Theoretical concepts of Machine Learning

Students will learn about the types of machine learning addressed in Python’s library, sklearn. Students will also learn about sklearn’s models used in supervised learning, semi-supervised learning and unsupervised learning.

Theoretical concepts of Machine Learning Course content

14 sections • 15 lectures • 1h 18m total length

Introduction1 lecture • 5min

  • Introduction05:19

Part I – Types of learning in sklearn1 lecture • 6min

  • Part 1 – Types of machine learning in sklearn05:43

Part II – Types of supervised learning1 lecture • 2min

  • Part II – Types of supervised learning02:22

Part III – Types of semi-supervised learning1 lecture • 2min

  • Part III – Types of semi-supervised learning01:34

Part IV – Unsupervised learning in nsklearn (clustering)1 lecture • 4min

  • Part IV – Unsupervised learning03:43

Part V – Sklearn models for supervised learning2 lectures • 19min

  • Part V(0) – Sklearn models for supervised learning11:57
  • Part V(1) – Sklearn models for supervised learning06:34

Part VI – Sklearn models for semi-supervised learning1 lecture • 6min

  • Part VI – Sklearn models for semi-supervised learning06:25

Part VII – Sklearn models for unsupervised learning1 lecture • 7min

  • Part VII – Sklearn models for unsupervised learning07:05

Part VIII – Dimensionality reduction in sklearn1 lecture • 2min

  • Part VIII – Diminsionality reduction02:11

Part IX – Feature selection in sklearn1 lecture • 4min

  • Part IX – Sklearn feature selection03:38

Part X – Preprocessing in sklearn1 lecture • 7min

  • Part X – Preprocessing in sklearn07:10

Part XI – Hyperparameter tuning in sklearn1 lecture • 3min

  • Part XI – Hyperparameter tuning in sklearn03:06

Part XII – Goodness of fit tests in sklearn1 lecture • 8min

  • Part XII – Goodness of fit testing in sklearn07:46

Bonus Lecture1 lecture • 4min

  • Bonus Lecture04:08

Requirements

  • No programming experience is needed, but it would be helpful to know basic Python programming.

Description

This course covers over 27 functions in Python’s machine learning library, sklearn. The functions covered in this course take the student through the entire machine learning life cycle.

The student will learn the types of learning that are part of sklearn, to include supervised, semi-supervised and unsupervised learning.

The student will learn about the types of estimators used in supervised, semi-supervised and unsupervised learning, to include classification and regression.

The student will learn about a variety of supervised learning estimators to include linear regression, logistic regression, decision tree, random forrest, naive bayes, support vector machine, k nearest neighbour, and neural network.

The student will learn about sklearn’s three semi-supervised functions to make predictions on classification problems.

the student will learn about some of the estimators used to make predictions on unsupervised learning, to include k means, hierarchical and Gaussian method.

The student will learn about dimensionality reduction and feature selection as a means of reducing the number of features in the dataset.

The student will learn about the different functions in sklearn that carry out preprocessing activities to include standardisation, normalisation, encoding and imputation.

The student will learn about hyperparameter tuning and how to perform a grid search on the different parameters in the model to help it work at peak optimisation.

The student will learn about goodness of fit tests, to include root mean squared error, accuracy score, confusion matrix, and classification report, which tell the user how well the model has performed.

The students will receive additional learning and cover the machine learning life cycle to enable him to initiate how own machine learning project using sklearn.

Who this course is for:

  • Beginner Python developers who would like to know how to undertake machine learning using Python’s sklearn library.
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