Machine Learning With Python

Welcome

Hello, and welcome to Machine Learning with Python. In this course, you’ll learn how Machine Learning is used in many key fields and industries. For example, in the health care industry, data scientists use Machine Learning to predict whether a human cell that is believed to be at risk of developing cancer, is either benign or malignant. As such, Machine learning can play a key role in determining a person’s health and welfare. You’ll also learn about the value of decision trees and how building a good decision tree from historical data helps doctors to prescribe the proper medicine for each of their patients. You’ll learn how bankers use machine learning to make decisions on whether to approve loan applications. And you will learn how to use machine learning to do bank customer segmentation, where it is not usually easy to run for huge volumes of varied data. In this course, you’ll see how machine learning helps websites such as YouTube, Amazon, or Netflix develop recommendations to their customers about various products or services, such as which movies they might be interested in going to see or which books to buy. There is so much that you can do with Machine Learning! Here, you’ll learn how to use popular python libraries to build your model. For example, given an automobile dataset, we use the sci-kit learn (sklearn) library to estimate the Co2 emission of cars using their Engine size or Cylinders. We can even predict what the Co2 emissions will be for a car that hasn’t even been produced yet! And we’ll see how the telecommunications industry can predict customer churn. You can run and practice the code of all these samples using the built-in lab environment in this course. You don’t have to install anything to your computer or do anything on the cloud. All you have to do is click a button to start the lab environment in your browser. The code for the samples is already written using python language, in Jupyter notebooks, and you can run it to see the results, or change it to understand the algorithms better. So, what will you be able to achieve by taking this course? Well, by putting in just a few hours a week over the next few weeks, you’ll get new skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy. You’ll also get new projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. You’ll also get a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. So let’s get started.

Learning Objectives

In this course you will learn about:

• How Statistical Modeling relates to Machine Learning and do a comparison of each.
• Real-life examples of Machine learning and how it affects society in ways you may not have guessed!
• In the labs: Use Python libraries for Machine Learning, such as scikit-learn.

Explore many algorithms and models:

• Popular algorithms: Regression, Classification, and Clustering
• Recommender Systems: Content-Based and Collaborative Filtering
• Popular models: Train/Test Split, Gradient Descent, and Mean Squared Error
• Get ready to do more learning than your machine!

Syllabus

Module 1 - Machine Learning

• Python for Machine Learning
• Supervised vs Unsupervised
• Lab & Review

Module 2 - Regression

• Simple Linear Regression
• Multiple Linear Regression
• Model Evaluation in Regression Models
• Non-Linear Regression
• Lab & Review

Module 3 - Classification

• K-Nearest Neighbors
• Decision Trees
• Evaluation Metrics in Classification
• Logistic Regression vs Linear Regressin
• Support Vector Machine (SVM)
• Lab & Review

Module 4 - Clustering

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Lab & Review

Module 5 - Recommender Systems

• Content-Based Recommender Systems
• Collaborative Filtering
• Lab & Review

Final Exam

Learning Objectives

In this lesson you will learn about:

• Machine Learning applications
• Python libraries for Machine Learning
• Supervised vs Unsupervised Learning

Module 1

Introduction to machine learning

What is machine learning

How machine learning works ?

Examples of machine learning

Major machine learning techniques

• Regression/Estimation
  • Predicting continuous values
• Classification
  • Predicting the item class / category a case
• Clustering
  • Finding the structure of data; summarization
• Association
  • Association frequent co-occuring items / events
• Anomaly detection
• Discovering abnormal and unusuals cases
• Sequence mining
  • Predicting next events; click-stream (Markov Model, HMM)
• Dimension Reduction
  • Reducing the size of data (PCA)
• Recommendation systems
  • Recommending items

Difference between artificial intelligence, machine learning and deep learning

Let's get started with machine learning!

Python for Machine Learning

Python libraries for machine learning

• Numpy
• Scipy
• matplotlib
• pandas
• sklearn

More about scikit-learn

Scikit-learn functions

Supervised vs Unsupervised

What is supervised learning

Teaching the model with labeled data

Type of supervised learning

What is classification ?

What is regression ?

What is unsupervised learning

What is clustering ?

Supervised vs unsupervised learning

Module 2

Learning Objectives

In this lesson you will learn about:

• Regression Algorithms
• Model Evaluation
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models
• Simple Linear Regression

Introduction to Regression

What is regression ?

What is a regression model ?

Types of regression

Application of regression

Regression algorithms

Simple Linear Regression

Using linear regression to predict continuous values

Linear regression topology

How does linear regression works ?

Linear regression model representation

How to find the best fit ?

Estimating the parameters

Predictions with linear regression

Pros of linear regression

Data Source Lab 2

https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64

Multiple Linear Regression

Examples of multiple linear regression

Predicting continuous values with multiple linear regression

Using MSE to expose the errors in the model

Estimating multiple linear regression parameters

Making prediction with multiple linear regression

A&A - on multiple linear regression

Model Evaluation in Regression Model

Model evaluation approaches

Best approach for most accurate result ?

