantiles or binarize

Lesson 8: Pipelines

Learning objectives

8.1 Understand imperative sequential processing

8.2 Use pipelines

8.3 Do pipelines with grid search

Lesson 9: Robust Train/Test Splits

Learning objectives

9.1 Understand splitting

9.2 Understand multiple splitting: KFold, LeaveOneOut, StratifiedKFold, etc

9.3 Use cross validation

Summary

Table of Contents

  1. Introduction
    1. Machine Learning with scikit-learn LiveLessons: Introduction 00:05:38
  2. Lesson 1: What is Machine Learning?
    1. Learning objectives 00:01:02
    2. 1.1 Install 00:09:45
    3. 1.2 Understand the ML Libraries (new lesson, title TBD) 00:07:35
    4. 1.3 Describe the techniques used in machine learning 00:06:08
    5. 1.4 Understand the difference between "deep learning" and other ML techniques 00:08:37
    6. 1.5 Understand classification versus regression versus.clustering and over/underfitting 00:13:49
    7. 1.6 Perform dimensionality reduction, explain feature engineering, and utilize feature selection 00:06:29
    8. 1.7 Distinguish categorical versus ordinal versus continuous variables 00:05:18
    9. 1.8 Perform one-hot encoding 00:05:37
    10. 1.9 Utilize hyperparameters and grid search 00:04:26
    11. 1.10 Understand choose and metrics 00:12:08
  3. Lesson 2: Exploring a Data Set
    1. Learning objectives 00:00:25
    2. 2.1 Uncover anomalies and data integrity problems 00:11:59
    3. 2.2 Clean and massage your data 00:05:31
    4. 2.3 Choose features and a target 00:06:36
    5. 2.4 Implement a train/test split and choose model 00:09:30
  4. Lesson 3: Classification
    1. Learning objectives 00:01:09
    2. 3.1 Understand feature importances 00:04:41
    3. 3.2 Establish cut points in a decision tree 00:07:06
    4. 3.3 Utilize a common API 00:04:52
    5. 3.4 Use a more encouraging dataset 00:03:47
    6. 3.5 Compare multiple classifiers 00:08:45
    7. 3.6 Understand more about feature importances 00:06:02
    8. 3.7 Use multiclass classification 00:09:44
    9. 3.8 Understand prediction probabilities and decision boundaries 00:07:16
  5. Lesson 4: Regression
    1. Learning objectives 00:00:49
    2. 4.1 Sample data sets in scikit-learn 00:10:47
    3. 4.2 Compare a gaggle of regressors 00:08:42
    4. 4.3 Use linear models 00:05:49
    5. 4.4 Understand the pitfalls of linear models 00:08:27
    6. 4.5 Use non-linear regressors 00:06:58
  6. Lesson 5: Clustering
    1. Learning objectives 00:01:09
    2. 5.1 Compare clustering algorithms 00:05:49
    3. 5.2 Cluster to test a hypothesis 00:04:42
    4. 5.3 Cluster into N classes 00:14:02
    5. 5.4 Cluster into an unknown number of categories 00:12:44
    6. 5.5 Use density based clustering: DBScan and HDBScan 00:08:20
    7. 5.6 Evaluate clustering 00:06:31
  7. Lesson 6: Hyperparameters
    1. Learning objectives 00:01:36
    2. 6.1 Explore one hyperparameter 00:07:40
    3. 6.2 Explore many hyperparameters 00:06:08
    4. 6.3 Use GridsearchCV 00:06:27
  8. Lesson 7: Feature Engineering and Feature Selection
    1. Learning objectives 00:01:20
    2. 7.1 Understand a synthetic example 00:10:16
    3. 7.2 Understand dimensionality reduction 00:02:21
    4. 7.3 Use principal component analysis (PCA) 00:09:57
    5. 7.4 Use other decompositions: NMF, LDA, ICA, t-dist 00:12:05
    6. 7.5 Implement feature selection: Univariate 00:08:19
    7. 7.6 Implement feature selection: Model-based 00:09:54
    8. 7.7 Understand dimensionality expansion (polynomial features) 00:10:18
    9. 7.8 Use one-hot encoding 00:06:04
    10. 7.9 Scale with StandardScaler, RobustScaler, MinMaxScaler, Normalizer, and others 00:14:41
    11. 7.10 Bin values with quantiles or binarize 00:05:06
  9. Lesson 8: Pipelines
    1. Learning objectives 00:00:46
    2. 8.1 Understand imperative sequential processing 00:10:32
    3. 8.2 Use pipelines 00:09:26
    4. 8.3 Do pipelines with grid search 00:07:23
  10. Lesson 9: Robust Train/Test Splits
    1. Learning objectives 00:00:38
    2. 9.1 Understand splitting 00:06:26
    3. 9.2 Understand multiple splitting: KFold, LeaveOneOut, StratifiedKFold, etc 00:11:37
    4. 9.3 Use cross validation 00:07:00
  11. Summary
    1. Summary 00:03:03