Machine Learning with Python: A Mathematical Perspective

سرفصل های دوره

Classification, Clustering, Regression Analysis

1. Machine Learning Training Simple Machine Learning Algorithms for Classification

1. Introduction and Configuration

2. The three different types of machine learning

3. Supervised Machine Learning Classification and Regression

4. Unsupervised Machine Learning Reinforcement Learning

5. Introduction to the basic terminology, notations and roadmap

6. Training Simple Machine Learning Algorithms for Classification

7. Implementing a perception learning algorithm in Python

8. Implementing a perceptron learning algorithm in Python

9. Training a perceptron model on the Iris dataset

10. Perceptron Training Prediction

11. Perceptron Decision Boundaries

12. Adaptive linear neurons and the convergence of learning

13. Adaptive linear neurons and the convergence of learning

14.zip

2. A Tour of Machine Learning Classifiers Using scikit-learn

1. First steps with scikit-learn training a perceptron

2. Modeling class probabilities via logistic regression

3. Maximum margin classification with support vector machines

4. Solving nonlinear problems using a kernel SVM

5. Decision tree learning

6. K-nearest neighbors a lazy learning algorithm

7.zip

3. Regression Analysis

1. Predicting Continuous Target Variables with Regression Analysis

2. Exploring the Housing dataset

3. Visualizing the important characteristics of a dataset

4. Implementing an ordinary least squares linear regression model

5. Estimating the coefficient of a regression model via scikit-learn

6. Fitting a robust regression model using RANSAC

7. Evaluating the performance of linear regression models

8. Using regularized methods for regression

9. Turning a linear regression model into a curve polynomial regression

10.zip

4. Dealing with nonlinear relationships Working with Unlabeled Data

1. Dealing with nonlinear relationships using random forests

2. Working with Unlabeled Data Clustering Analysis

3. Organizing clusters as a hierarchical tree

4. Locating regions of high density via DBSCAN

5.zip

5. Multilayer Artificial Neural Network and Deep Learning

1. Modeling complex functions with artificial neural networks

2. Classifying handwritten digits

3. Training an artificial neural network

4. About the convergence in neural networks

5. Parallelizing Neural Network Training with TensorFlow

6.zip

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تولید کننده: Udemy