Data Science & Machine Learning

Course details

• 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."""

This course will teach you the techniques used by real data scientists in the tech industry and prepare you for a move into this career path. It includes hands-on Python code examples which you can use for reference and for practice. It also contains an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analysed on a computing cluster.

Frank Kane spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He also started his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

This course is intended for software developers or programmers who want to transition into the lucrative data science career path. It would also suit Data analysts in the finance or other non-tech industries who want to transition into the tech industry. You will learn how to analyse data using code instead of tools and it covers the machine learning and data mining techniques real employers are looking for.

• Introduction
• Share your course with friends and family!
• Say hi to your fellow students!

• [Activity] Installing Enthought Canopy
• Python Basics, Part 1
• [Activity] Python Basics, Part 2
• Running Python Scripts

• 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

• [Activity] Linear Regression
• [Activity] Polynomial Regression
• [Activity] Multivariate Regression, and Predicting Car Prices
• Multi-Level Models

• 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

• 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

• 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

• [Activity] K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• [Activity] Cleaning web log data
• Normalizing numerical data
• [Activity] Detecting outliers

• [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

• 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

• Recommended Courses