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Software Development / AI & Machine Learning

Data Science and Machine Learning with Python

Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python & Spark.



Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including: 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 ...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!
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  • Getting Started
    DOWNLOAD Course Resources
    Download this .zip file to access materials that I reference throughout the course.
    What to expect in this course, who it's for, and the general format we'll follow.
    Installing Enthought Canopy
    We'll walk through installing the Python scientific computing IDE used in this course: Enthought Canopy, and the Python packages needed to run the scripts used in this course.
    Python Basics, Part 2
    In part 2 of our Python crash course, we'll cover functions, boolean expressions, and looping constructs in Python.
    Running Python Scripts
    This course presents Python examples in the form of iPython Notebooks, but we'll cover the other ways to run Python code: interactively from the Python shell, or running stand-alone Python script files.
    Python Basics, Part 1
    In a crash course on Python and what's different about it, we'll cover the importance of whitespace in Python scripts, how to import Python modules, and Python data structures including lists, tuples, and dictionaries.
  • Statistics and Probability Refresher, and Python Practise
    Types Of Data
    We cover the differences between continuous and discrete numerical data, categorical data, and ordinal data.
    Mean, Median, Mode
    A refresher on mean, median, and mode - and when it's appropriate to use each.
    Using Mean, Median, and Mode in Python
    We'll use mean, median, and mode in some real Python code, and set you loose to write some code of your own.
    Variation and Standard Deviation
    We'll cover how to compute the variation and standard deviation of a data distribution, and how to do it using some examples in Python.
    Probability Density Function; Probability Mass Function
    Introducing the concepts of probability density functions (PDF's) and probability mass functions (PMF's).
    Common Data Distributions
    We'll show examples of continuous, normal, exponential, binomial, and poisson distributions using iPython.
    Percentiles and Moments
    We'll look at some examples of percentiles and quartiles in data distributions, and then move on to the concept of the first four moments of data sets.
    A Crash Course in matplotlib
    An overview of different tricks in matplotlib for creating graphs of your data, using different graph types and styles.
    Covariance and Correlation
    The concepts of covariance and correlation used to look for relationships between different sets of attributes, and some examples in Python.
    Exercise: Conditional Probability
    We cover the concepts and equations behind conditional probability, and use it to try and find a relationship between age and purchases in some fabricated data using Python.
    Exercise Solution: Conditional Probability of Purchase by Age
    Here we'll go over my solution to the exercise I challenged you with in the previous lecture - changing our fabricated data to have no real correlation between ages and purchases, and seeing if you can detect that using conditional probability.
    Bayes Theorem
    An overview of Bayes' Theorem, and an example of using it to uncover misleading statistics surrounding the accuracy of drug testing.
  • Predictive Models
    Linear Regression
    We introduce the concept of linear regression and how it works, and use it to fit a line to some sample data using Python.
    Polynomial Regression
    We cover the concepts of polynomial regression, and use it to fit a more complex page speed - purchase relationship in Python.
    Multivariate Regression, and Predicting Car Prices
    Multivariate models let us predict some value given more than one attribute. We cover the concept, then use it to build a model in Python to predict car prices based on their age, mileage, and model. We'll also get our first look at the pandas library in Python.
    Multi-Level Models
    We'll just cover the concept of multi-level modeling, as it is a very advanced topic. But you'll get the ideas and challenges behind it.
  • Machine Learning with Python
    Supervised vs. Unsupervised Learning, and Train/Test
    The concepts of supervised and unsupervised machine learning, and how to evaluate the ability of a machine learning model to predict new values using the train/test technique.
    Using Train/Test to Prevent Overfitting a Polynomial Regression
    We'll apply train test to a real example using Python.
    Bayesian Methods: Concepts
    We'll introduce the concept of Naive Bayes and how we might apply it to the problem of building a spam classifier.
    Implementing a Spam Classifier with Naive Bayes
    We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!
    K-Means Clustering
    K-Means is a way to identify things that are similar to each other. It's a case of unsupervised learning, which could result in clusters you never expected!
    Clustering people based on income and age
    We'll apply K-Means clustering to find interesting groupings of people based on their age and income.
    Measuring Entropy
    Entropy is a measure of the disorder in a data set - we'll learn what that means, and how to compute it mathematically.
    Decision Trees: Concepts
    Decision trees can automatically create a flow chart for making some decision, based on machine learning! Let's learn how they work.
    Decision Trees: Predicting Hiring Decisions
    We'll create a decision tree and an entire "random forest" to predict hiring decisions for job candidates.
    Ensemble Learning
    Random Forests was an example of ensemble learning; we'll cover over techniques for combining the results of many models to create a better result than any one could produce on its own.
    Support Vector Machines (SVM) Overview
    Support Vector Machines are an advanced technique for classifying data that has multiple features. It treats those features as dimensions, and partitions this higher-dimensional space using "support vectors."
    Using SVM to cluster people using scikit-learn
    We'll use scikit-learn to easily classify people using a C-Support Vector Classifier.
  • Recommender Systems
    User-Based Collaborative Filtering
    One way to recommend items is to look for other people similar to you based on their behavior, and recommend stuff they liked that you haven't seen yet.
