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DEMO - How to think about Machine Learning Algorithms
DEMO - How to think about Machine Learning AlgorithmsRobotics Courses |
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| Superadmin | 31.05.17 | 407 | No Rating | |
| Description | ||||
| 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. Course Overview 1m 45s Introducing Machine Learning 24m 43s Recognizing Machine Learning Applications 5m 54s Knowing When to Use Machine Learning 5m 14s Understanding the Machine Learning Process 4m 44s Identifying the Type of a Machine Learning Problem 8m 49s Classifying Data into Predefined Categories 28m 44s Understanding the Setup of a Classification Problem 8m 6s Detecting the Gender of a User 4m 13s Classifying Text on the Basis of Sentiment 5m 18s Deciding a Trading Strategy 3m 23s Detecting Ads 2m 49s Understanding Customer Behavior 4m 53s Solving Classification Problems 31m 37s Using the Naive Bayes Algorithm for Sentiment Analysis 7m 29s Understanding When to use Naive Bayes 1m 59s Implementing Naive Bayes 8m 14s Detecting Ads Using Support Vector Machines 4m 33s Implementing Support Vector Machines 9m 20s Predicting Relationships between Variables with Regression 16m 21s Understanding the Regression Setup 3m 24s Forecasting Demand 2m 15s Predicting Stock Returns 2m 38s Detecting Facial Features 2m 16s Contrasting Classification and Regression 5m 46s Solving Regression Problems 20m 26s Introducing Linear Regression 3m 38s Applying Linear Regression to Quant Trading 4m 18s Minimizing Error Using Stochastic Gradient Descent 4m 58s Finding the Beta for Google 4m 11s Implementing Linear Regression in Python 3m 19s Recommending Relevant Products to a User 27m 48s Appreciating the Role of Recommendations 4m 31s Predicting Ratings Using Collaborative Filtering 7m 27s Finding Hidden Factors that Influence Ratings 8m 21s Understanding the Alternative Least Squares Algorithm 4m 18s Implementing ALS to Find Movie Recommendations 3m 9s Clustering Large Data Sets into Meaningful Groups 24m 49s Understanding the Clustering Setup 5m 54s Contrasting Clustering and Classification 7m 46s Document Clustering with K-Means 6m 17s Implementing K-Means Clustering 4m 51s Wrapping up and Next Steps 12m 38s Surveying Machine Learning Techniques 6m 50s Looking Ahead 5m 47s | ||||
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