Python Machine Learning - Part 1
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VIDEO TUTORIAL Python Machine Learning - Part 1

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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

About This Python Machine Learning Video course

  • Leverage Python’s most powerful open source libraries for deep learning, data wrangling, and data visualization
  • Get to know effective strategies and best practices to improve and optimize machine learning systems and algorithms
  • Ask—and answer— tough questions of your data with robust statistical models, built for a range of datasets

Python Machine Learning in detail

Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

This video course gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. It coversa wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and features guidance and tips on everything from sentiment analysis to neural networks.

With this video,you’ll soon be able to answer some of the most important questions facing you and your organization.

What will you learn in this course?

Course plan
Chapter 1
Giving Computers the Ability to Learn from Data
Chapter 2
Training Machine Learning Algorithms for Classification
Chapter 3
A Tour of Machine Learning Classifiers Using Scikit-Learn
Chapter 4
Building Good Training Sets – Data Preprocessing

Detailed course plan

Chapter 1 : Giving Computers the Ability to Learn from Data
11m10s
 
Lesson 1The Course Overview
Lesson 2Transforming Data into Knowledge
Lesson 3Types of Machine Learning
Chapter 2 : Training Machine Learning Algorithms for Classification
1h13m
 
Lesson 1Implementing a Perceptron Algorithm in Python
Lesson 2The Iris Dataset
Lesson 3Training the Perceptron
Lesson 4Improving the Visualization
Lesson 5Adaline in Python
Lesson 6Feature Standardization
Lesson 7Implementing Adaline
Chapter 3 : A Tour of Machine Learning Classifiers Using Scikit-Learn
1h10m
 
Lesson 1Scikit-Learn Perceptron
Lesson 2Logistic Regression in Scikit-Learn
Lesson 3Predicting Class Probabilities
Lesson 4Training a Support Vector Machine in Scikit-Learn
Lesson 5The Effect of Gamma
Lesson 6Decision Trees
Chapter 4 : Building Good Training Sets – Data Preprocessing
47m14s
 
Lesson 1Handling Data
Lesson 2Mapping Ordinal Features
Lesson 3Feature Scaling
Lesson 4Feature Importance's with Random Forests

Your questions about the course

With which software version is this tutorial compatible with?

Python

What is the required level to follow this tutorial ?

beginner

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