Machine Learning with scikit-learn LiveLessons

MP4 | Video: AVC 1280×720 | Audio: AAC 48KHz 2ch | Duration: 7H 17M | 13 GB
Genre: eLearning | Language: English

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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)

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