Machine Learning with scikit-learn LiveLessons

Video description

6+ Hours of Video Instruction

Learn the main concepts and techniques used in modern machine learning through numerous examples written in scikit-learn Overview

Machine Learning with scikit-learn LiveLessons is your guide to the scikit-learn library, which provides a wide range of algorithms in machine learning that are unified under a common and intuitive Python API. Most of the dozens of classes provided for various kinds of models share the large majority of the same calling interface. Quite often you can easily substitute one algorithm for another with very little or no change in your underlying code. This enables you to explore the problem space quickly and often to arrive at an optimal–or at least satisficing–approach to your problem domain or datasets.

The scikit-learn library is built on the foundations of the numeric Python stack. It uses NumPy for its fundamental data structures and optimized performance, and it plays well with pandas and matplotlib. It is free software under a BSD license. The great bulk of machine learning programming in Python is done with scikit-learn—at least outside the specialized domain of deep neural networks. About the Instructor David Mertz has been involved with the Python community for 20 years, with data science, (under various previous names) and with machine learning since way back when it was more likely to be called “artificial intelligence.” He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column Charming Python for IBM developerWorks, for many years; Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them. Skill Level


Learn How To

Use various machine learning techniques

Explore a dataset

Perform various types of classification

Use regression, clustering, and hyperparameters

Use feature engineering and feature selection

Implement data pipelines

Develop robust train/test splits

Who Should Take This Course

Programmers and statisticians interested in using Python and the scikit-learn library to implement machine learning

Course Requirements

Programming experience

Table of Contents


Lesson 1: What Is Machine Learning?

Lesson 2: Exploring a Dataset

Lesson 3: Classification

Lesson 4: Regression

Lesson 5: Clustering

Lesson 6: Hyperparameters

Lesson 7: Feature Engineering and Feature Selection

Lesson 8: Pipelines

Lesson 9: Robust Train/Test Splits


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Table of contents

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

Product information

  • Title: Machine Learning with scikit-learn LiveLessons
  • Author(s): David Mertz
  • Release date: January 2019
  • Publisher(s): Pearson
  • ISBN: 0135474191