30 Best Online Artificial Intelligence Courses To Boost The Skills On Your Resume

February 6th, 2019   |   Updated on April 16th, 2019

Artificial intelligence or AI as it is also known as is a giant leap for mankind. Irrespective of whatever business you are in basic knowledge of the same is going to give you the much-needed jump. Thankfully, learning about AI is not as a strenuous task as it seems.

You do not have to undergo courses of exceedingly long duration to be able to understand what goes behind it. Neither do you have to find universities which offer you the same course extensively.

There are many online courses which give a detailed idea of Artificial Intelligence, automation, and cognitive systems. However, the dilemma remains about which one is the best suited for your needs still remains.

To assist you in the choice we bring a list of online artificial intelligence courses that are not only free but help you accomplish the task in the quickest time possible. These courses cover everything ranging from the basics of Artificial intelligence to its advanced implementation in business procedures.

Some of these courses are targeted at people who wish to start coding as soon as they can. While others are aimed at users who do not have any kind of technical expertise and just need to understand why and how this technology can be useful for a layman.

Whatever your requirement is, you can analyze the features of each software and comprehend their ability to fulfill the same for you. Hopefully, if you are also planning to incorporate AI skills into your resume, check out these 30 best online AI courses, which you can learn from the comfort of your couch.

1. Data Science: Machine Learning

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Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

What You Will Learn

• The basics of machine learning
• How to perform cross-validation to avoid overtraining
• Several popular machine learning algorithms
• How to build a recommendation system
• What is regularization and why it is useful?

 

2. Building Chatbots Powered by AI

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3. Structuring Machine Learning Projects

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You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set a direction for your team’s work, this course will show you how.

Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. This provides “industry experience” that you might otherwise get only after years of ML work experience.

After 2 weeks, you will:

• Understand how to diagnose errors in a machine learning system, and
• Be able to prioritize the most promising directions for reducing error
• Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
• Know how to apply end-to-end learning, transfer learning, and multi-task learning

 

4. Machine Learning with TensorFlow on Google Cloud Platform Specialization

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What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now?

How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models and offer high-performance predictions.

Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems.

You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using the Google Cloud Platform.

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-On Project

Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.

Earn A Certificate

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

 

5. Robotics: Dynamics and Control

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Flying drones or robot manipulators accomplish heavy-duty tasks that deal with considerable forces and torques not covered by a purely robot kinematics framework. Learn how to formulate dynamics problems and design appropriate control laws.

In this course, part of the Robotics MicroMasters program, you will learn how to develop dynamic models of robot manipulators, mobile robots, and drones (quadrotors), and how to design intelligent controls for robotic systems that can grasp and manipulate objects.

We will cover robot dynamics, trajectory generation, motion planning, and nonlinear control, and develop real-time planning and control software modules for robotic systems. This course will give you the basic theoretical tools and enable you to design control algorithms.

Using MATLAB, you will apply what you have learned through a series of projects involving real-world robotic systems.

What you’ll learn

• The dynamics of robot arms, mobile robots and quadrotors
• Position and force control for robots
• How to generate complex trajectories
• The basics of configuration spaces for robotic systems
• Controller synthesis and stability

 

6. MIT – Deep Learning For Self Driving Cars

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The self-driving cars which are widely expected to become a part of our everyday lives rely on AI to make sense of all of the data hitting the vehicle’s array of sensors and safely navigate the roads. This involves teaching machines to interpret data from those sensors just as our own brains interpret signals from our eyes, ears and touch.

It covers the use of the MIT DeepTraffic simulator, which challenges students to teach a simulated car to drive as fast as possible along a busy road without colliding with other road users.

Introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. In the mean time, if interested,

Here Are A Few Things You Can Do:

• Sign up to the mailing list for updates.
• Visit MIT Deep Learning for the 3 related courses teaching this year.
• If you’re an MIT student, pre-register for 6.S091, 6.S093, and 6.S094 to receive credit.
• If you have questions, check out the FAQ Google Doc.
• Connect with Lex on Twitter, LinkedIn, Instagram, Facebook, or YouTube.
• Join Slack channel (deep-mit.slack.com). Get an invite by clicking here.
• If you would like to participate in the competitions, register an account on the site.
• Attend the lectures. Listeners are welcome. It’s open to all.

