According to PwC’s recent report, Artificial intelligence will boost the global GDP by 14 percent (equivalent to $15.7 trillion) higher in 2030. No doubt, this makes it the most significant commercial opportunity in today’s economy. Besides, Artificial intelligence is the new horizon for application developers, which opens up a world of possibilities. Artificial intelligence, backed by machine learning and deep learning, helps produce better user profiles, recommendations, personalization, or incorporate smarter search. It also leverages a voice interface or intelligent assistance to improve an app. This also enables us to build human-like applications that can see, hear, and react.
Artificial intelligence is being used in a myriad of applications across industries. We are witnessing its implementation in every business. Learning AI can therefore open a world of opportunities for anyone. A combination of Artificial Intelligence, Machine learning, and deep learning can chart out a way to great career prospects. Although learning artificial intelligence is not very easy, however, it is now accessible through a variety of training and courses available online and offline. Best educators, experts, and researchers are leveraging the courses at an affordable cost. Some of these classes are very comprehensive and include a curriculum of an equivalent college degree. The good news is some of these are even available for free and are perfect to get a glimpse into the world of AI.
In this blog, we have mentioned about 5 best artificial intelligence courses, classes, certifications, training programs, and tutorials available online that help you to prepare a good ground in the field of AI.
Related post – 10 Best Online Artificial Intelligence certifications
Best artificial intelligence courses
1.Machine Learning by Stanford University – Coursera
Created by Andrew Ng- the most renowned expert in AI and Machine Learning, co-founder of Coursera, this machine learning course has gained immense popularity among students and professionals that counts around 3.5 million. 93% of them have given it a 5-star rating. Undoubtedly, AI experts often cite this course as the single most important resource for anyone looking to learn AI and ML. This course learn you about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Topics covered
- Introduction
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR
Key Highlights
- This course rated highest among the other available online courses.
- The course is ideal for beginners in the field of artificial intelligence and machine learning
- It covers the most effective machine learning techniques, and gain practice implementing them
- Gain the practical know-how needed to quickly and powerfully apply ML techniques to new real life situations and problems
- The course is free and you can get a paid certificate for showcasing your learning of AI and ML skills
Prerequisites
- A Bachelor’s degree with a 3.0 or higher grade point average.
- Understanding of advanced probability.
- Advanced statistics and advanced linear algebra.
- Experience with programming in C/C++, Java, Python, or other similar languages
2.Artificial Intelligence A-Z™: Learn How To Build An AI (Udemy)
Combine the power of Data Science, Machine Learning, and Deep Learning to create powerful AI for Real-World applications!
Topics covered
- Build an AI
- Understand the theory behind Artificial Intelligence
- Make a virtual Self Driving Car
- Make an AI to beat games
- Solve Real World Problems with AI
- Master the State of the Art AI models
- Q-Learning
- Deep Q-Learning
- Deep Convolutional Q-Learning
Key highlights
- Artificial Intelligence, it’s working, and its uses.
- Artificial Intelligence Designs.
- Intuition Q-learning, Deep Q-learning, and Deep Convolutional Q-learning.
- Learning to work with A3C.
- Control advanced AI models.
- Build Virtual Self-driving cars.
- AI programming to test games and defeat them.
- Actively solving real problems in the world by using various AI designs.
Prerequisites:
- Basic knowledge of python and high school mathematics.
3. Artificial Intelligence Nanodegree Programs (Udacity)
Udacity’s School of Artificial Intelligence offers many Nanodegree programs. Nanodegrees are very extensive programs comprising of a larger course of study, usually presented in partnership with leading companies or universities. For those who want to make a career in AI, there are some excellent, powerful, career-centered programs that can be very helpful to advance in the field of AI by spending as little as 8-10 hours per week. There are choices for every level of knowledge and experience from complete beginner focussed programs to those intended for more advanced learners.
Programs covered
- Machine Learning Engineer for Microsoft Azure
- AI for Healthcare
- Intel® Edge AI for IoT Developers
- Intro to Machine Learning with TensorFlow
- AI Product Manager
- Intro to Machine Learning with PyTorch
- AI programming with Python
- Artificial intelligence for Trading
- Computer Vision
- Natural Language Processing
- Deep Reinforcement Learning
- Artificial Intelligence
- Deep LearningProgramming
- AI for Business Leaders
- Machine Learning DevOps Engineer
- Digital Freelancer
- AWS Machine Learning Engineer
- AI Engineer using Microsoft Azure
Key highlights
- Curriculum designed and delivered by industry experts
- Get practical experience by applying your skills to code exercises and projects
- Get 1-on-1 technical mentor support
- Personal career coach also available for career path guidance
- Complete flexibility with timelines and schedule
Link of the program
4. IBM AI Engineering Professional Certificate –Coursera
If you want to launch your career as an AI engineer course this learns you how to provide business insights from big data using machine learning and deep learning techniques. You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
Courses covered
- Machine learning with Python
- Introduction to Deep Learning & Neural Networks with Keras
- Introduction to Computer Vision and Image Processing
- Deep Neural Networks with PyTorch
- Building Deep Learning Models with TensorFlow
- AI Capstone Project with Deep Learning
Key highlights
- Curriculum designed by a panel of top IBM experts in the field
- Understand machine learning algorithms including classification, regression, clustering, and dimensional reduction
- Deploy machine learning algorithms and pipelines on Apache Spark
- Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
- Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
- Be able to build, train, and deploy different types of deep architectures
- Demonstrate ability to present and communicate outcomes of deep learning projects
- Option to audit all courses at no charge; verified certificate and IBM badge can be earned at a low monthly fee
Pre-requisites
- Working knowledge of Python and Jupyter Notebooks (Don’t have these skills? Try taking the Python for Data Science and course)
- High school mathematics or math for machine learning
- It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:
IBM Data Science Professional Certificate
IBM Applied AI Professional Certificate
5. AI for everyone – By Andrew Ng – Coursera
As mentioned in Coursera – 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.
Topics covered
- What is AI?
- Building AI Projects
- Building AI In Your Company
- AI and Society
Key Highlights
- Highest rated Coursera Artificial Intelligence online course
- Understand the meanings of various concepts in artificial intelligence and machine learning
- Learn how to work better with an AI team in your organization
- Learn how to chose an AI project
- Get a glimpse into the technical tools used by AI teams
- Case studies related to building an AI product and strategy
- No prerequisites, can be taken by anyone at any level of experience