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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.
Description
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.
Level
• Intermediate
About Author
A problem solver at heart, 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.
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FILE LIST
Filename
Size
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208 B
01.Course Overview/01.01.Course Overview.mp4
9.9 MB
02.Understanding Approaches to Machine Learning/02.01.Module Overview.mp4
6.5 MB
02.Understanding Approaches to Machine Learning/02.02.Prerequisites and Course Outline.mp4
4.8 MB
02.Understanding Approaches to Machine Learning/02.03.Rule-based vs. ML-based Learning.mp4
13.6 MB
02.Understanding Approaches to Machine Learning/02.04.Traditional ML vs. Representation ML.mp4
7 MB
02.Understanding Approaches to Machine Learning/02.05.The Machine Learning Workflow.mp4
6 MB
02.Understanding Approaches to Machine Learning/02.06.Choosing the Right Model Based on Data.mp4
10.5 MB
02.Understanding Approaches to Machine Learning/02.07.Supervised vs. Unsupervised Learning.mp4
9.3 MB
02.Understanding Approaches to Machine Learning/02.08.Transfer Learning, Cold Start ML and Warm Start ML.mp4
10.1 MB
02.Understanding Approaches to Machine Learning/02.09.Popular Machine Learning Frameworks.mp4
6.5 MB
02.Understanding Approaches to Machine Learning/02.10.Demo Getting Started with scikit-learn.mp4
4.4 MB
02.Understanding Approaches to Machine Learning/02.11.Module Summary.mp4
2.7 MB
03.Understanding and Implementing Regression Models/03.01.Module Overview.mp4
2.4 MB
03.Understanding and Implementing Regression Models/03.02.Building and Evaluating Regression Models.mp4
9.3 MB
03.Understanding and Implementing Regression Models/03.03.Demo Linear Regression Using Numeric Features.mp4
17.6 MB
03.Understanding and Implementing Regression Models/03.04.Demo Exploring Regression Data.mp4
9.7 MB
03.Understanding and Implementing Regression Models/03.05.Demo Preprocessing Numeric and Categorical Data and Fitting a Regression Model.mp4
10.5 MB
03.Understanding and Implementing Regression Models/03.06.Choosing Regression Algorithms.mp4
4.9 MB
03.Understanding and Implementing Regression Models/03.07.Regularized Regression Models Lasso, Ridge, and Elastic Net.mp4
7.4 MB
03.Understanding and Implementing Regression Models/03.08.Stochastic Gradient Descent.mp4
4.4 MB
03.Understanding and Implementing Regression Models/03.09.Demo Multiple Types of Regression.mp4
12 MB
03.Understanding and Implementing Regression Models/03.10.Module Summary.mp4
2.6 MB
04.Understanding and Implementing Classification Models/04.01.Module Overview.mp4
2.3 MB
04.Understanding and Implementing Classification Models/04.02.Types of Classifiers.mp4
8 MB
04.Understanding and Implementing Classification Models/04.03.Understanding Logistic Regression Intuitively.mp4
10 MB
04.Understanding and Implementing Classification Models/04.04.Demo Building and Training a Binary Classification Model.mp4
13.8 MB
04.Understanding and Implementing Classification Models/04.05.Understanding Support Vector and Nearest Neighbors Classification.mp4
7.5 MB
04.Understanding and Implementing Classification Models/04.06.Understanding Decision Tree and Naive Bayes Classification.mp4
9.9 MB
04.Understanding and Implementing Classification Models/04.07.Demo Building Classification Models Using Multiple Techniques.mp4
15.5 MB
04.Understanding and Implementing Classification Models/04.08.Demo Using Warm Start with an Ensemble Classifier.mp4
6.6 MB
04.Understanding and Implementing Classification Models/04.09.Demo Performing Multiclass Classification on Text Data.mp4
14.9 MB
04.Understanding and Implementing Classification Models/04.10.Module Summary.mp4
2 MB
05.Understanding and Implementing Clustering Model/05.01.Module Overview.mp4
2.2 MB
05.Understanding and Implementing Clustering Model/05.02.Clustering as an Unsupervised Learning Technique.mp4
7.6 MB
05.Understanding and Implementing Clustering Model/05.03.Choosing Clustering Algorithms.mp4
7.4 MB
05.Understanding and Implementing Clustering Model/05.04.Categorizing Clustering Algorithms.mp4
5.8 MB
05.Understanding and Implementing Clustering Model/05.05.K-means Clustering.mp4
5 MB
05.Understanding and Implementing Clustering Model/05.06.Hierarchical Clustering.mp4
6.8 MB
05.Understanding and Implementing Clustering Model/05.07.Demo Performing K-means Clustering on Unlabeled Data.mp4
11.9 MB
05.Understanding and Implementing Clustering Model/05.08.Demo Clustering Using Labeled Data.mp4
18.2 MB
05.Understanding and Implementing Clustering Model/05.09.Demo Agglomerative Clustering.mp4
49.2 MB
05.Understanding and Implementing Clustering Model/05.10.Summary and Further Study.mp4
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