 

7. Artificial Intelligence A-Z™

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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.

 

8.  Artificial Intelligence: Reinforcement Learning In Python

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A complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning. When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’s AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario. Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially. Learning about supervised and unsupervised machine learning is no small feat. To date I have over sixteen(16!) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.

It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.

What’s Covered In This Course?

• The multi-armed bandit problem and the explore-exploit dilemma
• Ways to calculate means and moving averages and their relationship to stochastic gradient descent
• Markov Decision Processes (MDPs)
• Dynamic Programming
• Monte Carlo
• Temporal Difference (TD) Learning
• Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

 

9. Mathematics for Machine Learning: Multivariate Calculus

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This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster.

Next, we learn how to calculate vectors that point uphill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models.

This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

 

10. Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

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Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.

You’ve definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.

But What If You Could Also Become A Creator?

What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Sounds Too Good To Be True, Doesn’t It?

But there actually is a way.. Computer Vision is by far the easiest way of becoming a creator. And it’s not only the easiest way, but it’s also the branch of AI where there is the most to create.

Why? You’ll Ask.

That’s because Computer Vision is applied everywhere. From health to retail to entertainment – the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially. Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there’s a business opportunity which you can take advantage of. So now that raises the question: how do you break into the World of Computer Vision?

Up until now, computer vision has for the most part been a maze. A growing maze. As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost. On top of that, not only do you need to know how to use it – you also need to know how it works to maximise the advantage of using Computer Vision.

11. The Beginner’s Guide To Artificial Intelligence In Unity

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A practical guide to programming non-player characters for games.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you to create your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together.

In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award-winning books on games AI.

Through-out you will follow along with hands-on workshops designed to teach you about the fundamental AI techniques used in today’s games. You’ll join in as NPCs are programmed to chase, patrol, shoot, race, crowd and much more.

Learn How To Program And Work With:

• vectors;
• waypoints;
• navmeshes;
• the A* algorithm;
• crowds;
• flocks;
• animated characters, and
• vehicles.
• Contents and Overview

The course begins with a detailed examination of vector mathematics that sits at the very heart of programming the movement of NPCs. Following this system of waypoints will be used to move characters around in an environment before examining the Unity waypoint system for car racing with AI controlled cars.

This leads into an investigation of graph theory and the A* algorithm before we apply these principles to developing navmeshes and developing NPCs who can find their way around a game environment. Before an aquarium is programmed complete with autonomous schooling fish, crowds of people will be examined from the recreation of sidewalk traffic to groups of people fleeing from danger.

Having examined the differing ways to move NPCs around in a game environment their thinking abilities will be discussed with full explanations and more hands-on workshops in finite state machines and behaviour trees.

The follow-along workshops included in the course come with starter Unity asset files and projects complete with solutions. Throughout there are also quizzes and challenge exercises to reinforce your learning and to guide you to express your new found knowledge.

At the completion of this course you will have gained a broad understanding of what AI is in games, how it works and how you can use it in your own projects. It will equip you with a toolset to examine any of the techniques presented in more depth to take your game environments to the next level.

What Students Are Saying About This Course:

This has been my favourite Udemy-Unity course so far. It took me from literally 0% knowledge of how game AI is achieved, and took me to a whole new level. Waypoints, pathfinding, state machines, etc etc etc are all covered in-depth and will reveal the magic (spoiler alert: it isn’t magic) behind making your computer characters seem like they really have a mind of their own.

Oh My God. I love her way of teaching things. I haven’t finished this course yet. But all i can say is that it is another brilliant course from her. Artificial intelligence by itself is a tricky thing to do. And before starting this course i never thought that i will understand anything in it. But i was wrong. With her style of teaching, you will learn how to move your characters in an ”intelligent“ way. This course is perfectly sliced and the pace is wonderful.

