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Machine Learning - Beginners Level 1 on 1 Highly Personalized

From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core.

In this course, students will know the methods and tools widely applied to the field of machine learning: linear models for regression and classification, clustering methods, working with text data, neural networks, reinforcement learning, and other advanced topics.

Pooja Umathe Pooja Umathe is an Aspiring Data Scientist with strong Analytical background and 3+ years of experience using predictive modeling, data processing, machine learning, and data mining algorithms to solve challenging business problems.

She intellectually curious, with strong leadership qualities and communication skills, she also demonstrates strong problem-solving skills, and comfortable in manipulating, wrangling and analyzing large and complex data sets.

She has strong analytical skills with excellent track records of her ability to solve unstructured problems involving large amounts of quantitative data with a structured and hypothesis-driven approach.

8 Best Topics for Research and Thesis in Artificial Intelligence

We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task.

(In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data.

These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!). This

web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach.

means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future.

For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings.

This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!).

There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation, etc. NLP

This information can be object detection in the image, identification of image content to group various images together, etc.

An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention.

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