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Data Science and Machine Learning with Python - Hands On!
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$15.00 - $160.00
Tags: A/B testing / Apache Spark / bayes theorem / clustering / data mining / dimensionality reduction / IPython / machine learning / python / regression / reinforcement learning / Spark / statistics
“Become a data scientists in the tech industry! Comprehensive data mining and machine learning course with Python & Spark.
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 Principle 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/DF at massive scale with Apache Spark’s MLLib
- Design and evaluate A/B tests using T-Tests and P-Values”
Recommended Prerequisites: "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."
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