Who This Course Is For:

• Anyone interested in learning how to program their own non-player characters.
• Anyone interested in seeing how artificial intelligence is applied in computer games.

 

12. Robotics: Locomotion Engineering

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How do robots climb stairs, traverse shifting sand and navigate through hilly and rocky terrain?

This course, part of the Robotics MicroMasters program, will teach you how to think about complex mobility challenges that arise when robots are deployed in unstructured human and natural environments.

You will learn how to design and program the sequence of energetic interactions that must occur between sensors and mechanical actuators in order to ensure stable mobility. We will expose you to underlying and still actively developing concepts, while providing you with practical examples and projects.

What you’ll learn

• The design and analysis of agile, bioinspired, sensorimotor systems
• How to develop simplified models of complex dynamic systems
• Ways to utilize simplified models to achieve dynamical mobility tasks

 

13. Artificial Intelligence For Business

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Solve Real World Business Problems with AI Solutions

Structure Of The Course:

Part 1 – Optimizing Business Processes
Case Study: Optimizing the Flows in an E-Commerce Warehouse
AI Solution: Q-Learning

Part 2 – Minimizing Costs
Case Study: Minimizing the Costs in Energy Consumption of a Data Center
AI Solution: Deep Q-Learning

Part 3 – Maximizing Revenues
Case Study: Maximizing Revenue of an Online Retail Business
AI Solution: Thompson Sampling

Real World Business Applications:

With Artificial Intelligence, you can do three main things for any business:

• Optimize Business Processes
• Minimize Costs
• Maximize Revenues

We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge.
In Part 1 – Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse.

In Part 2 – Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.

In Part 3 – Maximizing Revenues, we will build a different AI that will maximize revenue of an Online Retail Business, making it earn more than 1 Billion dollars in revenue!

But that’s not all, this time, and for the first time, we’ve prepared a huge innovation for you. With this course, you will get an incredible extra product, highly valuable for your career:

“a 100-pages book covering everything about Artificial Intelligence for Business!”.

14. Artificial Intelligence Website Creation 2018 (No Coding)

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Artificial Intelligence Tools Taught to Create Websites At Blazing Speed With No Experience. This game-changing course will cover artificial intelligence tools in website, chatbot design and analytics which will help you to create website in minutes.

I will teach you to easily create websites in the fastest time possible and customize your site look and feel according to your requirement in a simple drag-and-drop timeline by talking to chatbots.

Why learn this artificial intelligence game-changing course and how is this a differentiator?

This course can change your life as a web developer or marketer. With no coding experience, you can create amazing looking websites and pave the path for unlimited designs and interchange content and play god using artificial intelligence tech.

This course will save you a ton of time when it comes to creating websites without using any expensive website design tool and without using complex tools like wordpress etc. You do not even need to outsource websites to other agencies ever again as you can do it yourself now in minutes.

The question is “Are you ready to get into action and embrace the power to leverage artificial intelligence in website creation?”.

 

15. Artificial Intelligence I: Basics And Games In Java

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A guide on how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help to detect cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market.

Section 1:

• pathfinding algorithms
• graph traversal (BFS and DFS)
• enhanced search algorithms
• A* search algorithm

Section 2:

• basic optimization algorithms
• brute-force search
• stochastic search and hill climbing algorithm

Section 3:

• heuristics and meta-heuristics
• tabu search
• simulated annealing
• genetic algorithms
• particle swarm optimization

Section 4:

• minimax algorithm
• game trees
• applications of game trees in chess

Tic Tac Toe Game And Its Implementation

In the first chapter, we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.

The second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.

The last topic will be about minimax algorithm and how to use this technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree-like structures and so on. We will implement the tic-tac-toe game together in the end.

Thanks for joining the course, let’s get started!

