Data Science and Machine Learning with Python - Hands On! [Video]

Preview in Mapt

Data Science and Machine Learning with Python - Hands On! [Video]

Frank Kane

3 customer reviews
Perform data mining and Machine Learning efficiently using Python and Spark
Mapt Subscription
FREE
$29.99/m after trial
Video
$84.15
RRP $98.99
Save 14%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$84.15
$29.99 p/m after trial
RRP $98.99
Subscription
Video
Start 30 Day Trial

Frequently bought together


Data Science and Machine Learning with Python - Hands On! [Video] Book Cover
Data Science and Machine Learning with Python - Hands On! [Video]
$ 98.99
$ 84.15
DevOps in Finance Book Cover
DevOps in Finance
$ 35.99
$ 25.20
Buy 2 for $35.00
Save $99.98
Add to Cart

Video Details

ISBN 139781787127081
Course Length8 hour 52 minutes

Video Description

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.

Table of Contents

Getting Started
Introduction
[Activity] Getting What You Need
[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, and Mode
[Activity] Using Mean, Median, and Mode in Python
[Activity] Variation and Standard Deviation
Probability Density Function and 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 versus Unsupervised Learning and Train/Test
[Activity] Using Train/Test to Prevent Overfitting of a Polynomial Regression
Bayesian Methods – Concepts
[Activity] Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
[Activity] Clustering People Based on Income and Age
Measuring Entropy
Decision Trees – Concepts
[Activity] Decision Trees – Predicting Hiring Decisions
Ensemble Learning
Support Vector Machines (SVM) Overview
[Activity] Using SVM to Cluster People by 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 and Principal Component Analysis
[Activity] A PCA Example with the Iris Dataset
Data Warehousing Overview – ETL and ELT
Reinforcement Learning
Dealing with Real-World Data
Bias/Variance Trade-off
[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 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
[Activity] Decision Trees in Spark
[Activity] K-Means Clustering in Spark
TF/IDF
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark 2.0 DataFrame API for MLLib
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
You Made It!
More to Explore

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

Authors

Table of Contents

Getting Started
Introduction
[Activity] Getting What You Need
[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, and Mode
[Activity] Using Mean, Median, and Mode in Python
[Activity] Variation and Standard Deviation
Probability Density Function and 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 versus Unsupervised Learning and Train/Test
[Activity] Using Train/Test to Prevent Overfitting of a Polynomial Regression
Bayesian Methods – Concepts
[Activity] Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
[Activity] Clustering People Based on Income and Age
Measuring Entropy
Decision Trees – Concepts
[Activity] Decision Trees – Predicting Hiring Decisions
Ensemble Learning
Support Vector Machines (SVM) Overview
[Activity] Using SVM to Cluster People by 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 and Principal Component Analysis
[Activity] A PCA Example with the Iris Dataset
Data Warehousing Overview – ETL and ELT
Reinforcement Learning
Dealing with Real-World Data
Bias/Variance Trade-off
[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 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
[Activity] Decision Trees in Spark
[Activity] K-Means Clustering in Spark
TF/IDF
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark 2.0 DataFrame API for MLLib
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
You Made It!
More to Explore

Video Details

ISBN 139781787127081
Course Length8 hour 52 minutes
Read More
From 3 reviews

Read More Reviews

Recommended for You

DevOps in Finance Book Cover
DevOps in Finance
$ 35.99
$ 25.20
Unity 2017 Game Development Essentials - Third Edition Book Cover
Unity 2017 Game Development Essentials - Third Edition
$ 39.99
$ 28.00
Apache Camel Essentials Book Cover
Apache Camel Essentials
$ 19.99
$ 14.00
Robot Operating System Cookbook Book Cover
Robot Operating System Cookbook
$ 39.99
$ 28.00
Mastering Machine Learning Algorithms Book Cover
Mastering Machine Learning Algorithms
$ 35.99
$ 25.20
Mastering Spring Cloud Book Cover
Mastering Spring Cloud
$ 35.99
$ 25.20