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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

बुनियादीकौशल स्तर
2 hours
11.6K
An introduction to machine learning with no coding involved.

Track

Machine Learning Fundamentals in Python

16 hours
6.7K
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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मूल्यांकन करें

Machine Learning पाठ्यक्रमों और ट्रैक को ब्राउज़ करें

course

Feature Engineering for NLP in Python

विकसितकौशल स्तर
4 hours
525
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

course

Developing Machine Learning Models for Production

मध्यवर्तीकौशल स्तर
4 hours
521
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

course

Sentiment Analysis in Python

मध्यवर्तीकौशल स्तर
4 hours
504
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

course

Fraud Detection in Python

मध्यवर्तीकौशल स्तर
4 hours
480
Learn how to detect fraud using Python.

course

Unsupervised Learning in R

मध्यवर्तीकौशल स्तर
4 hours
476
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

course

Ensemble Methods in Python

विकसितकौशल स्तर
4 hours
428
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

course

Building Chatbots in Python

मध्यवर्तीकौशल स्तर
4 hours
423
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

course

Machine Learning with Tree-Based Models in R

बुनियादीकौशल स्तर
4 hours
418
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

course

Monitoring Machine Learning Concepts

मध्यवर्तीकौशल स्तर
2 hours
409
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

मध्यवर्तीकौशल स्तर
4 hours
392
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

Machine Learning with caret in R

मध्यवर्तीकौशल स्तर
4 hours
372
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

course

Fully Automated MLOps

मध्यवर्तीकौशल स्तर
4 hours
356
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

विकसितकौशल स्तर
3 hours
346
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

मध्यवर्तीकौशल स्तर
3 hours
331
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

course

MLOps for Business

बुनियादीकौशल स्तर
3 hours
238
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

course

Machine Learning for Marketing in Python

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

course

Modeling with tidymodels in R

मध्यवर्तीकौशल स्तर
4 hours
199
Learn to streamline your machine learning workflows with tidymodels.

course

Machine Learning in the Tidyverse

मध्यवर्तीकौशल स्तर
5 hours
184
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

course

Advanced NLP with spaCy

मध्यवर्तीकौशल स्तर
5 hours
145
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

course

Feature Engineering in R

मध्यवर्तीकौशल स्तर
4 hours
140
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

course

Support Vector Machines in R

मध्यवर्तीकौशल स्तर
4 hours
134
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Machine Learning पर संबंधित संसाधन

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Ready to apply your skills?

Projects allow you to apply your knowledge to a wide range of datasets to solve real-world problems in your browser

    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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