Data Science and Machine Learning with Python

Data Science and Machine Learning with Python
Data Science and Machine Learning with Python

English | 2016 | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 9 Hours | 2.52 GB
eLearning | Skill level: All Levels

Perform data mining and Machine Learning efficiently using Python and Spark

The job of a data scientist is one of the most lucrative jobs out there today – it involves analyzing large amounts of data, and gathering actionable business insights from it using a variety of tools. This course will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. You don’t have to be an expert coder in Python to get the most out of this course – just a basic programming knowledge of Python is sufficient.

What You Will Learn

  • Learn how to clean your data and ready it for analysis
  • Implement the popular clustering and regression methods in Python
  • Train efficient machine learning models using Decision Trees and Random Forests
  • Visualize the results of your analysis using Python’s Matplotlib library
  • Visualize the results of your analysis using Python’s Matplotlib library
+ Table of Contents

Getting Started
01 Introduction
02 Getting What You Need
03 Activity Installing Enthought Canopy
04 Python Basics, Part 1
05 Activity Python Basics, Part 2
06 Running Python Scripts

Statistics and Probability Refresher, and Python Practise
07 Types of Data
08 Mean, Median, Mode
09 Activity Using mean, median, and mode in Python
10 Activity Variation and Standard Deviation
11 Probability Density Function Probability Mass Function
12 Common Data Distributions
13 Activity Percentiles and Moments
14 Activity A Crash Course in matplotlib
15 Activity Covariance and Correlation
16 Exercise Conditional Probability
17 Exercise Solution Conditional Probability of Purchase by Age
18 Bayes Theorem

Predictive Models
19 Activity Linear Regression
20 Activity Polynomial Regression
21 Activity Multivariate Regression, and Predicting Car Prices
22 Multi-Level Models

Machine Learning with Python
23 Supervised vs. Unsupervised Learning, and TrainTest
24 Activity Using TrainTest to Prevent Overfitting a Polynomial Regression
25 Bayesian Methods Concepts
26 Activity Implementing a Spam Classifier with Naive Bayes
27 K-Means Clustering
28 Activity Clustering people based on income and age
29 Measuring Entropy
31 Decision Trees Concepts
32 Activity Decision Trees Predicting Hiring Decisions
33 Ensemble Learning
34 Support Vector Machines SVM Overview
35 Activity Using SVM to cluster people using scikit-learn

Recommender Systems
36 User-Based Collaborative Filtering
37 Item-Based Collaborative Filtering
38 Activity Finding Movie Similarities
39 Activity Improving the Results of Movie Similarities
40 Activity Making Movie Recommendations to People
41 Exercise Improve the recommenders results

More Data Mining and Machine Learning Techniques
42 K-Nearest-Neighbors Concepts
43 Activity Using KNN to predict a rating for a movie
44 Dimensionality Reduction Principal Component Analysis
45 Activity PCA Example with the Iris data set
46 Data Warehousing Overview ETL and ELT
47 Reinforcement Learning

Dealing with Real-World Data
48 Activity K-Fold Cross-Validation to avoid overfitting
48 BiasVariance Tradeoff
50 Data Cleaning and Normalization
51 Activity Cleaning web log data
52 Normalizing numerical data
53 Activity Detecting outliers

Apache Spark Machine Learning on Big Data
55 Activity Installing Spark – Part 2
56 Spark Introduction
57 Spark and the Resilient Distributed Dataset RDD
58 Introducing MLLib
59 Activity Decision Trees in Spark
60 Activity K-Means Clustering in Spark
62 Activity Searching Wikipedia with Spark

Experimental Design
63 AB Testing Concepts
64 T-Tests and P-Values
65 Activity Hands-on With T-Tests
66 Determining How Long to Run an Experiment
67 AB Test Gotchas

You made it
68 More to Explore
70 Bonus Lecture Discounts on Focused MapReduce and Spark Courses.