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Making Predictions with Data and Python
The Tools for Doing Predictive Analytics with Python
The Course Overview (4:09)
The Anaconda Distribution (3:21)
The Jupyter Notebook (5:13)
NumPy - The Foundation for Scientific Computing (10:35)
Using Pandas for Analyzing Data (10:52)
Visualization Refresher
Plotting with Matplotlib (9:54)
Visualizing data with Pandas (6:54)
Statistical Visualization with Seaborn (6:52)
Concepts in Predictive Analytics
What Is Predictive Analytics? (6:01)
How to Do Predictive Analytics? (2:52)
Machine Learning - Supervised Versus Unsupervised Learning (5:50)
Supervised Learning - Regression and Classification (4:08)
Models and Algorithms (7:48)
Regression: Concepts and Models
scikit-learn (7:32)
The Multiple Regression Model (6:38)
K-Nearest Neighbors for Regression (4:56)
Lasso Regression (3:51)
Model Evaluation for Regression (7:29)
Regression: Predicting Crime, Stock Prices, and Post Popularity
Predicting Diamond Prices (14:15)
Predicting Crime in US Communities (7:50)
Predicting Post Popularity (9:57)
Classification: Concepts and Models
Logistic Regression (14:41)
Classification Trees (11:33)
Naive Bayes Classifiers (6:12)
Model Evaluation for Classification (14:01)
Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages
Predicting Credit Card Default (22:48)
Predicting Bankruptcy (13:55)
Building a Spam Classifier (12:59)
Further Topics in Predictive Analytics (7:32)
NumPy - The Foundation for Scientific Computing
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