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

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File Name
Size
~i.txt
1.5 kB
cover.jpg
77 kB
exercise.7z
9.5 MB
playlist.m3u
3.1 kB
1. Course Overview/1. Course Overview.mp4
3.7 MB
1. Course Overview/1. Course Overview.vtt
2.5 kB
2. Introducing Machine Learning/1. Recognizing Machine Learning Applications.mp4
12 MB
2. Introducing Machine Learning/1. Recognizing Machine Learning Applications.vtt
7.7 kB
2. Introducing Machine Learning/2. Knowing When to Use Machine Learning.mp4
14 MB
2. Introducing Machine Learning/2. Knowing When to Use Machine Learning.vtt
7.1 kB
2. Introducing Machine Learning/3. Understanding the Machine Learning Process.mp4
7.6 MB
2. Introducing Machine Learning/3. Understanding the Machine Learning Process.vtt
6.3 kB
2. Introducing Machine Learning/4. Identifying the Type of a Machine Learning Problem.mp4
14 MB
2. Introducing Machine Learning/4. Identifying the Type of a Machine Learning Problem.vtt
11 kB
3. Classifying Data into Predefined Categories/1. Understanding the Setup of a Classification Problem.mp4
15 MB
3. Classifying Data into Predefined Categories/1. Understanding the Setup of a Classification Problem.vtt
10 kB
3. Classifying Data into Predefined Categories/2. Detecting the Gender of a User.mp4
7.8 MB
3. Classifying Data into Predefined Categories/2. Detecting the Gender of a User.vtt
5.4 kB
3. Classifying Data into Predefined Categories/3. Classifying Text on the Basis of Sentiment.mp4
10 MB
3. Classifying Data into Predefined Categories/3. Classifying Text on the Basis of Sentiment.vtt
6.7 kB
3. Classifying Data into Predefined Categories/4. Deciding a Trading Strategy.mp4
5.4 MB
3. Classifying Data into Predefined Categories/4. Deciding a Trading Strategy.vtt
4.8 kB
3. Classifying Data into Predefined Categories/5. Detecting Ads.mp4
5.1 MB
3. Classifying Data into Predefined Categories/5. Detecting Ads.vtt
3.7 kB
3. Classifying Data into Predefined Categories/6. Understanding Customer Behavior.mp4
8.6 MB
3. Classifying Data into Predefined Categories/6. Understanding Customer Behavior.vtt
6.8 kB
4. Solving Classification Problems/1. Using the Naive Bayes Algorithm for Sentiment Analysis.mp4
14 MB
4. Solving Classification Problems/1. Using the Naive Bayes Algorithm for Sentiment Analysis.vtt
9.9 kB
4. Solving Classification Problems/2. Understanding When to use Naive Bayes.mp4
3.3 MB
4. Solving Classification Problems/2. Understanding When to use Naive Bayes.vtt
2.5 kB
4. Solving Classification Problems/3. Implementing Naive Bayes.mp4
21 MB
4. Solving Classification Problems/3. Implementing Naive Bayes.vtt
10 kB
4. Solving Classification Problems/4. Detecting Ads Using Support Vector Machines.mp4
8.2 MB
4. Solving Classification Problems/4. Detecting Ads Using Support Vector Machines.vtt
6.0 kB
4. Solving Classification Problems/5. Implementing Support Vector Machines.mp4
24 MB
4. Solving Classification Problems/5. Implementing Support Vector Machines.vtt
12 kB
5. Predicting Relationships between Variables with Regression/1. Understanding the Regression Setup.mp4
6.2 MB
5. Predicting Relationships between Variables with Regression/1. Understanding the Regression Setup.vtt
4.8 kB
5. Predicting Relationships between Variables with Regression/2. Forecasting Demand.mp4
3.8 MB
5. Predicting Relationships between Variables with Regression/2. Forecasting Demand.vtt
3.2 kB
5. Predicting Relationships between Variables with Regression/3. Predicting Stock Returns.mp4
4.8 MB
5. Predicting Relationships between Variables with Regression/3. Predicting Stock Returns.vtt
3.4 kB
5. Predicting Relationships between Variables with Regression/4. Detecting Facial Features.mp4
5.0 MB
5. Predicting Relationships between Variables with Regression/4. Detecting Facial Features.vtt
3.1 kB
5. Predicting Relationships between Variables with Regression/5. Contrasting Classification and Regression.mp4
9.3 MB
5. Predicting Relationships between Variables with Regression/5. Contrasting Classification and Regression.vtt
7.8 kB
6. Solving Regression Problems/1. Introducing Linear Regression.mp4
6.0 MB
6. Solving Regression Problems/1. Introducing Linear Regression.vtt
4.6 kB
6. Solving Regression Problems/2. Applying Linear Regression to Quant Trading.mp4
7.8 MB
6. Solving Regression Problems/2. Applying Linear Regression to Quant Trading.vtt
5.4 kB
6. Solving Regression Problems/3. Minimizing Error Using Stochastic Gradient Descent.mp4
13 MB
6. Solving Regression Problems/3. Minimizing Error Using Stochastic Gradient Descent.vtt
5.9 kB
6. Solving Regression Problems/4. Finding the Beta for Google.mp4
9.0 MB
6. Solving Regression Problems/4. Finding the Beta for Google.vtt
5.4 kB
6. Solving Regression Problems/5. Implementing Linear Regression in Python.mp4
8.1 MB
6. Solving Regression Problems/5. Implementing Linear Regression in Python.vtt
4.3 kB
7. Recommending Relevant Products to a User/1. Appreciating the Role of Recommendations.mp4
9.1 MB
7. Recommending Relevant Products to a User/1. Appreciating the Role of Recommendations.vtt
5.9 kB
7. Recommending Relevant Products to a User/2. Predicting Ratings Using Collaborative Filtering.mp4
13 MB
7. Recommending Relevant Products to a User/2. Predicting Ratings Using Collaborative Filtering.vtt
9.5 kB
7. Recommending Relevant Products to a User/3. Finding Hidden Factors that Influence Ratings.mp4
15 MB
7. Recommending Relevant Products to a User/3. Finding Hidden Factors that Influence Ratings.vtt
9.8 kB
7. Recommending Relevant Products to a User/4. Understanding the Alternative Least Squares Algorithm.mp4
8.5 MB
7. Recommending Relevant Products to a User/4. Understanding the Alternative Least Squares Algorithm.vtt
5.2 kB
7. Recommending Relevant Products to a User/5. Implementing ALS to Find Movie Recommendations.mp4
5.6 MB
7. Recommending Relevant Products to a User/5. Implementing ALS to Find Movie Recommendations.vtt
4.0 kB
8. Clustering Large Data Sets into Meaningful Groups/1. Understanding the Clustering Setup.mp4
9.8 MB
8. Clustering Large Data Sets into Meaningful Groups/1. Understanding the Clustering Setup.vtt
6.9 kB
8. Clustering Large Data Sets into Meaningful Groups/2. Contrasting Clustering and Classification.mp4
14 MB
8. Clustering Large Data Sets into Meaningful Groups/2. Contrasting Clustering and Classification.vtt
9.4 kB
8. Clustering Large Data Sets into Meaningful Groups/3. Document Clustering with K-Means.mp4
11 MB
8. Clustering Large Data Sets into Meaningful Groups/3. Document Clustering with K-Means.vtt
7.4 kB
8. Clustering Large Data Sets into Meaningful Groups/4. Implementing K-Means Clustering.mp4
11 MB
8. Clustering Large Data Sets into Meaningful Groups/4. Implementing K-Means Clustering.vtt
6.1 kB
9. Wrapping up and Next Steps/1. Surveying Machine Learning Techniques.mp4
11 MB
9. Wrapping up and Next Steps/1. Surveying Machine Learning Techniques.vtt
9.2 kB
9. Wrapping up and Next Steps/2. Looking Ahead.mp4
11 MB
9. Wrapping up and Next Steps/2. Looking Ahead.vtt
7.7 kB