Creating Machine Learning Models
This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.
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, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance.
First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other.
Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You will gain an intuitive understanding of the the model algorithms you can use for classification and regression. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms, and validating the results.
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 and evaluation techniques for your use-case.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Rule-based vs. ML-based Learning 8m
- Traditional ML vs. Representation ML 4m
- The Machine Learning Workflow 3m
- Choosing the Right Model Based on Data 6m
- Supervised vs. Unsupervised Learning 5m
- Transfer Learning, Cold Start ML and Warm Start ML 5m
- Popular Machine Learning Frameworks 4m
- Demo: Getting Started with scikit-learn 2m
- Module Summary 2m
- Module Overview 1m
- Building and Evaluating Regression Models 5m
- Demo: Linear Regression Using Numeric Features 7m
- Demo: Exploring Regression Data 4m
- Demo: Preprocessing Numeric and Categorical Data and Fitting a Regression Model 4m
- Choosing Regression Algorithms 3m
- Regularized Regression Models: Lasso, Ridge, and Elastic Net 4m
- Stochastic Gradient Descent 3m
- Demo: Multiple Types of Regression 5m
- Module Summary 1m
- Module Overview 1m
- Types of Classifiers 4m
- Understanding Logistic Regression Intuitively 5m
- Demo: Building and Training a Binary Classification Model 5m
- Understanding Support Vector and Nearest Neighbors Classification 4m
- Understanding Decision Tree and Naive Bayes Classification 6m
- Demo: Building Classification Models Using Multiple Techniques 7m
- Demo: Using Warm Start with an Ensemble Classifier 3m
- Demo: Performing Multiclass Classification on Text Data 6m
- Module Summary 1m
- Module Overview 1m
- Clustering as an Unsupervised Learning Technique 3m
- Choosing Clustering Algorithms 4m
- Categorizing Clustering Algorithms 3m
- K-means Clustering 3m
- Hierarchical Clustering 4m
- Demo: Performing K-means Clustering on Unlabeled Data 5m
- Demo: Clustering Using Labeled Data 8m
- Demo: Agglomerative Clustering 8m
- Summary and Further Study 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 providing ... more