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Machine Learning courses

Machine learning courses cover algorithms and concepts for enabling computers to learn from data and make decisions without explicit programming. Build your skills in NLP, deep learning, MLOps and more.

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Recommended for Machine Learning beginners

Build your Machine Learning skills with interactive courses, curated by real-world experts

Course

Understanding Machine Learning

БазовыйУровень мастерства
4.5+
12.6K
2 hours
An introduction to machine learning with no coding involved.

Track

Machine Learning Fundamentals in Python

6.5K
16 hours
Learn the art of Machine Learning and come away as a boss at prediction, pattern recognition, and the beginnings of Deep and Reinforcement Learning.

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Просмотрите курсы и направления обучения по Machine Learning

Course

Developing Machine Learning Models for Production

СреднийУровень мастерства
4.5+
549
4 hours
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

Course

Unsupervised Learning in R

СреднийУровень мастерства
4.7+
537
4 hours
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Course

Introduction to TensorFlow in Python

СреднийУровень мастерства
4.3+
536
4 hours
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Course

Machine Learning with caret in R

СреднийУровень мастерства
4.7+
514
4 hours
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Course

Fraud Detection in Python

СреднийУровень мастерства
4.6+
486
4 hours
Learn how to detect fraud using Python.

Course

Ensemble Methods in Python

ПередовойУровень мастерства
4.7+
482
4 hours
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

Course

Supervised Learning in R: Regression

СреднийУровень мастерства
4.5+
479
4 hours
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Course

ARIMA Models in Python

ПередовойУровень мастерства
4.7+
470
4 hours
Learn about ARIMA models in Python and become an expert in time series analysis.

Course

Monitoring Machine Learning Concepts

СреднийУровень мастерства
4.6+
439
2 hours
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Course

Cluster Analysis in R

СреднийУровень мастерства
4.8+
424
4 hours
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

Course

Fully Automated MLOps

СреднийУровень мастерства
4.5+
381
4 hours
Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

Course

Monitoring Machine Learning in Python

ПередовойУровень мастерства
4.6+
356
3 hours
This course covers everything you need to know to build a basic machine learning monitoring system in Python

Course

Introduction to Data Versioning with DVC

СреднийУровень мастерства
4.5+
319
3 hours
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

Course

Building Chatbots in Python

СреднийУровень мастерства
4.6+
307
4 hours
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

Course

Modeling with tidymodels in R

СреднийУровень мастерства
4.6+
281
4 hours
Learn to streamline your machine learning workflows with tidymodels.

Course

Building Recommendation Engines with PySpark

ПередовойУровень мастерства
4.5+
277
4 hours
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Course

MLOps for Business

БазовыйУровень мастерства
4.7+
251
3 hours
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

Course

Machine Learning for Marketing in Python

СреднийУровень мастерства
4.4+
216
4 hours
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Course

Sentiment Analysis in R

СреднийУровень мастерства
4.5+
205
4 hours
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Course

Hyperparameter Tuning in R

ПередовойУровень мастерства
4.2+
203
4 hours
Learn how to tune your models hyperparameters to get the best predictive results.

Course

Machine Learning in the Tidyverse

СреднийУровень мастерства
4.7+
187
5 hours
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Course

Advanced NLP with spaCy

СреднийУровень мастерства
5
142
5 hours
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Course

Dimensionality Reduction in R

БазовыйУровень мастерства
4.5+
138
4 hours
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Дополнительные материалы по теме Machine Learning

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Ready to apply your skills?

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    Frequently asked questions

    Is machine learning easy to learn?

    DataCamp's beginner machine learning courses are a lot of hands-on fun, and they provide an excellent foundation for machine learning to advance your career or business. Within weeks, you'll be able to create models and generate predictions and insights. You'll also learn foundational knowledge of Python and R and the fundamentals of artificial intelligence.

    After that, the learning curve gets a bit steeper. Machine learning careers require a deeper understanding of statistics, math, and software engineering, all of which can be mastered at DataCamp.

    What is machine learning used for?

    In a nutshell, machine learning is a type of artificial intelligence whose algorithms, as they acquire data, produce analytical models and make predictions with little to no human intervention.

    It's difficult to find an industry that doesn't use machine learning. For example, marketers use machine learning to forecast returns on investments in marketing campaigns. Likewise, purchasing departments use machine learning to predict needed inventory.

    Businesses of all kinds use machine learning to predict customer behavior, map supply chains, and forecast revenues. Machine learning is used to predict health outcomes and to improve patient satisfaction. Machine learning helps scientists model climate change scenarios, including possible solutions.

    More specifically, machine learning is used in smart devices, search engines, and streaming services (when Netflix suggests a show or movie based on your viewing history, that's machine learning).

    What jobs can you get with machine learning skills?

    Machine learning skills are valuable in programming, data science, and other computer engineering disciplines. In addition, machine learning is a must for anyone wanting to work in robotics!

    Not all jobs that require machine learning are in tech though. For example, linguists use machine learning to track ever-changing languages and dialects. In addition, business departments, such as marketing, accounting, logistics, and purchasing, to name a few, increasingly need machine learning experts to help them make informed business decisions. Knowing machine learning can give you a step up in nearly any position, as modeling and predicting are critical business needs.

    Are machine learning skills in demand?

    Yes, machine learning skills are in high demand. According to a report by the World Economic Forum, demand for AI and ML specialists is expected to grow by 40% between 2023 and 2027.

    How much math do I need to take a machine learning course?

    If you're looking to develop a high-level understanding of machine learning concepts, you don't need much math. If you want to dive deeper and make machine learning your career (as opposed to an added value to your existing career), a foundation in statistics and algebra is helpful. If you don't have a mathematical background, that's okay. We'll teach you everything you need, and our instructors are a lot less scary than your high school calculus teacher.

    Do I need to download machine learning software to learn on DataCamp?

    You do not need to download anything while learning with DataCamp. All the tools we use are web-based.

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