Artificial Intelligence
Duration: 7h30m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.22 GB
Genre: eLearning | Language: English

Machine Learning, Supervised Learning, un-supervised learning
What you'll learn:
This tutorial covers the basic/intermediate concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic Algorithms etc.

, and its implementation in Python.
Requirements:
Basic knowledge about Python programming. He/she should be aware about basic teologies used in python packages
Description:
We'll discuss the different types of machine learning, the different algorithms involved in machine learning, which include classification algorithms, regression algorithms, clustering and association algorithms.
To make understand machine learning better, we'll run a couple of demos where we will see how machine learning algorithms are used to solve real world problems.
After that we will discuss the limitations of machine learning and why deep learning is needed. i will introduce you to the deep learning concepts, what are neurons, perceptrons and multiple layer perceptrons and so on. we will discuss the different types of neural networks and also look at what exactly back propagation is, apart from this, we will be running a demo tounderstand deep learning in more depth.
We will move on to next module which is natural language processing. Under natural language processing we will try to understand what is text mining, the difference between text mining in NLP, what are the different teologies in NLP and we will end the session by the practical implementation of NLP using Python. So guys there is a lot to cover in this course.
This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The learner can be a bner or an advanced learner.
Who this course is for:
This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The learner can be a bner or an advanced learner





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