Machine Learning courses

1775 Courses

Coursera

Chatbots for Beginners: A Complete Guide to Build Chatbots

Chatbots for Beginners: A Complete Guide to Build Chatbots Embark on a journey to master chatbot development with this detailed course designed for beginners. Starting with an introduction to chatbots, you’ll learn their history, applications, and benefits. Understand the differences between rule-based and self-lear.

Johns Hopkins University

Training AI with Humans

In the course "Training AI with Humans," you'll explore the intersection of machine learning and human collaboration, focusing on enhancing AI performance through effective data annotation and crowdsourcing. You will gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in us.

Johns Hopkins University

Securing AI and Advanced Topics

Securing AI and Advanced Topics In the course "Securing AI and Advanced Topics", learners will delve into the cutting-edge intersection of AI and cybersecurity, focusing on how advanced techniques can secure AI systems against emerging threats. Through a structured approach, you will explore practical applications, including fraud prevention usi.

Johns Hopkins University

Chatbots

Chatbots The "Chatbots" course offers a comprehensive dive into the world of chatbots, equipping learners with the skills necessary to design, build, and optimize conversational interfaces. Explore the evolution of chatbot technology and grasp the fundamental mechanics that drive their functionality. Engage in hands-on projects using Amazon Lex.

Coursera

Keras Deep Learning & Generative Adversarial Networks (GAN)

Keras Deep Learning & Generative Adversarial Networks (GAN) This course is designed to take you on an in-depth journey through the world of deep learning and artificial intelligence. Beginning with an introduction to AI and machine learning concepts, you’ll build a solid foundation in neural networks and deep learning with the Keras framework. As.

Coursera

Introduction to RNN and DNN

Introduction to RNN and DNN Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and.

Coursera

RNN Architecture and Sentiment Classification

Title: RNN Architecture and Sentiment Classification Description: Artificial Intelligence is revolutionizing data analysis. This course delves into Recurrent Neural Networks (RNNs), starting with basic memory models and advancing to deep RNN structures. You'll explore RNN models like ManyToMany, ManyToOne, and OneToMany through practical exerci.

Coursera

Intermediate Data Manipulation and Machine Learning

Intermediate Data Manipulation and Machine Learning | Coursera In this comprehensive course, you will explore artificial intelligence (AI) and its core concepts, forming a solid foundation for machine learning. You will delve into regression analysis, applying univariate, polynomial, and multivariate regression techniques to real-wo.

Johns Hopkins University

AI for Cybersecurity

AI for Cybersecurity This Specialization is designed for post-graduate students aiming to master AI applications in cybersecurity. Through three comprehensive courses, you will explore advanced techniques for detecting and mitigating various cyber threats. The curriculum covers essential topics such as AI-driven fraud prevention, malware analysis.

Johns Hopkins University

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.

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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!