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Machine Learning with Python

New – Use the most powerful Python libraries to implement machine learning and deep learning in this self-paced course.

GK# 7727

Course Overview

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This course is your entry point to machine learning. It starts with an introduction to machine learning and the Python language. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language.

Schedule

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  • Delivery Format:
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What You'll Learn

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  • Understand the key frameworks in data science, machine learning, and deep learning
  • Harness the power of the latest Python open source libraries in machine learning
  • Explore machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow library
  • Learn the mechanics of classification algorithms to implement the best tool for the job

Outline

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On-Demand Outline

Get introduced to machine learning and deep learning algorithms from scratch

Learn how to apply them to practical industry challenges using realistic and interesting examples

Find a new balance of classical ideas and modern insights into machine learning

Get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn

Gather a broad picture of the machine learning ecosystem and learn the best practices for applying machine learning techniques

Who Should Attend

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This course is for developers who wish to learn about the ML and DL techniques and how you can implement them in Python. Knowledge of Python is assumed.

Course Delivery

This course is available in the following formats:

On-Demand

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