5+ Awesome free Online Course for AI Artificial Intelligence and machine learning,Deep Learning during this Lock down

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.

5+ Awesome free Online Course  for AI Artificial Intelligence and machine learning,Deep Learning  during this Lock down

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
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Do you want to build systems that learn from the experience? 

Or exploit data to create simple predictive models of the 


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.

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.
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.
All programming examples and assignments will be in \

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.

                  What you'll learn

Skip What you'll 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?

              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.

                                    About this course

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Free Online Course Deep Learning Explained By Microsoft  

Machine learning uses computers to run predictive models 
that learn from existing data to forecast future behaviors, 
outcomes, and trends. Deep learning is a sub-field of 
machine learning, where models inspired by how our brain 
works are expressed mathematically, and the parameters 
defining the mathematical models, which can be in the order 
of few thousand to 100+ million, are learned automatically 
from the data.

Deep learning is a key enabler of AI-powered technologies 
being developed across the globe. In this deep learning 
course, you will learn an intuitive approach to building 
complex models that help machines solve real-world 
problems with human-like intelligence. The intuitive 
approaches will be translated into working code with 
practical problems and hands-on experience. You will learn 
how to build and derive insights from these models using 
Python Jupyter notebooks running on your local Windows or 
Linux machine, or on a virtual machine running on Azure. 
Alternatively, you can leverage the Microsoft Azure 
Notebooks platform for free.

This course provides the level of detail needed to enable 
engineers/data scientists/technology managers to develop 
an intuitive understanding of the key concepts behind this 
game-changing technology. At the same time, you will learn 

simple yet powerful "motifs" that can be used with lego-like 
flexibility to build an end-to-end deep learning model. You 
will learn how to use the Microsoft Cognitive Toolkit -- 
previously known as CNTK -- to harness the intelligence 
within massive datasets through deep learning with 
uncompromised scaling, speed, and accuracy.
edX offers financial assistance for learners who want to earn 
Verified Certificates but who may not be able to pay the fee. 
To apply for financial assistance, enroll in the course, then 
follow this link to complete an application for assistance

           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.

                                 About this course

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In this course, you will learn what AI is and understand its 
applications and use cases and how it is transforming our 
lives. You will explore basic AI concepts including machine 
learning, deep learning, and neural networks as well as use 
cases and applications of AI. You will be exposed to 
concerns surrounding AI, including ethics, bias, jobs and the impacts on society.
You will take a glimpse of the future with AI, get advice for starting an AI-related career, and wrap up the course by demonstrating AI in action with a mini-project.
This AI for Everyone course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a 
technical background or not.

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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