Best AI Training Courses

Looking to dive deep into the world of Artificial Intelligence? Look no further! We have compiled a comprehensive list of the best AI training courses available, carefully curated to help you gain the skills and expertise needed to excel in this exciting field.

With our collection of “Best AI Training Courses,” you’ll have access to top-notch educational resources that have been highly acclaimed by professionals and learners alike. From foundational concepts to advanced techniques, these courses cover a wide range of AI topics, including machine learning, deep learning, neural networks, natural language processing, computer vision, and more.

Whether you’re a novice eager to explore the basics or a seasoned practitioner seeking to expand your knowledge, these courses cater to learners of all levels. You’ll have the opportunity to learn from leading experts, engage in practical exercises, and work on real-world projects to reinforce your understanding.

By enrolling in these esteemed AI training courses, you’ll be equipped with the tools and skills necessary to stay at the forefront of AI innovation. Stay ahead of the curve, unleash your potential, and become a sought-after AI professional.

Don’t miss out on the opportunity to access the “Best AI Training Courses” available. Empower yourself in artificial intelligence today and take the first step towards a successful AI career.

Basics of Ai

Ai for Everyone (Andrew Ng)

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take.

In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

Developers/Coders

Artificial Intelligence A-Z™ 2023: Build an AI with ChatGPT4

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:

How to start building AI with no previous coding experience using Python
How to merge AI with OpenAI Gym to learn as effectively as possible
How to optimize your AI to reach its maximum potential in the real world

Here is what you will get with this course:

1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.

3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.

4. Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.

5. In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum.

CS50’s Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

Dive into Deep Learning

Interactive deep learning book with code, math, and discussions.

The course “Dive into Deep Learning” is an interactive deep learning book that provides a comprehensive exploration of deep learning concepts, accompanied by code, mathematical explanations, and discussions. The course material is implemented using popular deep learning frameworks such as PyTorch, NumPy/MXNet, JAX, and TensorFlow. It has been widely adopted by 400 universities across 60 countries.

The book is forthcoming on Cambridge University Press and has gained significant popularity, with the Chinese version becoming a best-seller in the IT category on the largest Chinese online bookstore. To stay updated with the latest developments, the course encourages following the open-source project of D2L.

Several updates and additions have been made to the course content. In December 2022, a JAX implementation was made available, introducing new topics like reinforcement learning, Gaussian processes, and hyperparameter optimization. In July 2022, a new API for implementation was introduced, along with additional topics covering generalization in classification and deep learning, ResNeXt, CNN design space, and transformers for vision and large-scale pretraining.

The course authors include Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola. Several chapter authors contributed expertise in various domains, such as reinforcement learning, Gaussian processes, hyperparameter optimization, recommender systems, and mathematics for deep learning. The course also credits Anirudh Dagar, Yuan Tang, and community contributors for their contributions to adapting the material to different deep learning frameworks.

Learn Machine Learning

This is the Curriculum for Learn Machine Learning in 3 months (PyTorch Curriculum) by Siraj Raval on Youtube. Beginners to Python will learn to build, train, deploy, scale & maintain modern Machine learning & Deep learning models. Each weekly assignment will teach you how to use a new concept or tool, like Docker, PyTorch, or Transformer Models. The Final Project will integrate everything you’ve learned into a Self Driving Car simulation. After completion, start an ML startup or find relevant work in the field. Together as a learning community, we’re going to help each other succeed!

Components

🤝 Social: Join our Discord channel to find a study buddy
✨ Interactive: Every resource is web-based with user input
🧑‍🎓 Beginner-Friendly: Build weekly projects without dependencies thanks to codespaces
🤖 Project-Based: Learn Computer Vision, Natural Language Processing, Time Series Forecasting, Audio Processing, & Recommender Systems

Tools Used

Python, Pip, Numpy, Pandas, Seaborn, Matplotlib, PyTorch, Replit, SQL, Jupyter, Streamlit, Gradio, HuggingFace, Airflow, GCP, AWS, Spark, Scikit-learn, Prometheus, Evidently, Grafana, Flask, Prefect, MongoDB, Postgres, Kafka, Terraform, RL-Baselines, Unity, W&B, Kubernetes, DBT

Learn PyTorch for Deep Learning: Zero to Mastery book

Learn PyTorch from scratch! This PyTorch course is your step-by-step guide to developing your own deep learning models using PyTorch. You’ll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you’ll have the skills and portfolio to get hired as a Deep Learning Engineer.

Neural Networks / Deep Learning

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Caltech Ai Training course

Artificial Intelligence course covers latest tools and technologies from the AI ecosystem, and features masterclasses by Caltech faculty and IBM experts, hackathons and Ask Me Anything sessions. Become an expert AI and ML professional with this PGP in AI & ML delivered in partnership with IBM.

Unlock your potential as an Artificial Intelligence and Machine Learning professional with industry-relevant AI courses. In this Artificial Intelligence course, you will learn about various AI-based technologies.

Artificial Intelligence is the next digital frontier, with profound implications for business and society. With the AI market expected to grow steadily over the years, this Artificial Intelligence course is perfect for those who want to stay ahead of the trend!

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Computer Science

Harvard University Computer Science for Artificial Intelligence

A broad and robust understanding of computer science and programming
Graph search algorithms
Reinforcement learning
Machine learning
Artificial intelligence principles
How to design intelligent systems
How to use AI in Python programs

The demand for expertise in AI and machine learning is growing rapidly. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, AI is transforming how we live, work, and play. This series will enable you to take the first steps toward understanding programming fundamentals so you can solve important real-world problems and future-proof your career.

This professional certificate series combines CS50’s legendary Introduction to Computer Science course with a new program that takes a deep dive into the concepts and algorithms at the foundation of modern artificial intelligence. This series will lead you through the most popular undergraduate course at Harvard, where you’ll learn the common programming languages, then carries that foundation through CS50’s Introduction to Artificial Intelligence with Python. Through hands-on projects, you’ll gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence.

By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever.

NLP – Natural Language Processing Courses

Hugging Face NLP Course

This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It’s completely free and without ads.

Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results on the Hub!
Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself.
Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used to tackle tasks in speech processing and computer vision. Along the way, you’ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply 🤗 Transformers to (almost) any machine learning problem!

This course:

Requires a good knowledge of Python
Is better taken after an introductory deep learning course, such as fast.ai’s Practical Deep Learning for Coders or one of the programs developed by DeepLearning.AI
Does not expect prior PyTorch or TensorFlow knowledge, though some familiarity with either of those will help

After you’ve completed this course, we recommend checking out DeepLearning.AI’s Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!

Natural Language Processing Specialization

Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October ’21.

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.

This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an .

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the . Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Stanford University Artificial Intelligence Programs

Now you can virtually step into the classrooms of Stanford professors who are leading the Artificial Intelligence revolution.

Our graduate and professional programs provide the foundation and advanced skills in the principles and technologies that underlie AI including logic, knowledge representation, probabilistic models, and machine learning.