Intro to Machine Learning with Python

By Bernd Klein. Last modified: 17 Feb 2022.

Machine Learning

Machine learning is is the kind of programming which gives computers the capability to automatically learn from data without being explicitly programmed. This means in other words that these programs change their behaviour by learning from data.

We will cover various aspects of machine learning in this tutorial. Of course, everything will be related to Python. So it is Machine Learning by using Python. It might well be that you came to this website when looking for an answer to the question: What is the best programming language for machine learning? Python is clearly one of the top players!

machine learning: robot jugglers

We will cover in this tutorial on Machine Learning and Python the following topics amongst others:

Machine learning can be roughly separated into three categories:

Supervised learning
The machine learning program is both given the input data and the corresponding labelling. This means that the learn data has to be labelled by a human being beforehand.
Unsupervised learning
No labels are provided to the learning algorithm. The algorithm has to figure out the a clustering of the input data.
Reinforcement learning
A computer program dynamically interacts with its environment. This means that the program receives positive and/or negative feedback to improve it performance.

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In this chapter

1. Machine Learning with PythonIntroduction in Machine Learning and Python
2. Machine Learning TerminologyMachine Learning related Terminology used in this Tutorial
3. Evaluation MetricsPython Machine Learning: Difference between Accuracy and precision, recall, F1-Score
4. Data Representation and Visualization of DataData representation and visualization in Machine Learning with Python
5. Available Data Sets in SklearnOverview and presentation of the available datasets in sklearn
6. Artificial Datasets with Scikit-LearnCreating Synthetical datasets for Machine Learning usage with and without Scikit-Learn (sklearn)
7. Train and Test Sets by Splitting Learn and Test DataData Sets in Machine Learning, splitting them in learn and test sets with Python
8. k-Nearest Neighbor Classifier in PythonIntroduction into k-nearest neighbor classifiers with Python
9. k-Nearest-Neighbor Classifier with sklearnk-nearest neighbor classifiers with Python and sklearn
10. Neural Networks IntroductionTutorial on Neural Networks with Python
11. Separating Classes with Dividing LinesShowing the connection between dividing lines (decision boundaries) and neural networks with a single perceptron using Python
12. A Simple Neural Network from Scratch in PythonExamining simple neural networks with one perceptron. Pointing out the limits by using Python programs.
13. Perceptron class in sklearnIntroduction in the Perceptron Class of sklearn with some examples.
14. Neural Networks, Structure, Weights and MatricesIntroduction into the structure of a Neural Network, explaining the weights and the usage Matrices with Python
15. Running a Neural Network with PythonGet a neural network running in Python
16. Backpropagation in Neural NetworksNeural Network: simple introduction into backpropagation and gradual descent
17. Training a Neural Network with PythonUnderstanding how the learning / training of a Neural Network written in Python works.
18. Softmax as Activation FunctionExplaining the softmax function and using it in a neural network as an activation function
19. Confusion Matrix in Machine LearningConfusion Matrix in Machine Learning and Python. Definition and Examples with Python Code
20. Training and Testing with MNISTNeural Network: using and testing with MNIST data set
21. Dropout Neural Networks in PythonNeural Network: using and testing with MNIST data set
22. Neural Networks with ScikitTutorial on Neural Networks with Python and Scikit
23. A Neural Network for the Digits DatasetA continuous Machine Learning example of the classification with neuronal networks using the Digits record.
24. Naive Bayes Classification with PythonIntroduction into Naive Bayes Classification with Python
25. Naive Bayes Classifier with ScikitIntroduction into Naive Bayes Classification with Python
26. Introduction to Text ClassificationIntroduction into Text Classification with Naive Bayes using the programming language Python.
27. Text Classification in PythonText Classification in Python Using Naive Bayes in the Python Implementation.
28. Natural Language Processing with PythonPython Machine Learning: Natural Language Processing
29. Natural Language Processing: ClassificationPython Machine Learning: Natural Language Processing, Examples
30. Introduction to Regression with PythonIntroduction in Regression using Python and MLPRegressor from Sklearn.
31. Decision Trees in PythonIntroduction into classification with decision trees using Python
32. Regression Trees in PythonIntroduction to Regression using Decision Trees with Python
33. Random Forests in PythonIntroduction to Random Forest classification with Python
34. Boosting Algorithm in PythonThe final chapter of our series on regression tree models in machine learning
35. Principal Component Analysis (PCA) in PythonPrincipal Component Analysis (PCA) - Unsupervised Learning
36. Linear Discriminant Analysis in PythonGives an overview about the idea of Linear Discriminant Analysis using Python.
37. Expectation Maximization and Gaussian Mixture Models (GMM)The Gaussian Mixture Models (GMM) algorithm is an unsupervised learning algorithm since we do not know any values of a target feature.
38. Introduction to TensorFlowMachine Learning: Introduction to TensorFlow