Machine Learning courses

1335 Courses

Pluralsight

Responsible AI for Developers: Fairness & Bias

Responsible AI for Developers: Fairness & Bias This course introduces you to the essential concepts of responsible AI and fundamental AI principles. Delve into practical techniques to identify fairness and uncover biases in AI/ML practices. Learn to mitigate these biases effectively. Explore an array of practical methods and tools designed to.

Complete Machine Learning and Data Science: Zero to Mastery

Complete Machine Learning and Data Science: Zero to Mastery Complete Machine Learning and Data Science: Zero to Mastery Learn Machine Learning (Artificial Intelligence), Python, Data Science, Data Analysis, Tensorflow, Pandas & more. All using the latest in AI! Categories: Artificial Intelligence Courses, Python Courses.

Coursera

Data Science for Professionals

Data Science for Professionals Data science has become indispensable across industries, driving informed decision-making and uncovering valuable insights from data. This course is designed for professionals seeking to leverage data science techniques and tools to extract meaningful information from data and drive organizational success. From dat.

LinkedIn Learning

Get Ready for Generative AI

Get Ready for Generative AI Course Title: Get Ready for Generative AI Description: Dive into the world of generative AI with this insightful course. Learn the fundamental concepts, explore new capabilities, and understand the emerging issues in the field of artificial intelligence. Provider: LinkedIn Learning Catego.

LinkedIn Learning

AI Engineering Essentials: Navigating the Tech Revolution

AI Engineering Essentials: Navigating the Tech Revolution | LinkedIn Learning Embark on a journey to master AI engineering with AI Engineering Essentials: Navigating the Tech Revolution hosted by LinkedIn Learning. This course imparts essential knowledge and key technical skills for those aspiring to excel as AI developers. Categories: Art.

LinkedIn Learning

LinkedIn AI Academy AI-100: 3 Scaling AI at LinkedIn

LinkedIn AI Academy AI-100: 3 Scaling AI at LinkedIn Discover the fundamentals of AI applications and their scaling process at LinkedIn by joining the third installment of the LinkedIn AI Academy AI-100 series. This course, provided by LinkedIn Learning, delves into essential AI techniques and their implementation. University: LinkedIn Learning.

LinkedIn Learning

Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference

Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference Enhance your understanding of machine learning and artificial intelligence with this foundational course on prediction, causation, and statistical inference. Ideal for professionals in the field, this course offers valuable insights to help you refine your mac.

LinkedIn Learning

GPT-4: The New GPT Release and What You Need to Know

LinkedIn Learning

Practical GitHub Copilot

Practical GitHub Copilot | LinkedIn Learning Course Title: Practical GitHub Copilot Description: Dive into a hands-on, advanced course dedicated to the practical applications of GitHub Copilot. Enhance your skills in artificial intelligence, machine learning, software development, and code generation. University: Provider: Linke.

LinkedIn Learning

Leading with a Digital Mindset in the Age of AI

Leading with a Digital Mindset in the Age of AI Learn how to become a digitally savvy people leader in the Age of AI. University: Provider: LinkedIn Learning Categories: Artificial Intelligence Courses Machine Learning Courses Digital Transformation Courses Change Management Courses Data Visualization Courses Data-Driven Decision.

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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!