The Supervised Machine Learning Bootcamp

The Supervised Machine Learning Bootcamp

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 83 lectures (5h 52m) | 2.76 GB

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

Why should you consider taking the Supervised Machine Learning course?

The supervised machine learning algorithms you will learn here are some of the most powerful data science tools you need to solve regression and classification tasks. These are invaluable skills anyone who wants to work as a machine learning engineer and data scientist should have in their toolkit.

Naïve Bayes, KNNs, Support Vector Machines, Decision Trees, Random Forests, Ridge and Lasso Regression.

In this course, you will learn the theory behind all 6 algorithms, and then apply your skills to practical case studies tailored to each one of them, using Python’s sci-kit learn library.

First, we cover naïve Bayes – a powerful technique based on Bayesian statistics. Its strong point is that it’s great at performing tasks in real-time. Some of the most common use cases are filtering spam e-mails, flagging inappropriate comments on social media, or performing sentiment analysis. In the course, we have a practical example of how exactly that works, so stay tuned!

Next up is K-nearest-neighbors – one of the most widely used machine learning algorithms. Why is that? Because of its simplicity when using distance-based metrics to make accurate predictions.

We’ll follow up with decision tree algorithms, which will serve as the basis for our next topic – namely random forests. They are powerful ensemble learners, capable of harnessing the power of multiple decision trees to make accurate predictions.

After that, we’ll meet Support Vector Machines – classification and regression models, capable of utilizing different kernels to solve a wide variety of problems. In the practical part of this section, we’ll build a model for classifying mushrooms as either poisonous or edible. Exciting!

Finally, you’ll learn about Ridge and Lasso Regression – they are regularization algorithms that improve the linear regression mechanism by limiting the power of individual features and preventing overfitting. We’ll go over the differences and similarities, as well as the pros and cons of both regression techniques.

Each section of this course is organized in a uniform way for an optimal learning experience:

– We start with the fundamental theory for each algorithm. To enhance your understanding of the topic, we’ll walk you through a theoretical case, as well as introduce mathematical formulas behind the algorithm.

– Then, we move on to building a model in order to solve a practical problem with it. This is done using Python’s famous sklearn library.

– We analyze the performance of our models with the aid of metrics such as accuracy, precision, recall, and the F1 score.

– We also study various techniques such as grid search and cross-validation to improve the model’s performance.

To top it all off, we have a range of complementary exercises and quizzes, so that you can enhance your skill set. Not only that, but we also offer comprehensive course materials to guide you through the course, which you can consult at any time.

The lessons have been created in 365’s unique teaching style many of you are familiar with. We aim to deliver complex topics in an easy-to-understand way, focusing on practical application and visual learning.

With the power of animations, quiz questions, exercises, and well-crafted course notes, the Supervised Machine Learning course will fulfill all your learning needs.

If you want to take your data science skills to the next level and add in-demand tools to your resume, this course is the perfect choice for you.

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
Table of Contents

Setting up the Environment
Installing Anaconda
Jupyter Dashboard Part 1
Jupyter Dashboard Part 2
Installing the relevant packages

Naïve Bayes
Bayes Thought Experiment
Bayes Theorem
The HamorSpam Example
Bayes Thought Experiment
Bayes Thought Experiment Assignment
Bayes Theorem
The HamorSpam Example
The HamorSpam Example Assignment
The YouTube Dataset Creating the Data Frame
The YouTube Dataset Preprocessing
The YouTube Dataset Preprocessing Assignment
The YouTube Dataset Classification
The YouTube Dataset Classification Assignment
The YouTube Dataset Confusion Matrix
The YouTube Dataset Accuracy Precision Recall and the F1 score
The YouTube Dataset Changing the Priors
Naïve Bayes Assignment

KNearest Neighbors
Math Prerequisites Distance Metrics
Math Prerequisites Distance Metrics
Random Dataset Generating the Dataset
Random Dataset Visualizing the Dataset
Random Dataset Classification
Random Dataset How to Break a Tie
Random Dataset Decision Regions
Random Dataset Choosing the Best Kvalue
Random Dataset Grid Search
Random Dataset Model Performance
KNeighbors Classifier Assignment
Theory with a Practical Example
KNN vs Linear Regression A Linear Problem
KNN vs Linear Regression A Nonlinear Problem
KNeighbors Regressor Assignment
Pros and Cons

Decision Trees and Random Forests
What is a Tree in Computer Science
The Concept of Decision Trees
Decision Trees in Machine Learning
Decision Trees Pros and Cons
Practical Example The Iris Dataset
Practical Example Creating a Decision Tree
Practical Example Plotting the Tree
Decision Tree Metrics Intuition Gini Inpurity
Decision Tree Metrics Information Gain
Tree Pruning Dealing with Overfitting
Random Forest as Ensemble Learning
From Bootstrapping to Random Forests
Random Forest in Code Glass Dataset
Census Data and Income Preprocessing
Training the Decision Tree
Training the Random Forest

Support Vector Machines
Intro to SVMs
Hard margin problem
Implementing a linear SVM
Introduction to Support Vector Machines
Linearly separable classes hard margin problem
Nonlinearly separable classes soft margin problem
Kernels Intuition
Intro to the practical case
Preprocessing the data
Splitting the data into train and test and rescaling
Implementing a linear SVM
Analyzing the results– Confusion Matrix Precision and Recall
Choosing the kernels and C values for crossvalidation
Hyperparameter tuning using GridSearchCV
Support Vector Machines Assignment

Ridge and Lasso Regression
Ridge Regression Mechanics
Lasso Regression Basics
Crossvalidation for Choosing a Tuning Parameter
Regression Analysis Overview
Overfitting and Multicollinearity
Introduction to Regularization
Ridge Regression Basics
Ridge Regression Mechanics
Regularization in More Complicated Scenarios
Lasso Regression Basics
Lasso Regression vs Ridge Regression
The Hitters Dataset Preprocessing and Preparation
Exploratory Data Analysis
Performing Linear Regression
Crossvalidation for Choosing a Tuning Parameter
Performing Ridge Regression with Crossvalidation
Performing Lasso Regression with Crossvalidation
Comparing the Results
Replacing the Missing Values in the DataFrame