How to Think About Machine Learning Algorithms (Swetha Kolalapudi, 2016)

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How to Think About Machine Learning Algorithms (Swetha Kolalapudi, 2016)

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种子哈希：0a24cb29deb600bbd08fd0b3c23b1dd82b0e9222
文件大小：374.32M
已经下载：1586次
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收录时间：2024-06-28
最近下载：2025-10-05

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文件列表

  • 4. Solving Classification Problems/5. Implementing Support Vector Machines.mp4 24.9 MB
  • 4. Solving Classification Problems/3. Implementing Naive Bayes.mp4 22.1 MB
  • 7. Recommending Relevant Products to a User/3. Finding Hidden Factors that Influence Ratings.mp4 15.7 MB
  • 3. Classifying Data into Predefined Categories/1. Understanding the Setup of a Classification Problem.mp4 15.5 MB
  • 2. Introducing Machine Learning/4. Identifying the Type of a Machine Learning Problem.mp4 15.2 MB
  • 2. Introducing Machine Learning/2. Knowing When to Use Machine Learning.mp4 14.3 MB
  • 4. Solving Classification Problems/1. Using the Naive Bayes Algorithm for Sentiment Analysis.mp4 14.2 MB
  • 8. Clustering Large Data Sets into Meaningful Groups/2. Contrasting Clustering and Classification.mp4 14.1 MB
  • 6. Solving Regression Problems/3. Minimizing Error Using Stochastic Gradient Descent.mp4 13.6 MB
  • 7. Recommending Relevant Products to a User/2. Predicting Ratings Using Collaborative Filtering.mp4 13.4 MB
  • 2. Introducing Machine Learning/1. Recognizing Machine Learning Applications.mp4 12.1 MB
  • 8. Clustering Large Data Sets into Meaningful Groups/4. Implementing K-Means Clustering.mp4 11.8 MB
  • 8. Clustering Large Data Sets into Meaningful Groups/3. Document Clustering with K-Means.mp4 11.7 MB
  • 9. Wrapping up and Next Steps/2. Looking Ahead.mp4 11.5 MB
  • 9. Wrapping up and Next Steps/1. Surveying Machine Learning Techniques.mp4 11.4 MB
  • 3. Classifying Data into Predefined Categories/3. Classifying Text on the Basis of Sentiment.mp4 11.0 MB
  • 8. Clustering Large Data Sets into Meaningful Groups/1. Understanding the Clustering Setup.mp4 10.3 MB
  • exercise.7z 9.9 MB
  • 5. Predicting Relationships between Variables with Regression/5. Contrasting Classification and Regression.mp4 9.8 MB
  • 7. Recommending Relevant Products to a User/1. Appreciating the Role of Recommendations.mp4 9.6 MB
  • 6. Solving Regression Problems/4. Finding the Beta for Google.mp4 9.5 MB
  • 3. Classifying Data into Predefined Categories/6. Understanding Customer Behavior.mp4 9.1 MB
  • 7. Recommending Relevant Products to a User/4. Understanding the Alternative Least Squares Algorithm.mp4 8.9 MB
  • 4. Solving Classification Problems/4. Detecting Ads Using Support Vector Machines.mp4 8.6 MB
  • 6. Solving Regression Problems/5. Implementing Linear Regression in Python.mp4 8.5 MB
  • 3. Classifying Data into Predefined Categories/2. Detecting the Gender of a User.mp4 8.2 MB
  • 6. Solving Regression Problems/2. Applying Linear Regression to Quant Trading.mp4 8.1 MB
  • 2. Introducing Machine Learning/3. Understanding the Machine Learning Process.mp4 8.0 MB
  • 5. Predicting Relationships between Variables with Regression/1. Understanding the Regression Setup.mp4 6.5 MB
  • 6. Solving Regression Problems/1. Introducing Linear Regression.mp4 6.3 MB
  • 7. Recommending Relevant Products to a User/5. Implementing ALS to Find Movie Recommendations.mp4 5.9 MB
  • 3. Classifying Data into Predefined Categories/4. Deciding a Trading Strategy.mp4 5.7 MB
  • 3. Classifying Data into Predefined Categories/5. Detecting Ads.mp4 5.4 MB
  • 5. Predicting Relationships between Variables with Regression/4. Detecting Facial Features.mp4 5.3 MB
  • 5. Predicting Relationships between Variables with Regression/3. Predicting Stock Returns.mp4 5.0 MB
  • 5. Predicting Relationships between Variables with Regression/2. Forecasting Demand.mp4 3.9 MB
