How should one start learning about AI and machine learning?

What are AI and Machine Learning, How do They Work and How to Learn Them

There are 5 different types of AI (Artificial Intelligence) and Machine Learning that are used in the industry.

AI is a set of algorithms and technologies that are based on an approach which is called machine learning.

This approach to solving problems through analysis and pattern recognition has been around for a long time, but it’s only now that we have enough processing power to make it work in complex environments like games or self-driving cars.

The first type of artificial intelligence is called statistical inference. It’s also considered a subset of machine learning and one of the simplest forms of artificial intelligence. This type of AI uses statistics, probability and random processes to solve problems in its environment. A classic example would be spam filters, which use statistical inference to identify what messages are spam by looking at which messages get flagged most often by other users who have installed the same spam filter software. Statistical inference can be applied to all sorts of problems, from preventing credit card fraud to identifying objects

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AI and ML are two terms that sound almost the same and are often used interchangeably. However, there is a difference between AI vs ML in the world of technology, which is what we will be exploring in this section.

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Artificial Intelligence or Machine Learning: What’s the Difference?

Differentiating between artificial intelligence (AI) and machine learning (ML) might seem like a difficult task. But if you look closely, they have many different aspects that differ from each other. They both work to create a more efficient future for us. And the only way to understand how they are different is by understanding their individual definitions.

AI is definitely the future. Machine learning, being the current application of artificial intelligence, is based on the idea to give computer access to data and make them learn themselves. There are obviously various ways to start.

These are the steps you are suggested to follow:

• KNOW THE STRUCTURE: Machine learning and AI as a whole, have a crux known as data science. So, before getting into deeper nodes of this web, you must start with the basics. To apply machine learning, you got to answer these questions; what to achieve and why to achieve it. Collection, storage and analysis of data using SQL and python will be helpful. This can be done using online study materials.
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• ANALYSE THE DATA: Next, jump to the most important characteristic of the machine learning course, mathematics. Clear the basic concepts of statistics, probability and calculus as well. You can start with the Khan Academy course on python statistics, covering various topics. You have to pull up resources and try to make the best use of them.

• ALGORITHMS: Once you are familiar with what and why you must start inputting your time into the theory and algorithms that are used while you are creating a specific application of machine learning. Start with regressions, decision trees, and Bayesian models and you will be choosing algorithms based on them.

• IMPLEMENTATION: Once you are clear with the basics and algorithms, start implementation of it. Start learning the python libraries that you must use and will be using to apply the theories you learned. You will get everything piled up in sci-kit learn, the best API with a simple and efficient set of tools for data mining and analysis. There are a bunch of libraries to start with like NumPy, Matplotlib, Pandas etc.

• TARGET: Machine learning is more about practical application rather than theory. So, once you are clear with the fundamentals, select an application and start exploring it. You might need to read and learn something extra down the line, but your knowledge will be practical-based. Take up projects, set deadlines and start exercising whatever you learnt.

There are two broad perspectives on getting into AI and machine learning; first, the API and second, the algorithms. These two prospects are hardly covered when you start an online course or you read a book. The best way to gain knowledge is by executing it in practical ways. Various Machine learning classes in Pune have been opened since.

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Some recommendations to start from:

Books

Bayesian Reasoning and Machine learning by David Barber

Machine learning: A probabilistic perspective by Kevin Murphy

Data science from scratch: First-principles with Python by Joel Grubs’

MOOCs (Massive open online courses)

Andrew Ng Machine learning at Coursera

Sebastian and Katie at Udacity