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Introduction to Machine Learning

Get started with fundamental machine learning algorithms using scikit-learn.

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About this course

Why is it important to understand machine learning? Many machine learning techniques can solve interesting problems, from identifying email spam to classifying images. It is important to understand how libraries like scikit-learn work under the hood, to know their strengths and weaknesses, and to determine if they should be used for a given problem.

What you'll learn—and how you can apply it

By the end of this course, you’ll understand:

  • What machine learning is and the various algorithms under its umbrella
  • How regression and classification techniques work, from linear regression to neural networks 
  • The API patterns of scikit-learn

And you'll be able to:

  • Use scikit-learn to make predictions on different types of problems, from email spam to image recognition 
  • Pair the proper application of supervised machine learning algorithms to a given problem and context appropriately
  • Explain how different machine learning algorithms work, including decision trees and neural networks

This training is for you because:

  • You’re a data science professional wanting to learn about ML and how it works.
  • You work with data science teams and want to understand their ML capabilities.
  • You want to become a machine learning or data engineer and want to take the first step in that career path

Prerequisites

Setup 

To follow along using your desktop IDE:

  1. Install or update to the latest version of Anaconda
  2. Launch your command line tool and configure your conda environment

For macOS and Linux users: Search and launch Terminal in your system

For Windows users: Locate and launch Anaconda Prompt in your system

3. (Optional but recommended) From the command line, run the following prompts to create and activate a new environment

conda create --name NEW_ENV_NAME

conda activate NEW_ENV_NAME 

4. Install required packages in the command line

conda install matplotlib pandas seaborn 

5. Launch JupyterLab from the command line

jupyter lab 

To open Anaconda Notebooks:

  1. Go to https://anaconda.cloud
  2. Click on 'Notebooks' from the top navigation menu
  3. Create an account or login if you already have one

Facilitator bio

Thomas Nield is the founder of Nield Consulting Group and Yawman Flight, as well as an instructor at University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. At USC he teaches AI System Safety, developing systematic approaches for identifying AI-related hazards in aviation and ground vehicles. He's authored three books, including Essential Math for Data Science (O’Reilly) and Getting Started with SQL (O'Reilly).

He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles. You can find him on Twitter | LinkedIn | GitHub | YouTube.

Questions? Issues? Join our Community page to get help. 

Curriculum02:23:29

  • Getting Started with Anaconda Notebooks 00:01:02
  • Linear Regression
  • Preview
    Linear Regression: Simple Linear Regression 00:09:45
  • Linear Regression Quiz: Calculate the Sum of Square
  • Linear Regression: Exercise Solution 00:01:01
  • Linear Regression: Finding Coefficients 00:05:26
  • Linear Regression: Scoring and Validation 00:07:24
  • Linear Regression Quiz: Perform a Linear Regression
  • Linear Regression: Exercise Solution 2 00:01:56
  • Logistic Regression
  • Logistic Regression: Classification 00:10:12
  • Logistic Regression: Multiple Logistic Regression 00:05:56
  • Logistic Regression: Accuracy and Confusion Matrices 00:04:01
  • Logistic Regression: Receiver Operator Characteristic (ROC) and Area Under the Curve (AUC) 00:07:52
  • Logistic Regression Quiz: Perform a Logistic Regression
  • Logistic Regression: Exercise Solution 00:02:13
  • Naive Bayes
  • Naive Bayes: Overview 00:12:42
  • Naive Bayes Quiz: Categorize Bank Transaction
  • Naive Bayes: Exercise Solution 00:03:41
  • Decision Trees and Random Forests
  • Decision Trees: Overview 00:08:56
  • Decision Trees: Gini Impurity 00:08:14
  • Decision Trees: Overfitting and Random Forests: Overview 00:07:30
  • Random Forests Quiz: Perform a Random Forest Classification
  • Random Forests: Exercise Solution 00:02:07
  • Neural Networks
  • Neural Networks: Overview 00:09:24
  • Neural Networks: Walkthrough 00:10:22
  • Neural Networks Quiz: Neural Network Classifier
  • Neural Networks: Exercise Solution 00:02:52
  • Neural Networks: Computer Vision 00:09:24
  • Neural Networks: Optimization Concerns 00:07:52
  • Neural Networks Quiz: Perform a Neural Network Prediction on the MNIST Dataset
  • Neural Networks: Exercise Solution 2 00:02:40
  • Conclusion 00:00:57
  • End of Course Survey
  • Course Completion

About this course

Why is it important to understand machine learning? Many machine learning techniques can solve interesting problems, from identifying email spam to classifying images. It is important to understand how libraries like scikit-learn work under the hood, to know their strengths and weaknesses, and to determine if they should be used for a given problem.

