Data Science & Machine Learning

• Introduction
• Getting Started
• [Activity] Installing Enthought Canopy
• Python Basics, Part 1
• [Activity] Python Basics, Part 2
• Running Python Scripts
• Statistics and Probability Refresher, and Python Practice
• Types Of Data
• Mean, Median, Mode
• [Activity] Using mean, median, and mode in Python
• [Activity] Variation and Standard Deviation
• Probability Density Function; Probability Mass Function
• Common Data Distributions
• [Activity] Percentiles and Moments
• [Activity] A Crash Course in matplotlib
• [Activity] Covariance and Correlation
• [Exercise] Conditional Probability
• Exercise Solution: Conditional Probability of Purchase by Age
• Bayes' Theorem
• Predictive Models
• [Activity] Linear Regression
• [Activity] Polynomial Regression
• [Activity] Multivariate Regression, and Predicting Car Prices
• Multi-Level Models
• Machine Learning with Python
• Supervised vs. Unsupervised Learning, and Train/Test
• Supervised vs. Unsupervised Learning, and Train/Test
• Bayesian Methods: Concepts
• [Activity] Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• [Activity] Clustering people based on income and age
• Measuring Entropy
• [Activity] Install GraphViz
• Decision Trees: Concepts
• Decision Trees: Concepts
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• [Activity] Using SVM to cluster people using scikit-learn
• Recommender Systems
• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• [Activity] Finding Movie Similarities
• [Activity] Improving the Results of Movie Similarities
• [Activity] Making Movie Recommendations to People
• [Exercise] Improve the recommender's results
• More Data Mining and Machine Learning Techniques
• K-Nearest-Neighbors: Concepts
• [Activity] Using KNN to predict a rating for a movie
• Dimensionality Reduction; Principal Component Analysis
• [Activity] PCA Example with the Iris data set
• Data Warehousing Overview: ETL and ELT
• Reinforcement Learning
• External Resources
• Dealing with Real-World Data
• [Activity] K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• [Activity] Cleaning web log data
• Normalizing numerical data
• [Activity] Detecting outliers
• Apache Spark: Machine Learning on Big Data
• [Activity] Installing Spark - Part 1
• [Activity] Installing Spark - Part 1
• [Activity] Installing Spark - Part 2
• [Activity] - Installing Sparks Part 2
• Spark Introduction
• Spark and the Resilient Distributed Dataset (RDD)
• Introducing MLLib
• [Activity] Decision Trees in Spark
• Introducing MLLib
• TF / IDF
• [Activity] Using the Spark 2.0 DataFrame API for MLLib
• [Activity] Searching Wikipedia with Spark
• Installing Spark file
• Experimental Design
• A/B Testing Concepts
• T-Tests and P-Values
• [Activity] Hands-on With T-Tests
• Determining How Long to Run an Experiment
• A/B Test Gotchas

What does the course include:

• Includes 68 lectures and 9 hours of video content.
• Learn how to perform machine learning on "big data" using Apache Spark and its MLLib package.
• Apply best practices in cleaning and preparing your data prior to analysis
• Be able to design experiments and interpret the results of A/B tests
• Suitable for software developers or programmers who want to transition into the data science career path.