python machine learning

python machine learning algorithm basics

• Chapter 1：Introduction to machine learning background knowledge (32 minutes and 26 seconds)

• Chapter 2：python installation (10 minutes and 24 seconds)

• Chapter 3：Basic concepts of machine learning (26 minutes and 37 seconds)

• Chapter 4：Univariate linear regression (19 minutes and 13 seconds)

• Chapter 5：cost function (35 minutes and 10 seconds)

• Chapter 6：gradient descent method (30 minutes and 48 seconds)

• Chapter 7：Practical gradient descent method for linear regression (27 minutes and 52 seconds)

• Chapter 8：Practical sklearn linear regression (8 minutes and 48 seconds)

• Chapter 9：Linear Algebra Review (11 minutes and 51 seconds)

• Chapter 10：multiple linear regression (7 minutes and 37 seconds)

• Chapter 11：Practical gradient descent method multiple linear regression (21 minutes and 22 seconds)

• Chapter 12：Practical sklearn multiple linear regression (4 minutes and 15 seconds)

• Chapter 13：Practical sklearn polynomial regression (20 minutes and 38 seconds)

• Chapter 14：standard equation method (21 minutes and 48 seconds)

• Chapter 15：Practical Standard Equation Method (11 minutes and 42 seconds)

• Chapter 16：Feature scaling, cross-validation method (13 minutes and 12 seconds)

• Chapter 17：Overfitting, regularization (12 minutes and 44 seconds)

• Chapter 18：ridge regression (18 minutes and 12 seconds)

• Chapter 19：Practical sklearn ridge regression (16 minutes and 26 seconds)

• Chapter 20：Practical standard equation normal ridge regression (9 minutes and 7 seconds)

• Chapter 21：LASSO algorithm (11 minutes and 52 seconds)

• Chapter 22：Practical sklearn elastic net (7 minutes and 54 seconds)

• Chapter 23：logistic regression (26 minutes and 51 seconds)

• Chapter 24：Precision rate, recall rate, F1 index (8 minutes and 27 seconds)

• Chapter 25：Practical Gradient Descent Logistic Regression (40 minutes and 42 seconds)

• Chapter 26：Practical sklearn logistic regression (5 minutes and 45 seconds)

• Chapter 27：Practical gradient descent method nonlinear logistic regression (16 minutes and 37 seconds)

• Chapter 28：Practical sklearn nonlinear logistic regression (7 minutes and 27 seconds)

• Chapter 29：Neural Networks Background Overview (17 minutes and 28 seconds)

• Chapter 30：Introduction to single layer perceptron (15 minutes and 53 seconds)

• Chapter 31：Practice single layer perceptron program (15 minutes and 35 seconds)

• Chapter 32：Practice the single-layer perceptron XOR problem (4 minutes and 24 seconds)

• Chapter 33：Practice linear neural networks, delta learning rules (15 minutes and 47 seconds)

• Chapter 34：Practice linear neural network to solve XOR problem (21 minutes and 49 seconds)

• Chapter 35：Introduction to BP Neural Network (20 minutes and 12 seconds)

• Chapter 36：BP algorithm derivation (19 minutes and 3 seconds)

• Chapter 37：Practice the XOR problem of BP neural network (20 minutes and 44 seconds)

• Chapter 38：In-depth understanding of BP neural network paper explanation (28 minutes and 40 seconds)

• Chapter 39：Introduction to Google Neural Network Demonstration Platform (23 minutes and 33 seconds)

• Chapter 40：Introduction to KNN algorithm (12 minutes and 20 seconds)

• Chapter 41：Practice KNN algorithm implementation (15 minutes and 20 seconds)

• Chapter 42：Practice using KNN to complete Iris data set classification (12 minutes and 2 seconds)

• Chapter 43：Decision tree information entropy, ID3, C4 5 algorithm introduction (25 minutes and 50 seconds)

• Chapter 44：Practical decision tree example (19 minutes and 11 seconds)

• Chapter 45：Practice decision tree drawing (16 minutes and 1 seconds)

• Chapter 46：Decision tree CART algorithm (19 minutes and 3 seconds)

• Chapter 47：Practical Decision Tree CART Algorithm (6 minutes and 22 seconds)

• Chapter 48：Practical decision tree linear binary classification (8 minutes and 56 seconds)

• Chapter 49：Practical decision tree nonlinear binary classification (12 minutes and 8 seconds)

• Chapter 50：Practical regression tree (8 minutes and 22 seconds)

• Chapter 51：Practical introduction and use of Bagging (24 minutes and 42 seconds)

• Chapter 52：Introduction and use of random forest RF in practice (7 minutes and 56 seconds)

• Chapter 53：Practical introduction and use of Adaboost (24 minutes and 5 seconds)

• Chapter 54：Practice the introduction and use of Stacking and Voting (10 minutes and 50 seconds)

• Chapter 55：Bayesian Algorithm Background (14 minutes and 48 seconds)

• Chapter 56：Introduction to Bayesian Algorithm (17 minutes and 43 seconds)

• Chapter 57：Practicing Bayesian iris (3 minutes and 33 seconds)

• Chapter 58：Introduction to the bag-of-words model in practice (17 minutes and 28 seconds)

• Chapter 59：Introduction to Practical TF IDF Algorithm (13 minutes and 45 seconds)

• Chapter 60：Introduction to K MEANS algorithm (20 minutes and 47 seconds)

• Chapter 61：Practice python to implement K MEANS algorithm (29 minutes and 45 seconds)

• Chapter 62：Practice sklearn K MEANS (4 minutes and 20 seconds)

• Chapter 63：Practice sklearn Mini Batch K Means (4 minutes and 12 seconds)

• Chapter 64：4 problems with K MEANS algorithm (5 minutes and 21 seconds)

• Chapter 65：Practice K MEANS cost function application (8 minutes and 11 seconds)

• Chapter 66：Practice the K MEANS Elbow Rule (7 minutes and 44 seconds)

• Chapter 67：K MEANS algorithm visualization (5 minutes and 43 seconds)

• Chapter 68：DBSCAN algorithm explanation (10 minutes and 15 seconds)

• Chapter 69：DBSCAN algorithm visualization (12 minutes and 53 seconds)

• Chapter 70：Practice sklearn DBSCAN algorithm (11 minutes and 35 seconds)

• Chapter 71：PCA algorithm explanation (17 minutes and 54 seconds)

• Chapter 72：Practice python to implement PCA dimensionality reduction (8 minutes and 22 seconds)

• Chapter 73：Introduction to SVM (17 minutes and 38 seconds)

• Chapter 74：Simple example of practicing SVM (5 minutes and 50 seconds)

• Chapter 75：SVM algorithm derivation (26 minutes and 24 seconds)

• Chapter 76：Slack variables and penalty functions (11 minutes and 2 seconds)

• Chapter 77：SVM simple example (10 minutes and 27 seconds)

• Chapter 78：SVM maps low dimension to high dimension (6 minutes and 55 seconds)

• Chapter 79：kernel function (9 minutes and 47 seconds)