Calculating the accuracy of a model

Train and test on the same dataset

What is training & out-of-sample accuracy ?

Train/Test split evaluation approch

How to use K-fold cross-validation ?

Evaluation Metrics in Regression

Regression accuracy

What is an error of the model ?

Non-Linear Regression

Should we use linear regression ?

Different types of regression

What is polynomial regression ?

What is non-polynomial regression ?

Linear vs non-linear regression

Module 3

Learning Objectives

In this lesson you will learn about:

• K-Nearest Neighbors
• Decision Trees
• Support Vector Machines
• Logistic Regression

Intro to Classification

What is classification ?

How does classification work ?

Example of multi-class classification

Classification use cases

Classification applications

Classification algorithms in machine learning

K-Nearest Neighbors

Intro to KNN

Determining the class unsing 1^st KNN

Determining the class unsing 5 KNNs

What is K-Nearest Neighbor ( or KNN ) ?

#### The K-Nearest Neighbors algorithm

Calculating the similarity / distance in

1 -dimensional space

multi-dimensional space

What is the best value of K for KNN ?

Computing continuous target using KNN

Evaluation Metrics in Classification

Classification accuracy

Jaccard index

F1-score

Log Loss

Intro to Decision Trees

What is decision tree ?

How to build a decision tree ?

How to build a decision tree ?

Building a decision tree with the training set

• Each internal node corresponds to a test
• Each branch corresponds to a result of the test
• Each leaf node assigns a classification

Decision tree learning algorithm

Building Decision Trees

How to build decision tree

Which attribute is the best attribute ?

Entropy

With attribute is the best one to use ?

Is 'Cholesterol' the best attribute ?

What about 'Sexe' ?

Which attribute is the best ?

What is information gain ?

Calculating information

Correct way to build a decision tree

Intro to Logistic Regression

What is logistic regression ?

Logistic regression applications

When is logistic regression suitables ?

Building a model for customer churn

Logistic Regression vs Linear Regression

Predicting customer income

Predicting churn using linear regression

Linear regression classification problems ?

The problem with using linear regression

Sigmoid function in logistic regression

Clarification customer churn model

The training process

Logistic Regression - Training

General cost function

Plotting cost function of the model

Logistic regression cost function

Minimizing the cost function of the model

Using gradient descent to minimizing the cost

Training algorithm recap

Support Vector Machines

Classification with SVM

What is SVM ?

Data transformation

Using SVM to find the hyperplane

Pros and const of SVM

SVM applications

Module 4

Learning Objectives

In this lesson you will learn about:

• K-Means Clustering plus Advantages & Disadvantages
• Hierarchical Clustering plus Advantages & Disadvantages
• Measuring the Distances Between Clusters - Single Linkage Clustering
• Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
• Density-Based Clustering

Intro to Clustering

Clustering for segmentation

What is clustering ?

Clustering vs classification

Clustering application

Why clustering ?

Clustering algorithms

K-Means Clustering

What is K-means clustering ?

K-means algorithms

Determine the simalarity or dissimilarity

1-dimensional similarity / distance

2-dimensional similarity / distance

Multi-dimensional similarity / distance

How does K-means clustering work ?

K-Means clustering - initialize K

![k-means initialize](images/k-means-initialize.png

K-Means clustering - calculate the distance

K-Means clustering - asign to centroid

K-Means clustering - compute new centroids

K-Means clustering - repeat

More on K-Means

K-Means clustering algorithm

K-Means accuracy

Choosing k

K-Means recap

Hierarchical Clustering

Hierarchical clustering

Agglomerative clustering

Hierarchical clustering

More on Hierarchical Clustering

Agglomerative algorithm

Similarity / Distance

How can calculate Distance

Distance between clusters

Adventages vs. disadventages

Hierarchical clustering vs. K-means

DBSCAN Clustering

Density-based clustering

K-means vs. density-based clustering

DBSCAN for class identification

WHat is DBSCAN ?

How DBSCAN works

DBSCAN algorithm - core point ?

DBSCAN algorithm - border point ?

DBSCAN algorithm - outliers ?

DBSCAN algorithm - clusters ?

Adventage of DBSCAN

Module 5

Learning Objectives

In this lesson you will learn about:

• To understand the purpuse and mechanizem of recommendation systems.
• To understand different types of recommander systems.
• To implement recommender system on a real dataset.

Intro to Recommender Systems

What are recommender systems ?

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Applications

Advantage of recommender systems

Two types of recommender systems

Implementing recommender systems

Content-based recommender systems

Weighing the genres

Candidate movies for recommendation

Finding the recommendation

Comme back to recommended

Collaborative Filtering

User-based collaborative filtering

User ratings matrix

Learning the similarity weights

Creating the weighted ratings matrix

Collaborative filtering

Challenges of collaborative filtering