Data Science and Machine Learning with Python - Hands On!

Frank Kane, Founder of Sundog Education, ex-Amazon

Play Speed
  • 0.5x
  • 1x (Normal)
  • 1.25x
  • 1.5x
  • 2x
69 Videos (9h 2m)
    • Introduction

      2:44
    • Getting What you Need

      2:36
    • Installing Enthought Canopy

      6:51
    • Python Basics, Part 1

      15:58
    • Python Basics, Part 2

      9:41
    • Running Python Scripts

      3:55
    • Introducing the Pandas Library

      10:14
    • Types of Data

      6:58
    • Mean, Median, Mode

      5:26
    • Using mean, median, and mode in Python

      8:30
    • Variation and Standard Deviation

      11:12
    • Probability Density Function; Probability Mass Function

      3:27
    • Common Data Distributions

      7:45
    • Percentiles and Moments

      12:32
    • A Crash Course in matplotlib

      13:46
    • Covariance and Correlation

      11:31
    • Exercise: Conditional Probability

      10:16
    • Exercise Solution: Conditional Probability

      2:18
    • Bayes' Theorem

      5:23
    • Linear Regression

      11:01
    • Polynomial Regression

      8:04
    • Multivariate Regression

      9:52
    • Multi-Level Models

      4:36
    • Supervised vs. Unsupervised Learning, Train / Test

      8:57
    • Using Train/Test to Prevent Overfitting

      5:47
    • Bayesian Methods: Concepts

      3:59
    • Implementing a Spam Classifier with Naive Bayes

      8:05
    • K-Means Clustering

      7:23
    • Clustering People by Income and Age

      5:14
    • Measuring Entropy

      3:09
    • Decision Trees: Concepts

      8:43
    • Decision Trees: Predicting Hiring Decisions

      9:47
    • Ensemble Learning

      5:59
    • Support Vector Machines (SVM) Overview

      4:27
    • Using SVM to Cluster People

      5:36
    • User-Based Collaborative Filtering

      7:57
    • Item-Based Collaborative Filtering

      8:15
    • Finding Movie Similarities

      9:08
    • Improving the Results of Movie Similarities

      7:59
    • Making Movie Recommendations to People

      10:22
    • Improving the Recommender's Results

      5:29
    • K-Nearest-Neighbors: Concepts

      3:44
    • Using KNN to Predict a Rating for a Movie

      12:29
    • Dimensionality Reduction; Principal Component Analysis

      5:44
    • PCA Example with the Iris Data Set

      9:05
    • Data Warehousing; ETL and ELT

      9:05
    • Reinforcement Learning

      12:44
    • Bias / Variance Tradeoff

      6:15
    • K-Fold Cross Validation

      10:54
    • Data Cleaning and Normalization

      7:10
    • Cleaning Web Log Data

      10:56
    • Normalizing Numerical Data

      3:22
    • Detecting Outliers

      7:00
    • Installing Spark - Part 1

      7:02
    • Installing Spark - Part 2

      13:29
    • Spark Introduction

      9:10
    • Spark and the Resilient Distributed Dataset (RDD)

      11:42
    • Introducing MLLib

      5:09
    • Decision Trees in Spark

      16:00
    • K-Means Clustering in Spark

      11:07
    • TF / IDF

      6:43
    • Searching Wikipedia with Spark

      8:11
    • Using the Spark 2.0 DataFrame API for MLLib

      7:57
    • A/B Testing Concepts

      8:23
    • T-Tests and P-Values

      5:59
    • Hands-On with T-Tests

      6:03
    • Determining How Long to Run an Experiment

      3:24
    • A/B Test Gotchas

      9:26
    • Where to Go From Here

      2:59

About This Class

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.

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. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them.

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.

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!



12

Students

--

Projects

0

Reviews (0)

Frank Kane

Founder of Sundog Education, ex-Amazon

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

See full profile

Technology Data Science Machine Learning