Who This Course Is For:

This course is meant for students or anyone who interested in programming and have some background in basic Java

 

16. Launching into Machine Learning

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Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems.

We then discuss how to set up a supervised learning problem and find a good solution using gradient descent.

This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Course Objectives:

• Identify why deep learning is currently popular
• Optimize and evaluate models using loss functions and performance metrics
• Mitigate common problems that arise in machine learning
• Create repeatable and scalable training, evaluation, and test datasets

 

17. Artificial Intelligence II – Neural Networks in Java

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Hopfield networks, neural networks, backpropagation, optical character recognition, feedforward networks

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays.

In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.

Section 1:

• what are neural networks
• modeling the human brain
• the big picture

Section 2:

• Hopfield neural networks

Section 3:

• what is back-propagation
• feedforward neural networks
• optimizing the cost function
• error calculation
• backpropagation and resilient propagation

Section 4:

• the single perceptron model
• solving linear classification problems
• logical operators (AND and XOR operation)

Section 5:

• applications of neural networks
• clustering
• classification (Iris-dataset)
• optical character recognition (OCR)

In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them. If you are keen on learning methods, let’s get started!

Who This Course Is For:

This course is recommended for students who are interested in artificial intelligence focusing on neural networks

18. Artificial Intelligence Masterclass

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Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models.

Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right…

Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores.

This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution.

By enrolling in this course you will have the opportunity to learn how to combine the below models in order to achieve best performing artificial intelligence system:

• Fully-Connected Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Variational AutoEncoders
• Mixed Density Networks
• Genetic Algorithms
• Evolution Strategies
• Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
• Parameter-Exploring Policy Gradients
• Plus many others

Therefore, you are not getting just another simple artificial intelligence course but all in one package combining a course and a master toolkit, of the most powerful AI models. You will be able to download this toolkit and use it to build hybrid intelligent systems. Hybrid Models are becoming the winners in the AI race, so you must learn how to handle them already.

In addition to all this, we will also give you the full implementations in the two AI frameworks: TensorFlow and Keras. So anytime you want to build an AI for a specific application, you can just grab those model you need in the toolkit, and reuse them for different projects!

Don’t wait to join us on this EPIC journey in mastering the future of the AI – the hybrid AI Models.

 

19. Artificial Intelligence ( AI ) Quick-Start For Managers

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Introduction to Data Science, Machine Learning, Deep Learning & Neural Networks for Beginners with Scikit-Learn & Python.

Do You Want To Learn Artificial Intelligence Technology Quickly?

Are you a manager, director, or VP who needs to understand how AI works at a technical level? This fast-paced course explains the core concepts of Artificial Intelligence through engaging animations. In less than 2 hours you will be able to:

• Identify opportunities for using AI in your business
• Evaluate technical solutions
• Manage AI development projects
• Estimate resource requirements for your AI project
• Reuse pre-trained libraries to save cost
• … and lead your AI project to success.

Technology Explained In Simple Terms

The only background you need is 10th grade high-school Math.

You will be able to apply these algorithms in your own projects: kNN, Stochastic Gradient Descent, Regularization, Support Vector Machines, Random Forests, Classification with Sigmoids, Multi-Layer Neural Nets, Deep Learning with Convolutional Neural Networks and Recurrent Nets, and Natural Language Processing with Word-Embeddings.

Real-world Project:

You will build an AI system that detects cancer. The code is explained clearly line-by-line. No prior programming knowledge is required. This project is developed on Python with the Scikit-Learn library.

Experience:

The material in this course is built upon 15 years of my experience developing machine learning systems for industry projects. Everything is explained in such simple terms that you need only 10th grade high school math to understand it.

Enroll today & accelerate your career with AI.

 

20. Artificial Intelligence Music Creation & Remixing 2018

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Become a Music Star with No Music Knowledge with using just Artificial Intelligence tools to create music.

Giant Tech firms have developed AI software that can compose music on its own. So, the machines will be composing soundtracks using Artificial Intelligence.

