Machine Learning courses

1336 Courses

Coursera

Intro to Snowflake for Devs, Data Scientists, Data Engineers

Intro to Snowflake for Devs, Data Scientists, Data Engineers Unlock the potential of Snowflake with our comprehensive course designed for developers, data scientists, and data engineers. This course introduces you to Snowflake as a powerful platform for building applications, creating data pipelines, and developing AI models and workflows. You'll.

Pluralsight

Introduction to Machine Learning with ENCOG 3

Introduction to Machine Learning with ENCOG 3 This machine-learning course is dedicated to the implementation and applications of various methods using ENCOG 3. Initiate your free trial today! While the domain of machine learning is extensive, this course narrows its focus to neural networks. We begin by building a foundation, explaining machin.

Pluralsight

Advanced Machine Learning with ENCOG

Advanced Machine Learning with ENCOG In this course, you will delve into advanced topics related to machine learning, focusing on enhancing the accuracy of neural network predictive models. Explore the different types of neural networks and their implementations using the open-source machine learning framework ENCOG. Are you concerned about you.

LinkedIn Learning

Data Science Foundations: Knowledge Graphs

Data Science Foundations: Knowledge Graphs Enhance your information management skills with our comprehensive course on knowledge graphs. Available on LinkedIn Learning, this course delves into various knowledge graph tools, techniques for networking databases, and methods to construct a robust digital knowledge base. University: Provider: Lin.

Coursera

MathWorks Computer Vision Engineer

MathWorks Computer Vision Engineer Prepare for a career in the rapidly expanding field of computer vision. The ability to extract meaningful information from visual data is crucial for efficiently developing smart monitoring systems, enhancing medical diagnostics, and powering the next generation of autonomous vehicles. This program is designed.

Coursera

Generative AI - Your Personal Code Reviewer

Generative AI - Your Personal Code Reviewer Generative AI - Your Personal Code Reviewer Dive into the forefront of coding evolution with our comprehensive course, "Generative AI - Your Personal Code Reviewer." This immersive course is tailored for learners seeking to unlock the full potential of generative AI in the world of code review and.

Coursera

Responsible AI for Developers: Privacy & Safety

Join the Responsible AI for Developers: Privacy & Safety course to delve into critical aspects of AI privacy and safety. This comprehensive course covers practical methods and tools for implementing recommended practices in AI privacy and safety using Google Cloud products and open-source tools. Enhance your understanding and skills in AI et.

Coursera

Fundamentals of Generative AI for Beginners

Fundamentals of Generative AI for Beginners Generative AI is a disruptive technology with immense impact. It is pushing businesses to rethink customer strategies, encouraging chipmakers to meet increased processor demands, and leading academia to alter learning paths and curriculums across various fields of study. This course introduces you to.

Coursera

Introduction to Generative AI - Art of the Possible

Introduction to Generative AI - Art of the Possible | Coursera The Introduction to Generative AI - Art of the Possible course introduces the concept, use cases, and the importance of generative AI in a business context. This course focuses on business leaders and other decision-makers currently or potentially involved in generative AI projects. B.

Udemy

Optical Character Recognition (OCR) MasterClass in Python

Optical Character Recognition (OCR) MasterClass in Python Enroll in the OCR MasterClass in Python offered by Udemy and master the art of Optical Character Recognition. This comprehensive course covers essential tools and technologies such as OpenCV, Pytesseract, Pillow, and Machine Learning techniques, ensuring you gain practical and in-depth kn.

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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!