Designing a Machine Learning Model
This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
What you'll learn
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.
In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it.
First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated.
Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks.
When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 2m
- A Case Study: Sentiment Analysis 7m
- Sentiment Analysis as a Binary Classification Problem 2m
- Rule Based vs. ML Based Analysis 7m
- Traditional Machine Learning Systems 4m
- Representation Machine Learning Systems 2m
- Deep Learning and Neural Networks 5m
- Traditional ML vs. Deep Learning 3m
- Traditional ML Algorithms and Neural Network Design 5m
- Module Summary 1m
- Module Overview 2m
- Regression Models 2m
- Choosing Regression Algorithms 4m
- Evaluating Regression Models 5m
- Types of Classification 4m
- Choosing Classification Algorithms 3m
- Evaluating Classifiers 4m
- Clustering Models 5m
- The Curse of Dimensionality 5m
- Dimensionality Reduction Techniques 3m
- Module Summary 1m
- Module Overview 1m
- Install and Set Up 2m
- Exploring the Regression Dataset 3m
- Simple Regression Using Analytical and Machine Learning Techniques 5m
- Multiple Regression Using Analytical and Machine Learning Techniques 2m
- Exploring the Classification Dataset 3m
- Classification Using Logistic Regression 4m
- Classification Using Decision Trees 3m
- Clustering Using K-means 7m
- Dimensionality Reduction Using Principal Component Analysis 4m
- Dimensionality Reduction Using Manifold Learning 5m
- Module Summary 1m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providin... more