This game-changing course introduces you to new-age technologies in Artificial Intelligence music creation to help you become a music star in no time.

Why Learn This Course And How Is This A Differentiator For Music Creators?

This course can change your life if you are a music composer. Because, we will tell you the most popular Artificial Intelligence Music Creation tools that can help you compose music tracks without you – having any music knowledge whatsoever.

We will also detail about the latest discovery tools in Music Mashups and also we will go through the complete tutorial of Adobe Amper and Jukedeck – great AI music assistants.

What’s More?

Welcome to the future as you will be able to know the chords behind every song and just hum to create music like popular artists. I meant – You can just hum to create songs in the style of the great artists.

The question is “Are you ready to get into action and embrace the power to leverage artificial intelligence in music creation in 2018?”.

If yes, plunge into action right away by signing up now.

There’s no time to waste. A mind-blowing experience is in store for you this moment.

Who This Course Is For:

• This course is a must for professionals who are in the music industry who want to use Artificial Intelligence (AI) technology for music composition
• This course is for anyone who is passionate about learning music and looking to create the same with Artificial Intelligence Technology
• This course is for anyone who is looking to upload unique and new music created with artificial intelligence technology on video platforms like YouTube for earnings

 

21. Machine Learning Foundations: A Case Study Approach

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Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

Learning Outcomes: By the end of this course, you will be able to:

• Identify potential applications of machine learning in practice.
• Describe the core differences in analyses enabled by regression, classification, and
  clustering.
• Select the appropriate machine learning task for a potential application.
• Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
• Represent your data as features to serve as input to machine learning models.
• Assess the model quality in terms of relevant error metrics for each task.
• Utilize a dataset to fit a model to analyze new data.
• Build an end-to-end application that uses machine learning at its core.
• Implement these techniques in Python.

 

22. Artificial Intelligence: History, Present and Future of AI

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Artificial Intelligence: non-coding introduction course to AI for total beginners – on history, present and future of AI

Hi and welcome to the introduction to artificial intelligence course. In this course we will talk about the past, present and the future of AI. This course covers all the introductory topics to AI to get you started on the path of becoming AI specialist. You will learn about main philosophy, history and approaches of AI as well as its applications.

Learn The Fundamental Consents Of Artificial Intelligence And Become Ready To Master The Field Of Ai

• Learn about the history and founding fathers of AI
• Learn about 4 types of AI and 3 main domains of AI technology
• Get familiar with the main fields of AI research and applications of artificial intelligence
• Learn about the basics of Neural Networks, Fuzzy Logic and Genetic Algorithms
• Learn the basics of Case-Based Reasoning, Bayesian networks and Behavior-Based approaches
• Know the main advantages and disadvantages associated with artificial intelligence
• Learn about the future possibilities and tangible projects with artificial intelligence
• Learn the fundamentals of AI

In this course we will talk about all that you need to know to get started in the field of AI. You will get familiar with the main approaches and research fields of artificial intelligence. You will know the advantages and disadvantages of AI as well as its possible applications in the future.

The course is split into 5 main sections starting from the history of AI. In this section we cover the basics and the history, next we will go into the present day applications of AI followed by the topics on the main categories and methods of AI. Lastly we will speak about cons and pros as well as the future of AI technology.

Who This Course Is For:

• Students studying Computer Science
• Students studying Computer Engineering
• Students studying Mathematics
• People interested in Technology
• People interested in Science
• People interested in Programming
• Professionals in STEM fields
• Programmers

 

23. Machine Learning Data Science Deep Learning Neural Networks

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Learn Machine Learning, Data Science, Neural Networks, Artificial Intelligence, Deep Learning and much more!

Do you want to become a Data Scientist? Are you willing to learn Machine Learning? Well you’re at the right place!! The average salary for a Machine Learning Engineer is $138,920 per year in the United States by Indeed.

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed ~ by Wikipedia.

Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.

Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions

1. Churn Analysis – it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth – but you must have the answers for questions like “Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?”

