Data Science Courses

897 Courses

YouTube

Know When You Know: Handling Adversarial Data by Abstaining

Explore sequential prediction in stochastic settings with adversarial interference. Learn strategies for handling distribution shifts and making confident predictions while abstaining from uncertain cases.

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YouTube

Gender Bias in Machine Learning

Explore gender bias in machine learning with Shalvi Mahajan. Uncover AI's role in amplifying biases, real-world challenges, and evolving techniques to address them in product design and services across genders.

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YouTube

Practice Machine Learning for Petroleum Engineers with Little to No Code

Explore machine learning applications in petroleum engineering with minimal coding. Gain practical skills for implementing ML solutions in reservoir analysis and production optimization.

On-Demand

YouTube

Networks that Adapt to Intrinsic Dimensionality Beyond the Domain

Explore neural networks' ability to adapt to intrinsic dimensionality, focusing on ReLU networks approximating functions with dimensionality-reducing feature maps. Gain insights into manifold learning and data analysis.

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Calling the Shot: How AI Predicted Fusion Ignition Before It Happened

Explore how AI predicted fusion ignition at the National Ignition Facility, revolutionizing clean energy research and bringing us closer to harnessing the power of the stars.

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Integrating Cyber Security and Machine Learning for Applications in Transportation Systems

Explore integrating cybersecurity and machine learning in transportation systems. Learn about challenges, research, and innovative solutions for securing Internet of Transportation and Intelligent Transportation Systems.

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Ontologies, Graph Deep Learning, and AI in Materials Science

Explore ontologies, graph deep learning, and AI in materials science, focusing on advanced manufacturing and synchrotron science. Learn about innovative approaches for multimodal analysis and decision-making.

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YouTube

Lowering the Entry Threshold for Neural Vector Search - Applying Similarity Learning

Explore similarity learning for efficient neural search implementation, reducing data requirements and training time while addressing domain-specific challenges.

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Relevance in the Age of Generative Search - Haystack US 2023 Keynote

Explore the integration of generative AI in search, covering strategies, code examples, and the balance between AI capabilities and traditional relevance techniques for accurate results.

On-Demand

YouTube

Relevance in the Age of Generative Search - Haystack US 2023 Keynote

Explore strategies for integrating generative AI and language models into search applications, balancing accuracy and relevance while leveraging new capabilities in the evolving search landscape.

On-Demand

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Learning Paths for Career Advancement

Data Scientist works with company data, analyses it, looks for potential dependencies, draws conclusions on this basis and, if necessary, builds visualisations. To solve such tasks, the specialist uses mathematical algorithms, development tools and special programmes. A Data Scientist understands how to build a forecast and helps to make the right decisions.

This specialist uses Data Science methods to process large amounts of information. He builds and tests models of data behaviour. This is how he finds patterns in them and predicts future values. For example, knowing everything about the demand for a product earlier, Data Scientist helps the company to make a forecast about sales in the near future. All models are built thanks to machine learning algorithms.

Usually Data Scientists become for the following reasons:

There is a desire to learn a promising and highly paid profession.

There is experience in a related industry, but you want to move to a new, more demanded direction. Data Scientists are often programmers, marketers, financiers and business analysts.

In professional activities or scientific research, it is necessary to apply innovative technologies: big data, neural networks, artificial intelligence.

To work in Data Science you need find best data science and ai courses for improving programming skills and knowledge of maths beyond the school curriculum.

Course Benefits and Features

Why choose online artificial intelligence and data science courses?

Classes are taught by experienced programmers and analysts who explain complex material, such as probability theory or mathematical analysis in an easy-to-understand way with examples.

Recordings of all lessons are stored in your personal account. You will not have gaps in your knowledge, because webinars can be watched repeatedly at any time. The videos will stay with you forever.

After each topic there is a practical task. The tasks gradually become more complex, and by the end of the course you will build a neural network or recommendation system. That is, you will have projects ready for your portfolio.

If you cannot cope with a question on your own, the tutor will help you. He will point out mistakes and give recommendations.

Many schools help with finding a job for free, and also invite students for internships, so you will have the opportunity to gain experience in a real project.

Excellent knowledge of maths, statistics, programming languages, English, as well as creativity, communication and critical thinking: employers are willing to pay more for specialists with this set of skills.

How to Enroll in Data Science and AI Courses?

Let's tell you in detail what steps you need to go through to become a Data Science specialist:

Study mathematics and linear algebra. If you have knowledge within the school curriculum, you can start with books that explain the basic concepts in simple language: derivative, differential, matrix and so on.

Any analytics uses mathematical statistics and probability theory - these are the next big topics you should familiarise yourself with.

Working in Data Science is impossible without knowledge of programming languages. For a beginner, Python is suitable - it is relatively simple, flexible and feature-rich.

The next step is to learn machine learning algorithms: "with a teacher", "without a teacher", "with reinforcement". You need to learn how to collect data for analysis and visualise it.

Having sorted out the theory, move on to practice. For example, you can look for an assistant position or an internship in large IT companies!

Data Science Courses

Know When You Know: Handling Adversarial Data by Abstaining

Gender Bias in Machine Learning

Practice Machine Learning for Petroleum Engineers with Little to No Code

Networks that Adapt to Intrinsic Dimensionality Beyond the Domain

Calling the Shot: How AI Predicted Fusion Ignition Before It Happened

Integrating Cyber Security and Machine Learning for Applications in Transportation Systems

Ontologies, Graph Deep Learning, and AI in Materials Science

Lowering the Entry Threshold for Neural Vector Search - Applying Similarity Learning

Relevance in the Age of Generative Search - Haystack US 2023 Keynote

Relevance in the Age of Generative Search - Haystack US 2023 Keynote

How to train in Data Science and become one from scratch?

Learning Paths for Career Advancement

Course Benefits and Features

How to Enroll in Data Science and AI Courses?