Machine Learning courses

1736 Courses

Mastering Neural Networks and Model Regularization

Mastering Neural Networks and Model Regularization The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with r.

University of Pennsylvania

Deep Learning Essentials

Deep Learning Essentials | University of Pennsylvania | Coursera Join the University of Pennsylvania's Deep Learning Essentials course on Coursera. Delve into the rich history of deep learning, and gain a profound understanding of neural networks, including the perceptron. Discover how these networks function and explore the architectu.

Southern New Hampshire University

Generative AI and Model Selection

Generative AI and Model Selection | Southern New Hampshire University | Coursera Delve into the realm of generative AI and discover the intricacies of selecting the perfect model to suit your requirements with this hands-on course. You will develop a robust understanding of generative AI models, and examine various deployment options such as web.

Johns Hopkins University

Advanced Malware and Network Anomaly Detection

The course "Advanced Malware and Network Anomaly Detection" equips learners with essential skills to combat advanced cybersecurity threats using artificial intelligence. This course takes a hands-on approach, guiding students through the intricacies of malware detection and network anomaly identification. In the first two modules, you will gain fou.

Coursera

Building AI Cloud Apps with Microsoft Azure

Building AI Cloud Apps with Microsoft Azure Discover the comprehensive nine-course program, Building AI Cloud Apps with Microsoft Azure, designed to equip you with the skills to develop cutting-edge cloud and AI solutions using Microsoft Azure. Offered by Coursera, this program covers a wide range of topics including Azure Functions, A.

Coursera

Foundations and Core Concepts of PyTorch

Foundations and Core Concepts of PyTorch In this comprehensive course, you'll embark on a journey through the foundational elements and core concepts of PyTorch, one of the most popular deep learning frameworks. Starting with a detailed overview and system setup, you'll be guided through installing and configuring your environment t.

Coursera

Fundamentals of AI, Machine Learning, and Python Programming

Embark on a transformative learning experience designed to equip you with a robust understanding of AI, machine learning, and Python programming. This course begins with a thorough introduction to artificial intelligence and machine learning, demystifying the core concepts and exploring how algorithms and data-driven techniques empower compu.

Pluralsight

Information Extraction with Azure AI Document Intelligence

Information Extraction with Azure AI Document Intelligence Join the course "Information Extraction with Azure AI Document Intelligence" to master the Azure AI Document Intelligence service for processing documents, training models, and deploying in an on-premises network. In this comprehensive course, you'll learn: Techn.

Northeastern University

Practical AI for Professionals

Practical AI for Professionals Explore key ideas in Artificial Intelligence (AI) while delving into trending developments in the field. This course examines AI tools and frameworks to enable effective and efficient collaboration across technical and non-technical stakeholders. Analyze topics such as AI-enabled perception, representation, reason.

Southern New Hampshire University

The History and Relevance of the Rise of Generative AI

The History and Relevance of the Rise of Generative AI Dive into the fascinating journey of artificial intelligence, from its theoretical beginnings to today's powerful generative models. This course offers a unique perspective on how AI has transformed over decades, highlighting the crucial developments in deep learning that paved the way for mod.

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Machine learning use cases

The use of machine learning has touched many areas in our lives. Let's look at the most striking examples of the use of computer intelligence:

Voice assistant Alisa. This system, without human help, will always find the answer to any question of interest, turn on music, answer the call and much more.

Selecting relevant advertising in browsers is not an easy process for a person, but it is quick for a computer. The system will analyze the viewing data and select what a particular user will like. The same system is used on various marketplaces - products that a person viewed or purchased are offered.

Chatbots in various dialogues. For example, many banks use bots to solve customer problems. It's convenient and fast.

Data security: Machine learning models can identify data security vulnerabilities before they become breaches. By analyzing past experiences, machine learning models can predict future high-risk actions so that risk can be mitigated proactively.

Finance: Banks, trading brokers and fintech firms use machine learning algorithms to automate trading and provide financial advisory services to investors. Bank of America uses chatbot Erica to automate customer support.

Healthcare: Machine learning is used to analyze massive medical data sets to speed up the discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human errors. For example, IBM Watson uses data mining to provide doctors with data they can use to personalize patient care.

Fraud detection: AI is used in the finance and banking sector to autonomously analyze large numbers of transactions to identify fraudulent activities in real time. Technology services company Capgemini says fraud detection systems using machine learning and analytics reduce fraud investigation time by 70% and increase detection accuracy by 90% .

Retail: AI researchers and developers are using machine learning algorithms to develop AI recommendation engines that make relevant product suggestions based on shoppers' past choices, as well as historical, geographic and demographic data.

Facial recognition in the subway will help identify violators or criminals in a huge mass of people. Ordinary observers cannot cope with this task. But a fast-learning machine will do this job without any problems.

What do you need for machine learning (ML)?

For those interested in training, there are several requirements to be met in order to be successful in this field. So, there are the main points you need to know about the machine learning course. These requirements include:

Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.

Average knowledge of statistics and probability.

Basic knowledge of linear algebra in the ml course. In a linear regression model, a line is drawn through all the data points, and that line is used to calculate new values.

Understanding Calculus.

Knowledge of how to clean and structure raw data into the desired format to reduce the time required for decision making.

Machine learning courses from AI Eeducation are the best choice!