Author
Released
10/25/2019- Why use Python for data science
- Machine learning 101
- Linear regression
- Logistic regression
- Clustering models: K-means and hierarchal models
- Dimension reduction methods
- Association rules
- Ensembles methods
- Introduction to neural networks
- Decision tree models
Skill Level Intermediate
Duration
Views
- [Lillian] Ever wanted to be the point person at your company? You know, that person who uses data to make valuable business recommendations and then automates routine work tasks in their sleep? Then, I'm going to show you how to use machine learning algorithms to give you the insider's insight that only data scientists have. And, once you've seen how these algorithms work, you can start using them to automate almost anything you can think of. Hi, I'm Lillian Pierson. I'm a data strategist that specializes in training and advising. Let's get going on Python for Data Science.
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Introduction
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1. Introduction to Data Science
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Defining data science5m 9s
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Where does AI fit in?3m 29s
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2. Introduction to Machine Learning
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Machine learning 10110m 45s
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3. Regression Models
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Linear regression11m 18s
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Multiple linear regression8m 36s
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4. Clustering Models
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K-means method12m 31s
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Hierarchical methods13m 31s
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DBSCAN for outlier detection9m 43s
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5. Dimension Reduction Methods
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Explanatory factor analysis5m 11s
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6. Other Popular Machine Learning Methods
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Bayesian models with Naive Bayes12m 10s
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Conclusion
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Next steps1m 22s
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Video: Machine learning rocks