2. Customer Leads And Conversion – you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.

3. Customer Defections – make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.

Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live.

Insurance Agencies Across The World Are Also Able To Do The Following:

Predict the types of insurance and coverage plans new customers will purchase.

Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.

Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.

Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the outputs of machine learning – making your marketing message more effective across the board.

So In This Course Machine Learning, Data Science And Neural Networks + Ai We Will Discover Topics:

• Introduction
• Supervised Learning
• Bayesian Decision Theory
• Parametric Methods
• Multivariate Methods
• Dimensionality Reduction
• Clustering
• Nonparametric Methods
• Decision Trees
• McNemar’s Test
• Hypothesis Testing
• Bootstrapping
• Temporal Difference Learning
• Reinforcement Learning
• Stacked Generalization
• Combining Multiple Learners
• d-Separation
• Undirected Graphs: Markov Random Fields
• Hidden Markov Models
• Regression
• Kernel Machines
• Multiple Kernel Learning
• Normalized Basis Functions
• The Perceptron
• and much more!!

 

24. Yolo V3 – Robust Deep Learning Object Detection In 1 Hour

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The Complete Guide to Creating your own Custom AI Object Detection. Learn the Full Workflow – From Training to Inference.

Learn how we implemented YOLO V3 Deep Learning Object Detection Models From Training to Inference – Step-by-Step –

When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is that if you are just getting started learning about AI Object Detection, you may encounter some of the following common obstacles along the way:

• Labelling dataset is quite tedious and cumbersome,
• Annotation formats between various object detection models are quite different.
• Labels may get corrupt with free annotation tools,
• Unclear instructions on how to train models – causes a lot of wasted time during trial and error.
• Duplicate images are a headache to manage.

This got us searching for a better way to manage the object detection workflow, that will not only help us better manage the object detection process but will also improve our time to market.

Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you:

• Use AI to annotate your dataset,
• Annotation for one dataset can be used for other models (No need for any conversion) – Yolo, SSD, FR-CNN, Inception etc,
• Robust and Fast Annotation and Data Augmentation,
• Supervisely handles duplicate images.

You can Train your AI Models Online (for free) from anywhere in the world, once you’ve set up your Deep Learning Cluster.

So as you can see, that the features mentioned above can save you a tremendous amount of time. In this course, I show you how to use this workflow by training your own custom YoloV3 as well as how to deploy your models using PyTorch. So essentially, we’ve structured this training to reduce debugging, speed up your time to market and get you results sooner.

• In this course, here’s some of the things that you will learn:
• Learn the State of the Art in Object Detection using Yolo V3 pre-trained model,
• Discover the Object Detection Workflow that saves you time and money,
• The quickest way to gather images and annotate your dataset while avoiding duplicates,
• Secret tip to multiply your data using Data Augmentation,
• How to use AI to label your dataset for you,
• Find out how to train your own custom YoloV3 from scratch,
• Step-by-step instructions on how to Execute, Collect Images, Annotate, Train and Deploy Custom Yolo V3 models, and much more…
• You also get helpful bonuses:
• Neural Network Fundamentals
• Personal help within the course

I donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and I will do my best to help you. The office hours are free. I don’t try to sell anything.

Students can start discussions and message me with private questions. I answer 99% of questions within 24 hours. I love helping students who take my courses and I look forward to helping you.

I regularly update this course to reflect the current marketing landscape.

Get a Career Boost with a Certificate of Completion

• Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction.
• If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs.

Money-Back Guarantee

• The course comes with an unconditional, Udemy-backed, 30-day money-back guarantee. This is not just a guarantee, it’s my personal promise to you that I will go out of my way to help you succeed just like I’ve done for thousands of my other students.
• Let me help you get fast results. Enroll now, by clicking the button and let us show you how to Develop Object Detection Using Yolo V3.

Who This Course Is For:

• This course is for students with python, opencv or AI experience who want to learn how to do Object detection with Yolo V3.
• Those who do not need or already have a theoretical understanding of Object Detection, CNN’s and Yolo Architecture.
• Those who are looking for a practical only approach to Object Detection with Yolo V3.

