Data Science, Deep Learning, & Machine Learning with Python

Go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking!

What Will I Learn?

• 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
• Clean your input data to remove outliers
• Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark's MLLib
• Design and evaluate A/B tests using T-Tests and P-Values

Requirements

• You'll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
• Some prior coding or scripting experience is required.
• At least high school level math skills will be required.
• This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.

Includes:

• 12 hours on-demand video
• 3 Articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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