What you'll learn—and how you can apply it

By the end of this course, you’ll understand:

  • What machine learning is and the various algorithms under its umbrella
  • How regression and classification techniques work, from linear regression to neural networks 
  • The API patterns of scikit-learn

And you'll be able to:

  • Use scikit-learn to make predictions on different types of problems, from email spam to image recognition 
  • Pair the proper application of supervised machine learning algorithms to a given problem and context appropriately
  • Explain how different machine learning algorithms work, including decision trees and neural networks

This training is for you because:

  • You’re a data science professional wanting to learn about ML and how it works.
  • You work with data science teams and want to understand their ML capabilities.
  • You want to become a machine learning or data engineer and want to take the first step in that career path

Prerequisites

Setup 

To follow along using your desktop IDE:

  1. Install or update to the latest version of Anaconda
  2. Launch your command line tool and configure your conda environment

For macOS and Linux users: Search and launch Terminal in your system

For Windows users: Locate and launch Anaconda Prompt in your system

3. (Optional but recommended) From the command line, run the following prompts to create and activate a new environment

conda create --name NEW_ENV_NAME

conda activate NEW_ENV_NAME 

4. Install required packages in the command line

conda install matplotlib pandas seaborn 

5. Launch JupyterLab from the command line

jupyter lab 

To open Anaconda Notebooks:

  1. Go to https://anaconda.cloud
  2. Click on 'Notebooks' from the top navigation menu
  3. Create an account or login if you already have one

Facilitator bio

Thomas Nield is the founder of Nield Consulting Group and Yawman Flight, as well as an instructor at University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. At USC he teaches AI System Safety, developing systematic approaches for identifying AI-related hazards in aviation and ground vehicles. He's authored three books, including Essential Math for Data Science (O’Reilly) and Getting Started with SQL (O'Reilly).

He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles. You can find him on Twitter | LinkedIn | GitHub | YouTube.

Questions? Issues? Join our Community page to get help. 

Curriculum02:23:29

  • Getting Started with Anaconda Notebooks 00:01:02
  • Linear Regression
  • Preview
    Linear Regression: Simple Linear Regression 00:09:45
  • Linear Regression Quiz: Calculate the Sum of Square
  • Linear Regression: Exercise Solution 00:01:01
  • Linear Regression: Finding Coefficients 00:05:26
  • Linear Regression: Scoring and Validation 00:07:24
  • Linear Regression Quiz: Perform a Linear Regression
  • Linear Regression: Exercise Solution 2 00:01:56
  • Logistic Regression
  • Logistic Regression: Classification 00:10:12
  • Logistic Regression: Multiple Logistic Regression 00:05:56
  • Logistic Regression: Accuracy and Confusion Matrices 00:04:01
  • Logistic Regression: Receiver Operator Characteristic (ROC) and Area Under the Curve (AUC) 00:07:52
  • Logistic Regression Quiz: Perform a Logistic Regression
  • Logistic Regression: Exercise Solution 00:02:13
  • Naive Bayes
  • Naive Bayes: Overview 00:12:42
  • Naive Bayes Quiz: Categorize Bank Transaction
  • Naive Bayes: Exercise Solution 00:03:41
  • Decision Trees and Random Forests
  • Decision Trees: Overview 00:08:56
  • Decision Trees: Gini Impurity 00:08:14
  • Decision Trees: Overfitting and Random Forests: Overview 00:07:30
  • Random Forests Quiz: Perform a Random Forest Classification
  • Random Forests: Exercise Solution 00:02:07
  • Neural Networks
  • Neural Networks: Overview 00:09:24
  • Neural Networks: Walkthrough 00:10:22
  • Neural Networks Quiz: Neural Network Classifier
  • Neural Networks: Exercise Solution 00:02:52
  • Neural Networks: Computer Vision 00:09:24
  • Neural Networks: Optimization Concerns 00:07:52
  • Neural Networks Quiz: Perform a Neural Network Prediction on the MNIST Dataset
  • Neural Networks: Exercise Solution 2 00:02:40
  • Conclusion 00:00:57
  • End of Course Survey
  • Course Completion