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

Python Machine Learning - Part 1

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

  • Video Duration:04 Hours
  • Cost: $ 124.99

Course Details

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 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 featuresguidance 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.

Who all can attend

If you want to find out how to use Python to start answering critical questions using your data, this video is ideal. Whether you want to get started from scratch or want to extend your data science knowledge, this is an essential resource.

What you will learn from this course

    li> Discover the different types of machine learning and know when to use them
  • Explore machine learning algorithms and implement them in Python
  • Use powerful open source machine learning libraries to train predictive models
  • Use pandas, NumPy, and matplotlib to manipulate data
  • Evaluate and fine-tune machine learning models

Course Content

  1. Giving Computers the Ability to Learn from Data
    • The Course Overview
    • Transforming Data into Knowledge
    • Types of Machine Learning

  2. Training Machine Learning Algorithms for Classification
    • Implementing a Perceptron Algorithm in Python
    • The Iris Dataset
    • Training the Perceptron
    • Improving the Visualization
    • Adaline in Python
    • Feature Standardization
    • Implementing Adaline

  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
    • Scikit-Learn Perceptron
    • Logistic Regression in Scikit-Learn
    • Predicting Class Probabilities
    • Training a Support Vector Machine in Scikit-Learn
    • The Effect of Gamma
    • Decision Trees

  4. Building Good Training Sets – Data Preprocessing
    • Handling Data
    • Mapping Ordinal Features
    • Feature Scaling
    • Feature Importance's with Random Forests

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Instructor-led online training is also available for the same course.

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