Advanced Artificial Intelligence & Machine Learning E-Degree Programs

Eduonix Learning Solutions is raising funds for Advanced Artificial Intelligence & Machine Learning E-Degree Programs. A complete learning program having set of hand-picked courses which will help you to master all the advanced AI concepts such as Deep learning, Neural Networks, Reinforcement Learning, Computer Vision and NLP.


Advanced Artificial Intelligence & Machine Learning E-Degree Programs

A complete learning program having set of hand-picked courses which will help you to master all the advanced AI concepts such as Deep learning, Neural Networks, Reinforcement Learning, Computer Vision and NLP.

Why This E-Degree 
Artificial Intelligence is here to stay and in fact, it is growing exponentially. It not just allows machines to learn but brings hundreds of possibilities to modern businesses. From self-driving cars to job automation to diagnosing illness, everywhere AI & ML is changing the way humanity works.

But as AI progresses, the more complex it becomes; and here’s where things get really crazy. Already there is a significant gap in the availability of quality resources and good skills. Furthermore, with advanced AI, these gaps get more widened. All of these results in massive opportunities for people having quality skill sets.

In order to achieve this & solve complex AI problem, we bring you this Advanced AI & ML E-Degree

List Of Advanced Artificial Intelligence & Machine Learning Online E-Degree Programs

1. Introduction to Deep Learning

Learn machine learning concepts like supervising learning, unsupervised learning, reinforced learning, neural networks & so on in this machine learning tutorial. Master machine learning algorithms with Tensorflow such as supervised learning, unsupervised learning, and neural networks.

2. Deep Neural Networks & CNN

Understanding Neural Networks. From neuron to RNN, CNN With various variants like CNN (Convolutional Neural Networks), RNN(Recurrent Neural Networks), AutoEncoders, Deep Learning etc. When you first heard of the term convolutional neural networks, you may have ... The first layer in a CNN is always a Convolutional Layer.

3. Natural Language Processing

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.

4. Practical TensorFlow

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision.

This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. Build image recognition algorithms with deep neural networks and convolutional neural networks. The Machine Learning course and Deep Learning Specialization.

5. Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.

6. Fundamentals of Computer Vision

Fundamentals of Computer Vision. Covers the basic topics of computer vision, and introduces some fundamental approaches for computer vision research. Covers the basic topics of computer vision, and introduces some fundamental approaches for computer vision research.This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, and computer vision

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