  • 1. Course Overview/1. Course Overview.mp4 3.9 MB
  • 4. Solving Classification Problems/2. Understanding When to use Naive Bayes.mp4 3.5 MB
  • cover.jpg 78.6 kB
  • 4. Solving Classification Problems/5. Implementing Support Vector Machines.vtt 11.9 kB
  • 2. Introducing Machine Learning/4. Identifying the Type of a Machine Learning Problem.vtt 11.5 kB
  • 3. Classifying Data into Predefined Categories/1. Understanding the Setup of a Classification Problem.vtt 10.5 kB
  • 4. Solving Classification Problems/3. Implementing Naive Bayes.vtt 10.4 kB
  • 4. Solving Classification Problems/1. Using the Naive Bayes Algorithm for Sentiment Analysis.vtt 10.1 kB
  • 7. Recommending Relevant Products to a User/3. Finding Hidden Factors that Influence Ratings.vtt 10.0 kB
  • 7. Recommending Relevant Products to a User/2. Predicting Ratings Using Collaborative Filtering.vtt 9.7 kB
  • 8. Clustering Large Data Sets into Meaningful Groups/2. Contrasting Clustering and Classification.vtt 9.6 kB
  • 9. Wrapping up and Next Steps/1. Surveying Machine Learning Techniques.vtt 9.5 kB
  • 5. Predicting Relationships between Variables with Regression/5. Contrasting Classification and Regression.vtt 8.0 kB
  • 9. Wrapping up and Next Steps/2. Looking Ahead.vtt 7.9 kB
  • 2. Introducing Machine Learning/1. Recognizing Machine Learning Applications.vtt 7.8 kB
  • 8. Clustering Large Data Sets into Meaningful Groups/3. Document Clustering with K-Means.vtt 7.6 kB
  • 2. Introducing Machine Learning/2. Knowing When to Use Machine Learning.vtt 7.3 kB
  • 8. Clustering Large Data Sets into Meaningful Groups/1. Understanding the Clustering Setup.vtt 7.1 kB
  • 3. Classifying Data into Predefined Categories/6. Understanding Customer Behavior.vtt 6.9 kB
  • 3. Classifying Data into Predefined Categories/3. Classifying Text on the Basis of Sentiment.vtt 6.8 kB
  • 2. Introducing Machine Learning/3. Understanding the Machine Learning Process.vtt 6.5 kB
  • 8. Clustering Large Data Sets into Meaningful Groups/4. Implementing K-Means Clustering.vtt 6.2 kB
  • 4. Solving Classification Problems/4. Detecting Ads Using Support Vector Machines.vtt 6.1 kB
  • 7. Recommending Relevant Products to a User/1. Appreciating the Role of Recommendations.vtt 6.0 kB
  • 6. Solving Regression Problems/3. Minimizing Error Using Stochastic Gradient Descent.vtt 6.0 kB
  • 3. Classifying Data into Predefined Categories/2. Detecting the Gender of a User.vtt 5.5 kB
  • 6. Solving Regression Problems/4. Finding the Beta for Google.vtt 5.5 kB
  • 6. Solving Regression Problems/2. Applying Linear Regression to Quant Trading.vtt 5.5 kB
  • 7. Recommending Relevant Products to a User/4. Understanding the Alternative Least Squares Algorithm.vtt 5.3 kB
  • 5. Predicting Relationships between Variables with Regression/1. Understanding the Regression Setup.vtt 5.0 kB
  • 3. Classifying Data into Predefined Categories/4. Deciding a Trading Strategy.vtt 4.9 kB
  • 6. Solving Regression Problems/1. Introducing Linear Regression.vtt 4.7 kB
  • 6. Solving Regression Problems/5. Implementing Linear Regression in Python.vtt 4.4 kB
  • 7. Recommending Relevant Products to a User/5. Implementing ALS to Find Movie Recommendations.vtt 4.1 kB
  • 3. Classifying Data into Predefined Categories/5. Detecting Ads.vtt 3.8 kB
  • 5. Predicting Relationships between Variables with Regression/3. Predicting Stock Returns.vtt 3.5 kB
  • 5. Predicting Relationships between Variables with Regression/2. Forecasting Demand.vtt 3.3 kB
  • 5. Predicting Relationships between Variables with Regression/4. Detecting Facial Features.vtt 3.2 kB
  • playlist.m3u 3.2 kB
  • 4. Solving Classification Problems/2. Understanding When to use Naive Bayes.vtt 2.6 kB
  • 1. Course Overview/1. Course Overview.vtt 2.6 kB
  • ~i.txt 1.5 kB

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