5+ Awesome free Online Course for AI Artificial Intelligence and machine learning, Deep Learning during this Lockdown
The course covers the ground from a basic introduction to free machine learning, Deep learning to getting started with TensorFlow, to designing and training neural nets.
It is designed so that those with no prior knowledge of free machine learning, Deep Learning can jump in right at the start, those with some experience can pick or choose modules that interest them, while machine learning experts can use it as an introduction to TensorFlow.
Machine Learning Fundamentals
Free Online Course Machine Learning Fundamentals Understand machine learning's role in data-driven modeling,
prediction, and decision-making.
About this course
Do you want to build systems that learn from the experience?
Or exploit data to create simple predictive models of the
world?
Or exploit data to create simple predictive models of the
world?
In this course, part of the Data Science MicroMasters
the program, you will learn a variety of supervised and
unsupervised learning algorithms and the theory behind
those algorithms.
the program, you will learn a variety of supervised and
unsupervised learning algorithms and the theory behind
those algorithms.
Using real-world case studies, you will learn how to classify
images, identify salient topics in a corpus of documents,
partition people according to personality profiles, and
automatically capture the semantic structure of words and
use it to categorize documents.
images, identify salient topics in a corpus of documents,
partition people according to personality profiles, and
automatically capture the semantic structure of words and
use it to categorize documents.
Armed with the knowledge from this course, you will be able
to analyze many different types of data and to build
descriptive and predictive models.
to analyze many different types of data and to build
descriptive and predictive models.
All programming examples and assignments will be in \
Python, using Jupyter notebooks.
Python, using Jupyter notebooks.
What you'll learn
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
HarvardX Data Science: Machine Learning
Build a movie recommendation system and learn the
the science behind one of the most popular and
successful data science techniques.
About this course
HarvardX Data Science: Machine Learning 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.
Deep Learning Explained By Microsoft
(This Course Expire in June)
Learn an intuitive approach to building complex models that help
machines solve real-world problems with human-like intelligence.
AI for Everyone: Master the Basics by IBM
Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning, and neural networks.
This course is offered through Coursera and is taught by Andrew Ng, the founder of Google’s deep learning research unit, Google Brain, and head of AI for Baidu.
The entire course can be studied for free, although there is also the option of paying for certification which could certainly be useful if you plan to use your understanding of AI to increase your career prospects.
The course covers the spectrum of real-world machine learning implementations from speech recognition and enhancing web search, while going into technical depth with statistics topics such as linear regression, the backpropagation methods through which neural networks “learn”, and a Matlab tutorial – one of the most widely used programming languages for probability-based AI tools.
This course is also available in its entirety for free online, with an option to pay for certification should you need it.
It promises to teach models, methods and applications for solving real-world problems using probabilistic and non-probabilistic methods as well as supervised and unsupervised learning.
To get the most out of the course you should expect to spend around eight to ten hours a week on the materials and exercises, over 12 weeks – but this is a free Ivy League-level education so you wouldn’t expect it to be a breeze.
It is offered through the non-profit edX online course provider, where it forms part of the Artificial Intelligence nanodegree.
Computer vision is the AI sub-discipline of building computers which can “see” by processing visual information in the same way our brains do.
As well as the technical fundamentals, it covers how to identify situations or problems which can benefit from the application of machines capable of object recognition and image classification.
As a manufacturer of graphics processing units (GPUs), Nvidia unsurprisingly covers the crucial part these high-powered graphical engines, previously primarily aimed at displaying leading-edge images, has played in the widespread emergence of computer vision applications.
The final assessment covers the building and deploying a neural net application, and while the entire course can be studied at your own pace, you should expect to spend around eight hours on the material.
As with the course above, MIT takes the approach of using one major real-world aspect of AI as a jumping-off point to explore the specific technologies involved.
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.
This is a course taught at the bricks ‘n’ mortar university for the first time last year, and all of the materials including lecture videos and exercises are available online – however you won’t be able to gain certification. We are taken the few help of Forbes to find out the useful resource for you
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