5 Best Free Online Courses On Artificial Intelligence

Artificial Intelligence (AI) is about having human like capabilities such as understanding natural language, speech, vision. It will define the next generation of learning. There are many top universities around the world which provide free online courses on artificial intelligence.

Have a look at the five best free online courses that which give you basics as well as advanced applications of AI.

1. Learn From ML Experts At Google

Learn From ML Experts At Google

Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects.

Syllabus

  • Testing and Debugging in Machine Learning
  • Introduction to Machine Learning Problem Framing
  • Data Preparation and Feature Engineering in ML
  • Machine Learning Crash Course

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2. Intro To Deep Learning: Google Via Udacity

Udacity

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll get an overview of what deep learning is all about.

Partnering with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team, we’ll teach you how deep learning builds on machine learning. Then you’ll get a chance to learn more about deep neural networks and advanced architectures such as convolutional networks and recurrent networks.

And if you’d like to dive even deeper into this cutting-edge field, we recommend that you continue your studies with our full-fledged Deep Learning Nanodegree program to get more hands-on experience.

Syllabus

Lesson 1: From Machine Learning to Deep Learning

  • Understand the historical context and motivation for Deep Learning.
  • Set up a basic supervised classification task and train a black box classifier on it.
  • Train a logistic classifier “by hand”Optimize a logistic classifier using gradient descent, SGD, Momentum and AdaGrad.

Lesson 2: Deep Neural Networks

  • Train a simple deep network.
  • Effectively regularize a simple deep network.
  • Train a competitive deep network via model exploration and hyperparameter tuning.

Lesson 3: Convolutional Neural Networks

  • Train a simple convolutional neural net.
  • Explore the design space for convolutional nets.

Lesson 4: Deep Models for Text and Sequences

  • Train a text embedding model.
  • Train an LSTM model.

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3. Machine Learning Offered By Stanford Through Cursera

Machine Learning Offered By Stanford Through Cursera

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn 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, datamining, and statistical pattern recognition.

Topics Include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • 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.

Syllabus – What You Will Learn From This Course

  • Practical aspects of Deep Learning
  • Optimization algorithms
  • Hyperparameter tuning, Batch Normalization and Programming Frameworks

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4. Nvidia And Nvidia Deep Learning Institute Via Independent

Nvidia And Nvidia Deep Learning Institute Via Independent

In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks. You will:

  • Implement common deep learning workflows such as Image Classification and Object Detection.
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
  • Deploy your networks to start solving real-world problems

On completion of this course, you will be able to start solving your own problems with deep learning.

What You’ll Learn

  • Identify the ingredients required to start a Deep Learning project.
  • Train a deep neural network to correctly classify images it has never seen before.
  • Deploy deep neural networks into applications.
  • Identify techniques for improving the performance of deep learning applications.
  • Assess the types of problems that are candidates for deep learning.
  • Modify neural networks to change their behavior.

Syllabus

  • Unlocking New Capabilities
  1. Big Bang in Deep Learning: Introduction
  2. Deep Neural Networks: 45 minutes
  3. The GPU:20 minutes
  4. Big Data: 45 minutes
  • Creating Applications That Use Deep Learning
  1. A Deep Learning Project: Introduction
  2. Simple Deployment: 45 minutes
  • Measuring And Improving Performance
  1. Categories of Performance
  2. Deploying Pretrained Networks
  3. Beyond Image Classification
  4. End Of Course

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5. Machine Learning: Columbia University Via edX

eDX

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major Perspectives Covered Include:

  • probabilistic versus non-probabilistic modeling
  • supervised versus unsupervised learning

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.

In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Syllabus

Week 1: maximum likelihood estimation, linear regression, least squares
Week 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
Week 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron
Week 5: logistic regression, Laplace approximation, kernel methods, Gaussian processes
Week 6: maximum margin, support vector machines, trees, random forests, boosting
Week 7: clustering, k-means, EM algorithm, missing data
Week 8: mixtures of Gaussians, matrix factorization
Week 9: non-negative matrix factorization, latent factor models, PCA and variations
Week 10: Markov models, hidden Markov models
Week 11: continuous state-space models, association analysis
Week 12: model selection, next steps

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