How to Think About Machine Learning Algorithms
Год выпуска: 2016
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/library/courses/machine-learning-algorithms
Автор: Swetha Kolalapudi
Продолжительность: 3h 8m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
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.
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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Содержание
1. Course Overview
1. Course Overview
2. Introducing Machine Learning
1. Recognizing Machine Learning Applications
2. Knowing When to Use Machine Learning
3. Understanding the Machine Learning Process
4. Identifying the Type of a Machine Learning Problem
3. Classifying Data into Predefined Categories
1. Understanding the Setup of a Classification Problem
2. Detecting the Gender of a User
3. Classifying Text on the Basis of Sentiment
4. Deciding a Trading Strategy
5. Detecting Ads
6. Understanding Customer Behavior
4. Solving Classification Problems
1. Using the Naive Bayes Algorithm for Sentiment Analysis
2. Understanding When to use Naive Bayes
3. Implementing Naive Bayes
4. Detecting Ads Using Support Vector Machines
5. Implementing Support Vector Machines
5. Predicting Relationships between Variables with Regression
1. Understanding the Regression Setup
2. Forecasting Demand
3. Predicting Stock Returns
4. Detecting Facial Features
5. Contrasting Classification and Regression
6. Solving Regression Problems
1. Introducing Linear Regression
2. Applying Linear Regression to Quant Trading
3. Minimizing Error Using Stochastic Gradient Descent
4. Finding the Beta for Google
5. Implementing Linear Regression in Python
7. Recommending Relevant Products to a User
1. Appreciating the Role of Recommendations
2. Predicting Ratings Using Collaborative Filtering
3. Finding Hidden Factors that Influence Ratings
4. Understanding the Alternative Least Squares Algorithm
5. Implementing ALS to Find Movie Recommendations
8. Clustering Large Data Sets into Meaningful Groups
1. Understanding the Clustering Setup
2. Contrasting Clustering and Classification
3. Document Clustering with K-Means
4. Implementing K-Means Clustering
9. Wrapping up and Next Steps
1. Surveying Machine Learning Techniques
2. Looking Ahead
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 29fps, 147 kb/s
Аудио: AAC, 48.0 kHz, 96.0 kbit/s, 2 channels
