# The Supervised Machine Learning Bootcamp

Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs 4.4/5

## The Supervised Machine Learning Bootcamp

### What you’ll learn

Regression and Classification Algorithms
Using sk-learn and Python to implement supervised machine learning techniques
K-nearest neighbors for both classification and regression
Naïve Bayes
Ridge and Lasso Regression
Decision Trees
Random Forests
Support Vector Machines
Practical case studies for training, testing and evaluating and improving model performance
Cross-validation for parameter optimization
Learn to use metrics such as Precision, Recall, F1-score, as well as a confusion matrix to evaluate true model performance
You will dive into the theoretical foundation behind each algorithm with the aid of intuitive explanation of formulas and mathematical notions ### Requirements

The course is open to everyone who wants to learn data science.
You’ll need to install Anaconda and Jupyter Notebook. We will show you how to do that step by step.

### Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Setting up the Environment

Lecture 2 Installing Anaconda

Lecture 3 Jupyter Dashboard – Part 1

Lecture 4 Jupyter Dashboard – Part 2

Lecture 5 Installing the relevant packages

Section 3: Naïve Bayes

Lecture 6 Motivation

Lecture 7 Bayes’ Thought Experiment

Lecture 8 Bayes’ Thought Experiment: Assignment

Lecture 9 Bayes’ Theorem

Lecture 10 The Ham-or-Spam Example

Lecture 11 The Ham-or-Spam Example: Assignment

Lecture 12 The YouTube Dataset: Creating the Data Frame

Lecture 13 CountVectorizer

Lecture 14 The YouTube Dataset: Preprocessing

Lecture 15 The YouTube Dataset: Preprocessing: Assignment

Lecture 16 The YouTube Dataset: Classification

Lecture 17 The YouTube Dataset: Classification: Assignment

Lecture 18 The YouTube Dataset: Confusion Matrix

Lecture 19 The YouTube Dataset: Accuracy, Precision, Recall, and the F1 score

Lecture 20 The YouTube Dataset: Changing the Priors

Lecture 21 Naïve Bayes: Assignment

Section 4: K-Nearest Neighbors

Lecture 22 Motivation

Lecture 23 Math Prerequisites: Distance Metrics

Lecture 24 Random Dataset: Generating the Dataset

Lecture 25 Random Dataset: Visualizing the Dataset

Lecture 26 Random Dataset: Classification

Lecture 27 Random Dataset: How to Break a Tie

Lecture 28 Random Dataset: Decision Regions

Lecture 29 Random Dataset: Choosing the Best K-value

Lecture 30 Random Dataset: Grid Search

Lecture 31 Random Dataset: Model Performance

Lecture 32 KNeighbors Classifier: Assignment

Lecture 33 Theory with a Practical Example

Lecture 34 KNN vs Linear Regression: A Linear Problem

Lecture 35 KNN vs Linear Regression: A Non-linear Problem

Lecture 36 KNeighbors Regressor: Assignment

Lecture 37 Pros and Cons

Section 5: Decision Trees and Random Forests

Lecture 38 What is a Tree in Computer Science?

Lecture 39 The Concept of Decision Trees

Lecture 40 Decision Trees in Machine Learning

Lecture 41 Decision Trees: Pros and Cons

Lecture 42 Practical Example: The Iris Dataset

Lecture 43 Practical Example: Creating a Decision Tree

Lecture 44 Practical Example: Plotting the Tree

Lecture 45 Decision Tree Metrics Intuition: Gini Inpurity

Lecture 46 Decision Tree Metrics: Information Gain

Lecture 47 Tree Pruning: Dealing with Overfitting

Lecture 48 Random Forest as Ensemble Learning

Lecture 49 Bootstrapping

Lecture 50 From Bootstrapping to Random Forests

Lecture 51 Random Forest in Code – Glass Dataset

Lecture 52 Census Data and Income – Preprocessing

Lecture 53 Training the Decision Tree

Lecture 54 Training the Random Forest

Section 6: Support Vector Machines

Lecture 55 Introduction to Support Vector Machines

Lecture 56 Linearly separable classes – hard margin problem

Lecture 57 Non-linearly separable classes – soft margin problem

Lecture 58 Kernels – Intuition

Lecture 59 Intro to the practical case

Lecture 60 Preprocessing the data

Lecture 61 Splitting the data into train and test and rescaling

Lecture 62 Implementing a linear SVM

Lecture 63 Analyzing the results– Confusion Matrix, Precision, and Recall

Lecture 64 Cross-validation

Lecture 65 Choosing the kernels and C values for cross-validation

Lecture 66 Hyperparameter tuning using GridSearchCV

Lecture 67 Support Vector Machines – Assignment

Section 7: Ridge and Lasso Regression

Lecture 68 Regression Analysis Overview

Lecture 69 Overfitting and Multicollinearity

Lecture 70 Introduction to Regularization

Lecture 71 Ridge Regression Basics

Lecture 72 Ridge Regression Mechanics

Lecture 73 Regularization in More Complicated Scenarios

Lecture 74 Lasso Regression Basics

Lecture 75 Lasso Regression vs Ridge Regression

Lecture 76 The Hitters Dataset: Preprocessing and Preparation

Lecture 77 Exploratory Data Analysis

Lecture 78 Performing Linear Regression

Lecture 79 Cross-validation for Choosing a Tuning Parameter

Lecture 80 Performing Ridge Regression with Cross-validation

Lecture 81 Performing Lasso Regression with Cross-validation

Lecture 82 Comparing the Results

Lecture 83 Replacing the Missing Values in the DataFrame

Aspiring data scientists and machine learning engineers,Data Scientists and Data Analysts looking to up their skillset,Anyone who wants to gain an understanding of the machine learning field and its vast opportunities

#### Course Information:

Udemy | English | 5h 48m | 2.38 GB
Created by: 365 Careers

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