Data Science & Machine Learning

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.

Course Highlights

• 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

Course Overview

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.

Instructor Bio

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.

Course Curriculum

Introduction

• 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 Practise

• 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

Why Choose OfCourse Learning?

- All courses are taught by verified experts

- Students enjoy 24/7 access to their courses

- Courses can be accessed on any device

- Students get lifetime access to their courses so they can work at their own pace

- On completion, students receive a course certificate from a recognised educational institution

- Become a part of a global learning community where you can talk with teachers and students about the course

Suitable for software developers or programmers who want to transition into the data science career path.

This course has no prerequisites

Learn the fundamentals to start a career as a data scientist

Certificate of completion

Digital certificate - Included