How to Think About Machine Learning Algorithms

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Swetha Kolalapudi

3:08:38

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1. Course Overview

1. Course Overview.mp4

01:45

2. Introducing Machine Learning

1. Recognizing Machine Learning Applications.mp4

05:54

2. Knowing When to Use Machine Learning.mp4

05:14

3. Understanding the Machine Learning Process.mp4

04:44

4. Identifying the Type of a Machine Learning Problem.mp4

08:49

3. Classifying Data into Predefined Categories

1. Understanding the Setup of a Classification Problem.mp4

08:06

2. Detecting the Gender of a User.mp4

04:13

3. Classifying Text on the Basis of Sentiment.mp4

05:18

4. Deciding a Trading Strategy.mp4

03:23

5. Detecting Ads.mp4

02:49

6. Understanding Customer Behavior.mp4

04:53

4. Solving Classification Problems

1. Using the Naive Bayes Algorithm for Sentiment Analysis.mp4

07:29

2. Understanding When to use Naive Bayes.mp4

02:00

3. Implementing Naive Bayes.mp4

08:14

4. Detecting Ads Using Support Vector Machines.mp4

04:33

5. Implementing Support Vector Machines.mp4

09:20

5. Predicting Relationships between Variables with Regression

1. Understanding the Regression Setup.mp4

03:24

2. Forecasting Demand.mp4

02:15

3. Predicting Stock Returns.mp4

02:38

4. Detecting Facial Features.mp4

02:16

5. Contrasting Classification and Regression.mp4

05:46

6. Solving Regression Problems

1. Introducing Linear Regression.mp4

03:38

2. Applying Linear Regression to Quant Trading.mp4

04:18

3. Minimizing Error Using Stochastic Gradient Descent.mp4

04:58

4. Finding the Beta for Google.mp4

04:11

5. Implementing Linear Regression in Python.mp4

03:19

7. Recommending Relevant Products to a User

1. Appreciating the Role of Recommendations.mp4

04:31

2. Predicting Ratings Using Collaborative Filtering.mp4

07:27

3. Finding Hidden Factors that Influence Ratings.mp4

08:21

4. Understanding the Alternative Least Squares Algorithm.mp4

04:18

5. Implementing ALS to Find Movie Recommendations.mp4

03:09

8. Clustering Large Data Sets into Meaningful Groups

1. Understanding the Clustering Setup.mp4

05:54

2. Contrasting Clustering and Classification.mp4

07:46

3. Document Clustering with K-Means.mp4

06:17

4. Implementing K-Means Clustering.mp4

04:51

9. Wrapping up and Next Steps

1. Surveying Machine Learning Techniques.mp4

06:50

2. Looking Ahead.mp4

05:47

Description

If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.

What You'll Learn?

Machine learning is behind some of the coolest technological innovations today, Contrary to popular perception, however, you don't need to be a math genius to successfully apply machine learning. As a data scientist facing any real-world problem, you first need to identify whether machine learning can provide an appropriate solution. In this course, How to Think About Machine Learning Algorithms, you'll learn how to identify those situations. First, you will learn how to determine which of the four basic approaches you'll take to solve the problem: classification, regression, clustering or recommendation. Next, you'll learn how to set up the problem statement, features, and labels. Finally you'll plug in a standard algorithm to solve the problem. At the end of this course, you'll have the skills and knowledge required to recognize an opportunity for a machine learning application and seize it.

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Machine Learning

Swetha Kolalapudi

Instructor's Courses

Swetha loves playing with data and crunching numbers to get cool insights. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad. She was the first member of Flipkart’s elite Analytics team and was instrumental in scaling it to 100+ employees. Swetha has always had an entrepreneurial bent and a love for teaching. She now has the chance to do both as the co¬founder of Loonycorn, a content studio focused on providing high quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.