25. If You Can Cook You Can Code Vol 5: Artificial Intelligence

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Get a complete tour of the AI landscape in late 2016 in plain English that you can understand even if you don’t code. 2016 was the year of AI.

Kevin Kelly has stated that AI is going to make even bigger changes to the economy than the internet. If you missed out on the internet dot com bubble of the late 90s and early 2000s, now is your chance. AI is coming, and it’s coming fast. And AI is also one of the most difficult things to learn. It’s not taught well.

Once you get past a Wikipedia article the next step is a 1000 page textbook that will take a year to read plus learning a bunch of new fields of math and programming.

This course is designed to give you a birds eye view of the entire field of AI, with many of the coolest and cutting edge breakthroughs included along the way.

You, Will, Learn What Ai Is Through 4 Stages:

The first is Measurement. This is the aspect of AI that involves getting information into the computer, the brain, the AI, so that it can be processed. It’s the process that the eyes and ears and other senses help serve for us humans and other animals who have various degrees of intelligence.

The next step is Learning. A key aspect of intelligence is that an intelligent person or animal or computer has to be able to learn over time so that it doesn’t keep making the same mistakes over and over. And ideally, the AI becomes something that can learn without a person always teaching it everything and looking over it’s shoulder. It should be able to learn on it’s own. That is where Machine Learning comes into play. You’ll get an in depth overview of Machine Learning with plenty of examples.

The third stage is Planning. This is the task the AI takes on where it has to decide what to do. It has to make a decision. It has to prepare to take action by looking at the various options and choosing the best one based on some kind of formula, with some metric of success to maximize. How does that all work? You’ll learn all about it here.

The final stage is action. How does the computer act in the real world. This is primarily the area of robotics. How do you create motors, also called “actuators” in AI parlance, that will make the AI more than just another window running on your computer? How do you create a self driving car or a robot that can walk around or express emotions on it’s “face” while in conversation? You’ll learn about how robots act in the real world, and some of the interesting state of the art applications in this section.

For those of you who have been following along in this series, you’re going to have a lot of breakthroughs here where you see some of the lower level ideas and concepts from the previous courses start to really help you understand at a deeper level how a lot of the newest technology actually works under the hood.

Who This Course Is For:

• Entrepreneurs who want to understand how AI works
• Programmers who want to see the bigger picture of how AI fits into the future of tech
• Professionals who want to prepare for the sea change that will come along with AI
• Students who want to prepare themselves for AI-related jobs

 

26. Applying Machine Learning to your Data with GCP

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Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course!

This 1-week, accelerated online course teaches participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

Prerequisites

• To get the most out of this course, participants must complete the prior courses in this specialization:
• Exploring and Preparing your Data
• Storing and Visualizing your Data
• Architecture and Performance

 

27. Deep Learning: Plunge into Deep Learning

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Learn to create Deep Learning models starting from basics Interested in the field of Machine Learning and Deep Learning? Then this course is for you!

This course is designed in a very simple and easily understandable content. You might have seen lots of buzz on deep learning and you want to figure out where to start and explore. This course is designed exactly for people like you!

If basics are strong, we can do bigger things with ease. My focus in this course is to build complicated things starting from very basics. In this course, I will cover the following things –

Session 1 – Introductory material on Deep learning, its applications and significance.

Session 2 – Introduces the fundamental building block of deep learning

Session 3 – Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models

Session 4 – Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem.

Session 5 – Convolution Neural Networks: Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.

Session 6 – Sequence Models: Recurrent Neural Networks, LSTMs

Are There Any Course Requirements Or Prerequisites?

Just some high school mathematics level.

