Machine learning is the basis for most modern artificial intelligence solutions. A familiarity with the core concepts on which machine learning is based is an important foundation for understanding AI.
After completing this module, you will be able to:
- Describe core concepts of machine learning
- Identify different types of machine learning
- Describe considerations for training and evaluating machine learning models
- Describe core concepts of deep learning
Data exploration and analysis is at the core of data science. Data scientists require skills in programming languages like Python to explore, visualize, and manipulate data.
In this module, you'll learn:
- Common data exploration and analysis tasks.
- How to use Python packages like NumPy, Pandas, and Matplotlib to analyze data.
Regression is a commonly used kind of machine learning for predicting numeric values.
In this module, you'll learn:
- When to use regression models.
- How to train and evaluate regression models using the Scikit-Learn framework.
Train and evaluate classification models
In this module, you'll learn:
- When to use classification
- How to train and evaluate a classification model using the Scikit-Learn framework
Clustering is a type of machine learning that is used to group similar items into clusters.
In this module, you'll learn:
- When to use clustering
- How to train and evaluate a clustering model using the scikit-learn framework
Train and evaluate deep learning models
In this module, you will learn:
- Basic principles of deep learning
- How a deep neural network (DNN) works
- How a convolutional neural network (CNN) works and when to use one
- How transfer learning works and when to use it

Syllabus

Introduction to machine learning concepts
- Introduction
- Machine learning models
- Types of machine learning model
- Regression
- Binary classification
- Multiclass classification
- Clustering
- Deep learning
- Exercise - Explore machine learning scenarios
- Module assessment
- Summary
Explore and analyze data with Python
- Introduction
- Explore data with NumPy and Pandas
- Exercise - Explore data with NumPy and Pandas
- Visualize data
- Exercise - Visualize data with Matplotlib
- Examine real world data
- Exercise - Examine real world data
- Module assessment
- Summary
Train and evaluate regression models
- Introduction
- What is regression?
- Exercise - Train and evaluate a regression model
- Discover new regression models
- Exercise - Experiment with more powerful regression models
- Improve models with hyperparameters
- Exercise - Optimize and save models
- Module assessment
- Summary
Train and evaluate classification models
- Introduction
- What is classification?
- Exercise - Train and evaluate a classification model
- Evaluate classification models
- Exercise - Perform classification with alternative metrics
- Create multiclass classification models
- Exercise - Train and evaluate multiclass classification models
- Module assessment
- Summary
Train and evaluate clustering models
- Introduction
- What is clustering?
- Exercise - Train and evaluate a clustering model
- Evaluate different types of clustering
- Exercise - Train and evaluate advanced clustering models
- Module assessment
- Summary
Train and evaluate deep learning models
- Introduction
- Deep neural network concepts
- Convolutional neural networks
- Transfer learning
- Exercise - Train a deep neural network
- Module assessment
- Summary