    Item-Based Collaborative Filtering
    The shortcomings of user-based collaborative filtering can be solved by flipping it on its head, and instead looking at relationships between items instead of relationships between people.
    Finding Movie Similarities
    We'll use the real-world MovieLens data set of movie ratings to take a first crack at finding movies that are similar to each other, which is the first step in item-based collaborative filtering.
    Improving the Results of Movie Similarities
    Our initial results for movies similar to Star Wars weren't very good. Let's figure out why, and fix it.
    Making Movie Recommendations to People
    We'll implement a complete item-based collaborative filtering system that uses real-world movie ratings data to recommend movies to any user.
    Improve the recommender's results
    As a student exercise, try some of my ideas - or some ideas of your own - to make the results of our item-based collaborative filter even better.
  • More Data Mining and Machine Learning Techniques
    K-Nearest-Neighbors: Concepts
    KNN is a very simple supervised machine learning technique; we'll quickly cover the concept here.
    Using KNN to predict a rating for a movie
    We'll use the simple KNN technique and apply it to a more complicated problem: finding the most similar movies to a given movie just given its genre and rating information, and then using those "nearest neighbors" to predict the movie's rating.
    Dimensionality Reduction; Principal Component Analysis
    Data that includes many features or many different vectors can be thought of as having many dimensions. Often it's useful to reduce those dimensions down to something more easily visualized, for compression, or to just distill the most important information from a data set (that is, information that contributes the most to the data's variance.) Principal Component Analysis and Singular Value Decomposition do that.
    PCA Example with the Iris data set
    We'll use sckikit-learn's built-in PCA system to reduce the 4-dimensions Iris data set down to 2 dimensions, while still preserving most of its variance.
    Data Warehousing Overview: ETL and ELT
    Cloud-based data storage and analysis systems like Hadoop, Hive, Spark, and MapReduce are turning the field of data warehousing on its head. Instead of extracting, transforming, and then loading data into a data warehouse, the transformation step is now more efficiently done using a cluster after it's already been loaded. With computing and storage resources so cheap, this new approach now makes sense.
    Reinforcement Learning
    We'll describe the concept of reinforcement learning - including Markov Decision Processes, Q-Learning, and Dynamic Programming - all using a simple example of developing an intelligent Pac-Man.
  • Dealing with Real-World Data
    Bias/Variance Tradeoff
    Bias and Variance both contribute to overall error; understand these components of error and how they relate to each other.
    K-Fold Cross-Validation to avoid overfitting
    We'll introduce the concept of K-Fold Cross-Validation to make train/test even more robust, and apply it to a real model.
    Data Cleaning and Normalization
    Cleaning your raw input data is often the most important, and time-consuming, part of your job as a data scientist!
    Cleaning web log data
    In this example, we'll try to find the top-viewed web pages on a web site - and see how much data pollution makes that into a very difficult task!
    Normalizing numerical data
    A brief reminder: some models require input data to be normalized, or within the same range, of each other. Always read the documentation on the techniques you are using.
    Detecting outliers
    A review of how outliers can affect your results, and how to identify and deal with them in a principled manner.
  • Apache Spark: Machine Learning on Big Data
    Installing Spark - Part 1
    We'll present an overview of the steps needed to install Apache Spark on your desktop in standalone mode, and get started by getting a Java Development Kit installed on your system.
    Installing Spark - Part 2
    We'll install Spark itself, along with all the associated environment variables and ancillary files and settings needed for it to function properly.
    Spark Introduction
    A high-level overview of Apache Spark, what it is, and how it works.
    Spark and the Resilient Distributed Dataset (RDD)
    We'll go in more depth on the core of Spark - the RDD object, and what you can do with it.
    Introducing MLLib
    A quick overview of MLLib's capabilities, and the new data types it introduces to Spark.
    Decision Trees in Spark
    We'll take the same problem for our earlier Decision Tree lecture - predicting hiring decisions for job candidates - but implement it using Spark and MLLib!
    K-Means Clustering in Spark
    We'll take the same example of clustering people by age and income from our earlier K-Means lecture - but solve it in Spark!
    TF / IDF
    We'll introduce the concept of TF-IDF (Term Frequency / Inverse Document Frequency) and how it applies to search problems, in preparation for using it with MLLib.
    Searching Wikipedia with Spark
    Let's use TF-IDF, Spark, and MLLib to create a rudimentary search engine for real Wikipedia pages!
  • Experimental Design
    A/B Testing Concepts
    Running controlled experiments on your website usually involves a technique called the A/B test. We'll learn how they work.
    T-Tests and P-Values
    How to determine significance of an A/B tests results, and measure the probability of the results being just from random chance, using T-Tests, the T-statistic, and the P-value.
    Hands-on With T-Tests
    We'll fabricate A/B test data from several scenarios, and measure the T-statistic and P-Value for each using Python.
    Determining How Long to Run an Experiment
    Some A/B tests just don't affect customer behavior one way or another. How do you know how long to let an experiment run for before giving up?
    A/B Test Gotchas
    There are many limitations associated with running short-term A/B tests - novelty effects, seasonal effects, and more can lead you to the wrong decisions. We'll discuss the forces that may result in misleading A/B test results so you can watch out for them.
  • You Made It!
    More to Explore
    Where to go from here - recommendations for books, websites, and career advice to get you into the data science job you want.


  • Machine Learning
  • Apache Spark
  • Data Analysis
  • Python

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