- TBD
- 60 sessions
- 8.0 hours/session

- TBD
- 60 sessions
- 8.0 hours/session

• Deep Learning / Neural Networks (MLP's, CNN's, RNN's)

• Regression analysis

• K-Means Clustering

• Principal Component Analysis

• Train/Test and cross validation

• Bayesian Methods

• Decision Trees and Random Forests

• Multivariate Regression

• Multi-Level Models

• Support Vector Machines

• Reinforcement Learning

• Collaborative Filtering

• K-Nearest Neighbor

• Bias/Variance Tradeoff

• Ensemble Learning

• Term Frequency / Inverse Document Frequency

• Experimental Design and A/B Tests

• Develop using iPython notebooks

• Understand statistical measures such as standard deviation

• Visualize data distributions, probability mass functions, and probability density functions

• Visualize data with matplotlib

• Use covariance and correlation metrics

• Apply conditional probability for finding correlated features

• Use Bayes' Theorem to identify false positives

• Make predictions using linear regression, polynomial regression, and multivariate regression • Understand complex multi-level models

• Use train/test and K-Fold cross validation to choose the right model

• Build a spam classifier using Naive Bayes

• Use decision trees to predict hiring decisions

• Cluster data using K-Means clustering and Support Vector Machines (SVM)

• Build a movie recommender system using item-based and user-based collaborative filtering

• Predict classifications using K-Nearest-Neighbor (KNN)

• Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers

• Understand reinforcement learning - and how to build a Pac-Man bot

There are no open runs for this course at the moment. If you're interested in taking this course, you may join the waitlist and you will be notified when there are vacancies.

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