Top AI Courses from Intel

With cutting-edge hardware and toolkits, Intel is at the forefront of AI advancements. Their AI courses provide hands-on training with real-world applications, enabling learners to effectively leverage Intel's portfolio in areas such as deep learning, computer vision, and more. This article introduces Intel's AI courses, which provide a comprehensive learning path to leverage Intel's AI technologies, including deep learning, NLP, time series analysis, anomaly detection, robotics, and edge AI deployment.

Introduction to Machine Learning

This course covers the fundamentals of machine learning, including problem solving, model building, and key algorithms. By the end of the course, you will understand supervised learning, overfitting and underfitting, regularization, cross-validation, and model tuning.

Introduction to AI

This course introduces AI to developers, students, and professionals, focusing on its history, applications, and importance across various industries. It covers the fundamentals of AI, including the basics of supervised learning and deep learning, without complex math. The course is eight weeks long and consists of lectures and Python labs.

Intel AI Foundations Specialization

In this course, you will learn the fundamentals of AI, including what AI is, its current relevance, and general AI trends that will help you recommend and sell AI solutions. You will learn how to start conversations about AI with different personas and gain insights into selling the Intel AI portfolio through representative case studies applicable across industries.

Deep Learning

This course introduces deep learning and explains its techniques, terminology, and basic neural network architectures. Students will learn how to build, train, and apply models, including how to use pre-trained models to achieve optimal results.

Applied Deep Learning with TensorFlow

This course covers building models with TensorFlow, including basics like linear regression and gradient descent, and techniques like regularization and mini-batching. It also covers CNNs, TFRecords, and transfer learning. By the end of the course, you will have an understanding of network construction, kernels, and extending networks using transfer learning.

Natural Language Processing

This course covers natural language processing (NLP), including text manipulation, generation, and topic modeling. Students will learn string preprocessing techniques and the application of machine learning algorithms to text classification and other linguistic tasks.

Anomaly Detection

This course teaches how to use statistics and machine learning to detect anomalies, covering theory and techniques from basic to advanced levels. Students will learn to derive detection models, handle a variety of data types, and implement the models using Python labs.

Time Series Analysis

This course covers time series analysis including data smoothing, ARIMA models, Kalman filters, and Fourier transforms. It also covers deep learning techniques for sequential data. At the end of the course, you will have an understanding of time series theory, key concepts such as filters and signal transformations, and how to apply these techniques using Python.

Deep Learning for Robotics

This course teaches how to apply machine learning to robotics. It covers neural networks, LSTMs, and reinforcement learning, with a focus on obstacle detection, model training, and the use of simulation. Students will learn how to build deep learning systems using PyTorch.

AI on PC

This course focuses on deep learning inference on edge devices and teaches how to use Intel hardware and software for AI on the PC. Students will learn how to use Windows* Machine Learning, Intel Distribution for OpenVINO toolkit, and deep learning frameworks such as TensorFlow and ONNX.

Edge AI with Computer Vision

In this course, students learn how to use the Intel Neural Compute Stick 2 (Intel NCS2) for low-power deep learning on edge devices. Students will learn to install and configure the OpenVINO™ toolkit, create computer vision applications in Python, analyze model performance, and deploy models on the Intel NCS2 and Raspberry Pi.

Deep Learning Inference with Intel FPGAs

This course covers the deployment and acceleration of deep learning computer vision applications on CPUs and FPGAs. Students will learn about convolutional neural networks, the benefits of FPGAs, and using Docker and Kubernetes for scaling. At the end, students will understand how to build CNN-based applications, use the Intel FPGA Deep Learning Acceleration Suite, and target inference on Intel CPUs and FPGAs using the OpenVINO toolkit.

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