Machine Learning courses

1788 Courses

Coursera

AWS AI Practitioner

The AWS Certified AI Practitioner Specialization is meant for those who desire to establish a solid base in Artificial Intelligence (AI) and Machine Learning (ML) and have the ability to utilize AWS cloud services. This specialization is aligned with the AWS AI Practitioner Certification exam and offers a broad understanding of AI concepts, generat.

CodeSignal

Q-Learning Unleashed: Building Intelligent Agents

In this course, we focus on building a Q-learning agent step by step. We start with the Bellman equation and the Q-table update, then implement a basic Q-learning function. Next, we incorporate an exploration policy (ε-greedy), and finally we demonstrate how to use the learned Q-table for decision making.

edX

Azure AI Services: Core applications

"Core Applications of Azure AI Services" is an introductory course designed to familiarize users with the practical application of Microsoft's Azure AI services in various real-world scenarios. It aims to bridge the gap between theoretical AI concepts and their actual implementation. You will learn how Azure AI services are utilized to solve real-.

Coursera

Generative AI in Finance

Course Description: This course provides a comprehensive exploration of Generative AI and its transformative impact on the financial sector. Learners will delve into foundational concepts, key AI models, and their applications across financial analysis, decision-making, forecasting, and compliance. With a focus on real-world case studies and hands-.

XuetangX

Big data analytics and smart rehabilitation

Course BackgroundBig data analysis and smart rehabilitation have become key trends in modern rehabilitation medicine. By leveraging advanced computing technologies, artificial intelligence (AI) algorithms, and Internet of Things (IoT) devices, healthcare professionals can assess patients’ functional status more accurately, optimize rehabilita.

Coursera

Introduction to AI and Machine Learning

Welcome to the Introduction to AI and Machine Learning Course! This course is the first course of the 3 courses specialization - AWS Certified AI Practitioner. A journey into the world of Artificial Intelligence (AI) and Machine Learning (ML) with AWS begins with this course. This is perfect start whether you are a beginner or want to brush up on y.

Distributed Computing & AI with Spark

You’ll get hands-on experience in building, training, and customizing models using TensorFlow and Keras. Starting from implementing simple networks by hand, this course builds to making use of advanced pre-trained models with transfer learning. You’ll complete a practical mini-project on image classification, with the help of guidance during office.

Generative AI For Business Leaders

Ideal for those without an analytics or data background, you’ll get a foundational understanding of how the technology works and how to use it to meet business objectives. By the end of this course you’ll be able to weigh the benefits of AI and gAI, as well as understand the limitations, tradeoffs, and legal and ethical implications.

AI and Machine Learning

Master AI and machine learning with instructor support. This hands-on course teaches you to build ML pipelines, analyze data and drive innovation.

Data Wrangling with Python

Master data wrangling with Python in hands-on, self-paced course and live instructor support. Learn how to make messy data usable.

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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!