Who This Course Is For:

• Anyone interested in Machine Learning and Deep Learning
• Students who have high school knowledge in mathematics and who want to start learning Deep Learning
• Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Machine Learning and Deep Learning
• Any people who are not satisfied with their job and who want to become a Data Scientist
• Any people who want to create added value to their business by using powerful Learning tools
• Build a foundation on the principles of Deep Learning to understand the latest trends

Who This Course Is For:

• Anyone interested in Machine Learning and Deep Learning
• Students who have high school knowledge in mathematics and who want to start learning Deep Learning
• Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Machine Learning and Deep Learning
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Learning tools.
• Build a foundation on the principles of Deep Learning to understand the latest trends

28. Artificial Intelligence #2: Polynomial & Logistic Regression

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Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python

In statistics, Logistic Regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable—that is, where the output can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick.

Cases, where the dependent variable has more than two outcome categories, may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model.

Logistic Regression was developed by statistician David Cox in 1958. The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor.

Polynomial Regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in X. Polynomial regression fits a nonlinear relationship between the value of X and the corresponding conditional mean of Y. denoted E(y |x), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics.

Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, Polynomial Regression is considered to be a special case of multiple linear regression.

The predictors resulting from the polynomial expansion of the “baseline” predictors are known as interaction features. Such predictors/features are also used in classification settings.

In this Course you learn Polynomial Regression & Logistic Regression You learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future output Next you go further You will learn how to classify output of model by using Logistic Regression

In the first section, you learn how to use python to estimate the output of your system. In this section you can estimate the output of:

• Nonlinear Sine Function
• Python Dataset
• Temperature and CO2

In the Second section, you learn how to use python to classify output of your system with nonlinear structure. In this section, you can estimate the output of:

• Classify Blobs
• Classify IRIS Flowers
• Classify Handwritten Digits

 

29. Artificial Intelligence & Machine Learning For Business

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The Ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs

Are you prepared for the inevitable AI revolution? How can you leverage it in your current role as a business leader (whether that’s a manager, team leader or a CxO)? Analytics Vidhya’s ‘Artificial Intelligence (AI) & Machine Learning (ML) for Business’ course, curated and delivered by experienced instructors, will help you understand the answers to these pressing questions.

Artificial Intelligence has become the centrepiece of strategic decision making for organizations. It is disrupting the way industries function – from sales and marketing to finance and HR, companies are betting on AI to give them a competitive edge.

AI for Business Leaders is a thoughtfully created course designed specifically for business people and does not require any programming.

Through this course you will learn about the current state of AI, how it’s disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it’s necessary for you to have a high-level overview of these topics in today’s data-driven world.

We will also provide you with multiple practical case studies towards the end of the course that will test your understanding and add context to all that you’ve studied.

By the time you finish the course, you will be ready to apply your newly-acquired knowledge in your current organization. You will be able to make informed strategic decisions for yourself and your business.

 

30. Quantum Computing: Theory To Simulation And Programming

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Understand the Fundamentals of a Quantum Computer and the Cirq quantum framework. Solve tasks on a real Quantum Computer

This course teaches the fundamentals of Quantum Computing including the basics of Quantum Physics and Quantum Simulations. This course is divided into 4 modules

Quantum Computing Basics: This section deals with the introduction to the wonderful world of Quantum Computing. The comparison between a Classical Computer and a Quantum Computer are explained.

Quantum Physics Section: This section deals with the introduction to the astronomically tiny world of the physics phenomenon that support quantum computers. Concepts like SuperPosition, Quantum Entanglement, Quantum Tunnelling are covered in this section. Quantum Physics has a strong connection with mathematics. In this section, the quantum phenomenon are explained by avoiding a lot of the mathematical jargon aiming towards providing a good grasp over the fundamental concept.

Cirq: This section deals with using Google’s Cirq framework in Python to design Quantum Circuits. A simulator called Quirk is also used which uses Silicon Hardware to emulate a Quantum Processor

Dwave Leap: This sections covers the Signing-Up for the Dwave-Leap service which enables anyone to start using a real Quantum Computer to solve real world problems. This Quantum Annealer will also be used to solve a Graph-Optimization problem.

Hope you have fun exploring the depths of Quantum Computing.