Machine Learning courses

1334 Courses

Udemy

2025 Machine Learning & Data Science for Beginners in Python

Welcome to the 2025 Machine Learning & Data Science for Beginners in Python course, brought to you by Udemy. Perfect for those eager to embark on their data science journey, this course covers a variety of essential projects. You will gain hands-on experience with techniques such as Linear Regression, Logistic Regression, Random Forest, Support.

Udemy

Machine Learning in GIS : Understand the Theory and Practice

Elevate your skills by exploring the synergies between Machine Learning and Geographic Information Systems (GIS) in this comprehensive course offered by Udemy. Delve into the principles and practical applications of Remote Sensing in QGIS and Google Earth Engine. This course is ideal for individuals eager to enhance their understanding of b.

Udemy

Decision Trees, Random Forests, Bagging & XGBoost: R Studio

Delve into the advanced world of Decision Trees and Ensembling techniques with our comprehensive course - Decision Trees, Random Forests, Bagging & XGBoost: R Studio, offered by Udemy. This course is specifically designed for those eager to enhance their understanding and application of R programming in machine learning. Throughout this course.

Udemy

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

Join our comprehensive course on Udemy to master Decision Trees and Ensembling techniques using Python. Delve into the world of machine learning and data analysis as you learn to implement Bagging, Random Forest, Gradient Boosting Machine (GBM), AdaBoost, and XGBoost efficiently. Perfect for data enthusiasts aiming to enhance their skills in.

Udemy

Complete A.I. & Machine Learning, Data Science Bootcamp

Dive into the world of A.I. and machine learning through this thorough Data Science Bootcamp. Master the essentials of data analysis and machine learning using Python, TensorFlow, and Pandas. Join our course, provided by Udemy, and leverage expert insights into deep learning and data visualization. This bootcamp caters to a wide range of inte.

Udemy

TensorFlow for Deep Learning Bootcamp

TensorFlow for Deep Learning Bootcamp | Udemy Enroll in the TensorFlow for Deep Learning Bootcamp offered by Udemy and elevate your skills in Artificial Intelligence, Machine Learning, and Deep Learning. This comprehensive course is designed to provide you with hands-on experience and in-depth knowledge using TensorFlow, Google's powerful open-so.

Udemy

Linear Regression and Logistic Regression using R Studio

Discover the foundational concepts of Linear Regression and Logistic Regression tailored for beginners in R Studio. This course provides a clear understanding of the distinctions between Regression and Classification, equipping you with essential skills for data analysis and visualization. Offered by Udemy, this course is perfect for those l.

Udemy

TensorFlow: Machine Learning e Deep Learning com Python

Udemy

Deep Learning with TensorFlow (beginner to expert level)

Coursera

Microsoft Azure for AI and Machine Learning

This comprehensive course immerses you in Microsoft Azure's AI and Machine Learning services, offering valuable hands-on experience. Develop the skills to set up, manage, and troubleshoot Azure-based AI & ML workflows. The curriculum addresses the entire ML lifecycle in Azure, spanning from data preparation to model deployment and monitoring..

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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!