The Supervised Machine Learning Bootcamp

سرفصل های دوره

Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs

1 - Introduction

1 - Introduction

2 - Setting up the Environment

2 - Installing Anaconda

3 - Jupyter Dashboard Part 1

4 - Jupyter Dashboard Part 2

5 - Installing the relevant packages

3 - Naive Bayes

1 - Bayes Thought Experiment.html

2 - Bayes Theorem.html

3 - The HamorSpam Example.html

6 - 365-ML-infographic.pdf

6 - Machine-Learning-with-Naive-Bayes-Course-Notes-365-Data-Science.pdf

6 - Motivation

7 - Bayes Thought Experiment

8 - Bayes Thought Experiment Assignment.html

9 - Bayes Theorem

10 - The HamorSpam Example

11 - The HamorSpam Example Assignment.html

12 - Notebooks-and-Dataset.zip

12 - The YouTube Dataset Creating the Data Frame

13 - CountVectorizer

14 - The YouTube Dataset Preprocessing

15 - The YouTube Dataset Preprocessing Assignment.html

15 - exercise-1.zip

15 - exercise-1-solution.zip

15 - youtube-dataset.zip

16 - The YouTube Dataset Classification

17 - The YouTube Dataset Classification Assignment.html

17 - exercise-2.zip

17 - exercise-2-solution.zip

17 - youtube-dataset.zip

18 - The YouTube Dataset Confusion Matrix

19 - The YouTube Dataset Accuracy Precision Recall and the F1 score

20 - The YouTube Dataset Changing the Priors

21 - 365-ML-infographic.pdf

21 - Machine-Learning-with-Naive-Bayes-Course-Notes-365-Data-Science.pdf

21 - Naïve Bayes Assignment.html

21 - exercise-3.zip

21 - exercise-3-solution.zip

4 - KNearest Neighbors

4 - Motivation.html

5 - Math Prerequisites Distance Metrics.html

22 - 365-ML-infographic.pdf

22 - Machine-Learning-with-K-Nearest-Neighbors-Course-Notes-365-Data-Science.pdf

22 - Motivation

23 - Math Prerequisites Distance Metrics

24 - KNeighborsClassifier-Notebooks.zip

24 - Random Dataset Generating the Dataset

25 - Random Dataset Visualizing the Dataset

26 - Random Dataset Classification

27 - Random Dataset How to Break a Tie

28 - Random Dataset Decision Regions

29 - Random Dataset Choosing the Best Kvalue

30 - Random Dataset Grid Search

31 - Random Dataset Model Performance

32 - KNeighborsClassifier-Exercise.zip

32 - KNeighbors Classifier Assignment.html

33 - KNeighborsRegressor-Notebooks.zip

33 - Theory with a Practical Example

34 - KNN vs Linear Regression A Linear Problem

34 - LinearProblem-Notebooks.zip

35 - KNN vs Linear Regression A Nonlinear Problem

35 - NonLinearProblem-Notebooks.zip

36 - KNeighborsRegressor-Exercise.zip

36 - KNeighbors Regressor Assignment.html

37 - 365-ML-infographic.pdf

37 - Machine-Learning-with-K-Nearest-Neighbors-Course-Notes-365-Data-Science.pdf

37 - Pros and Cons

5 - Decision Trees and Random Forests

38 - Course-Notes-Decision-Trees-and-Random-Forests.pdf

38 - What is a Tree in Computer Science

39 - The Concept of Decision Trees

40 - Decision Trees in Machine Learning

41 - Decision Trees Pros and Cons

42 - Practical Example The Iris Dataset

43 - 5.6.creating-a-decision-tree.zip

43 - Practical Example Creating a Decision Tree

44 - 5.7.full-iris-code.zip

44 - Practical Example Plotting the Tree

45 - Decision Tree Metrics Intuition Gini Inpurity

46 - Decision Tree Metrics Information Gain

47 - Tree Pruning Dealing with Overfitting

48 - Random Forest as Ensemble Learning

49 - Bootstrapping

50 - From Bootstrapping to Random Forests

51 - 5.14.random-forest-code-glass-dataset.zip

51 - Glass-dataset.zip

51 - Random Forest in Code Glass Dataset

52 - 5.15.census-data-preprocessing.zip

52 - Census Data and Income Preprocessing

52 - Census-Income-Dataset.zip

53 - 5.16.census-data-decision-tree.zip

53 - Training the Decision Tree

54 - 5.17.census-data-random-forest.zip

54 - Course-Notes-Decision-Trees-and-Random-Forests.pdf

54 - Training the Random Forest

6 - Support Vector Machines

6 - Intro to SVMs.html

7 - Hard margin problem.html

8 - Kernels.html

9 - Implementing a linear SVM.html

55 - 365-ML-infographic.pdf

55 - Introduction to Support Vector Machines

56 - Linearly separable classes hard margin problem

57 - Nonlinearly separable classes soft margin problem

58 - Kernels Intuition

59 - Intro to the practical case

59 - mushrooms-full-dataset.csv

60 - 3.1-support-vector-machines-classification-notebook.zip

60 - Preprocessing the data

61 - Splitting the data into train and test and rescaling

62 - Implementing a linear SVM

63 - Analyzing the results Confusion Matrix Precision and Recall

64 - Crossvalidation

65 - 3.8-support-vector-machines-classification-complete.zip

65 - Choosing the kernels and C values for crossvalidation

66 - Hyperparameter tuning using GridSearchCV

67 - Support Vector Machines Assignment.html

67 - social.csv

67 - social-purchase-svms-assignment-3.zip

67 - social-purchase-svms-assignment-3-solution.zip

7 - Ridge and Lasso Regression

10 - Ridge Regression Mechanics.html

11 - Lasso Regression Basics.html

12 - Crossvalidation for Choosing a Tuning Parameter.html

68 - 365-ML-infographic.pdf

68 - Regression Analysis Overview

69 - Overfitting and Multicollinearity

70 - Introduction to Regularization

71 - Ridge Regression Basics

72 - Ridge Regression Mechanics

73 - Regularization in More Complicated Scenarios

74 - Lasso Regression Basics

75 - Lasso Regression vs Ridge Regression

76 - Hitters.csv

76 - Hitters-Data-Legend.xlsx

76 - The Hitters Dataset Preprocessing and Preparation

76 - multiple-linear-regression-ridge-lasso-hitters.zip

77 - Exploratory Data Analysis

78 - Performing Linear Regression

79 - Crossvalidation for Choosing a Tuning Parameter

80 - Performing Ridge Regression with Crossvalidation

81 - Performing Lasso Regression with Crossvalidation

82 - Comparing the Results

83 - Replacing the Missing Values in the DataFrame

83 - exercise.zip

83 - exercise-solutions.zip

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تولید کننده: Udemy-Training