Intel: Get now a free Python course oriented to Artificial Intelligence

This course is aimed at developers, students or industry professionals from other fields of engineering and computer science who are curious about AI.

Explore artificial intelligence, what is it used for and why, without the math that later courses entail.

Topics covered include:

  • The history of AI and why it is one of today's key technologies
  • The role of AI in business and in various industries, from medicine to automated driving
  • Why data is important both for training neural networks and for steps in a data science workflow
  • An introduction to supervised learning and deep learning (before taking a full deep learning course)
  • An introduction to current hardware and software

At the end of this course, students will have practical knowledge of:

  • The definition of AI, machine learning, deep learning and the historical developments that now differentiate modern AI from the AI ​​of the past.
  • How AI Can Help Solve Problems in Today's Industry (with examples) and how it is becoming more important in business computing
  • The importance of data sets, the data sources, problem solving with data and data science workflows.
  • The Fundamentals of Supervised Learning and an Introduction to Deep Learning Concepts
  • How Intel® Hardware and Software Can Be Applied to Solve AI Problems

The course is structured around eight weeks of lectures and exercises. Each week requires 90 minutes to complete.

The exercises are implemented in Python *, so it is recommended to become familiar with the language (can learn along the way).


Python programming *

Week 1

This class introduces the key concepts of AI:

  • The definition of AI, machine learning and deep learning
  • Historical developments that now differentiate modern AI from previous AI
  • Examples of machine learning and deep learning
  • The differences between supervised and unsupervised learning
  • Examples of where AI is being applied

Week 2

This class covers industries that are being transformed by AI and gives examples of:

  • Health and genomics
  • Automated driving and transport
  • Retail and supply chain
  • Finance
  • Industrial
  • government

Week 3

This class focuses on AI in the enterprise, introduces the data science workflow and teaches you how:

  • Identify the steps in the data science workflow.
  • Identify key roles and skill sets within the field of AI.
  • Describe ways to structure an AI team.
  • Identify common data science misconceptions
  • Identify the components of maintaining the AI ​​model after implementation.

Week 4

This class introduces the concept of supervised learning. You will be able to:

  • Explain how to formulate a supervised learning problem.
  • Compare and understand the differences between training and inference.
  • Describe the dangers of overfitting and training versus test data.
  • Understand how the Python programming language applies to AI.

For a more advanced view of machine learning and supervised learning, seeMachine learning .

Week 5

This class focuses on data types and sources. Since data is a fundamental part of training an artificial intelligence neural network, this lesson analyzes:

  • How to Recognize Situations Where More Data Samples Are Needed
  • Data management, data augmentation and function engineering
  • How to identify problems such as overfitting and misfitting
  • Various popular data sets used in neural network training.
  • Different data preprocessing methods
  • Ways to label data
  • How to identify challenges when working with data

Week 6

This session reviews the principles of deep learning, including:

  • The basics of deep learning and how it fits into artificial intelligence and machine learning
  • The Kinds of Problems Deep Learning Solves
  • The steps to build a neural network model
  • The definition of a convolutional neural network (CNN)
  • Transfer learning and why it is useful
  • Common deep learning architectures

For a more advanced insight into deep learning, seeDeep learning .

Week 7

This week covers hardware, what includes:

  • End-to-end computing for AI
  • The capabilities that data centers provide, gateways and edge computing.
  • The different types of processors from the data center to the edge
  • How Intel® Hardware Applies to AI

Week 8

Conclude the course with a review of important software building blocks. This class covers:

  • Deep learning frameworks
  • Libraries and Frameworks Optimized for Intel® Architecture
  • The impact of big data and the use of the BigDL library for Apache Spark *
  • Get access to Intel® AI DevCloud

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