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!
We will cover in this tutorial on Machine Learning and Python the following topics amongst others:
- k-nearest Neighbor Classifier
- Neural networks
- Neural Networks from Scratch in Python
- Neural Network in Python using Numypy
- Dropout Neural Networks
- Neural Networks with Scikit
- Machine Learning with Scikit and Python
- Naive Bayes Classifier
- Introduction into Text Classification using Naive Bayes and Python
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
| Page | Description |
|---|---|
| 1. Machine Learning with Python | Introduction in Machine Learning and Python |
| 2. Machine Learning Terminology | Machine Learning related Terminology used in this Tutorial |
| 3. Evaluation Metrics | Python Machine Learning: Difference between Accuracy and precision, recall, F1-Score |
| 4. Data Representation and Visualization of Data | Data representation and visualization in Machine Learning with Python |
| 5. Available Data Sets in Sklearn | Overview and presentation of the available datasets in sklearn |
| 6. Artificial Datasets with Scikit-Learn | Creating Synthetical datasets for Machine Learning usage with and without Scikit-Learn (sklearn) |
| 7. Train and Test Sets by Splitting Learn and Test Data | Data Sets in Machine Learning, splitting them in learn and test sets with Python |
| 8. k-Nearest Neighbor Classifier in Python | Introduction into k-nearest neighbor classifiers with Python |
| 9. k-Nearest-Neighbor Classifier with sklearn | k-nearest neighbor classifiers with Python and sklearn |
| 10. Neural Networks Introduction | Tutorial on Neural Networks with Python |
| 11. Separating Classes with Dividing Lines | Showing the connection between dividing lines (decision boundaries) and neural networks with a single perceptron using Python |
| 12. A Simple Neural Network from Scratch in Python | Examining simple neural networks with one perceptron. Pointing out the limits by using Python programs. |
| 13. Perceptron class in sklearn | Introduction in the Perceptron Class of sklearn with some examples. |
| 14. Neural Networks, Structure, Weights and Matrices | Introduction into the structure of a Neural Network, explaining the weights and the usage Matrices with Python |
| 15. Running a Neural Network with Python | Get a neural network running in Python |
| 16. Backpropagation in Neural Networks | Neural Network: simple introduction into backpropagation and gradual descent |
| 17. Training a Neural Network with Python | Understanding how the learning / training of a Neural Network written in Python works. |
| 18. Softmax as Activation Function | Explaining the softmax function and using it in a neural network as an activation function |
| 19. Confusion Matrix in Machine Learning | Confusion Matrix in Machine Learning and Python. Definition and Examples with Python Code |
| 20. Training and Testing with MNIST | Neural Network: using and testing with MNIST data set |
| 21. Dropout Neural Networks in Python | Neural Network: using and testing with MNIST data set |
| 22. Neural Networks with Scikit | Tutorial on Neural Networks with Python and Scikit |
| 23. A Neural Network for the Digits Dataset | A continuous Machine Learning example of the classification with neuronal networks using the Digits record. |
| 24. Naive Bayes Classification with Python | Introduction into Naive Bayes Classification with Python |
| 25. Naive Bayes Classifier with Scikit | Introduction into Naive Bayes Classification with Python |
| 26. Introduction to Text Classification | Introduction into Text Classification with Naive Bayes using the programming language Python. |
| 27. Text Classification in Python | Text Classification in Python Using Naive Bayes in the Python Implementation. |
| 28. Natural Language Processing with Python | Python Machine Learning: Natural Language Processing |
| 29. Natural Language Processing: Classification | Python Machine Learning: Natural Language Processing, Examples |
| 30. Introduction to Regression with Python | Introduction in Regression using Python and MLPRegressor from Sklearn. |
| 31. Decision Trees in Python | Introduction into classification with decision trees using Python |
| 32. Regression Trees in Python | Introduction to Regression using Decision Trees with Python |
| 33. Random Forests in Python | Introduction to Random Forest classification with Python |
| 34. Boosting Algorithm in Python | The final chapter of our series on regression tree models in machine learning |
| 35. Principal Component Analysis (PCA) in Python | Principal Component Analysis (PCA) - Unsupervised Learning |
| 36. Linear Discriminant Analysis in Python | Gives 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 TensorFlow | Machine Learning: Introduction to TensorFlow |