Learn About AI Vs ML Vs DL Vs DS Vs NN Vs NLP Vs RPA

In this video I will Learn about AI vs ML vs DL vs DS vs NN vs NLP vs RPA | ARTIFICIAL INTELLIGENCE | AI Python | AI Course | AI ML | Python AI | AI

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Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

AI vs Machine Learning vs Deep Learning | AI vs ML vs DL | Machine Learning Training with Python

This video is about the difference between the three terms Artificial Intelligence, Machine Learning & Deep Learning.
AI vs ML vs DL

Download the Course Curriculum File from here: https://drive.google.com/file/d/17i0c6SmncNuwSgr9W1MRRk3YYdEOP9Gd/view?usp=sharing

#machine-learning #deep-learning #artificial-intelligence #data-science #ai

How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML

This is a complete guide to start and improve your knowledge of machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

#learn-ai #ai #artificial-intelligence #machine-learning #deep-learning #learn-machine-learning #youtube-transcripts #youtubers #web-monetization

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai