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What is AI? Artificial Intelligence Tutorial for Beginners

A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.

In this basic tutorial, you will learn- Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale.

According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques.

Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on tasks like relationship building, lead nurturing, etc.

In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being.

The primary purpose of the research project was to tackle 'every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it.'

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

AI has broad applications- AI is used in all the industries, from marketing to supply chain, finance, food-processing sector.

Explained by three critical factors for its popularity are: Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches.

Hardware In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop.

Besides, big companies use clusters of GPU to train deep learning model with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.

company needs exceptionally diverse data sources to be able to find the patterns and learn and in a substantial volume.

Algorithm Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms.

Since 2010, remarkable discoveries have been made to improve the neural network Artificial intelligence uses a progressive learning algorithm to let the data do the programming.

At the beginning of the AI's ages, programmers wrote hard-coded programs, that is, type every logical possibility the machine can face and how to respond.

AI vs Machine Learning vs Deep Learning: What's the Difference?

Artificial intelligence is imparting a cognitive ability to a machine.

In this tutorial, you will learn- Machine learning is the best tool so far to analyze, understand and identify a pattern in the data.

One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being.

The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output.

When the machine finished learning, it can predict the value or the class of new data point.

It is a subset of machine learning and is called deep learning because it makes use of deep neural networks.

In the example, the classifier will be trained to detect if the image is a: The four objects above are the class the classifier has to recognize.

Training an algorithm requires to follow a few standard steps: The first step is necessary, choosing the right data will make the algorithm success or a failure.

The training set would be fed to a neural network Each input goes into a neuron and is multiplied by a weight.

it provides an actual value for the regression task and a probability of each class for the classification task.

The neural network is fully trained when the value of the weights gives an output close to the reality.

For instance, a well-trained neural network can recognize the object on a picture with higher accuracy than the traditional neural net.

A crucial part of machine learning is to find a relevant set of features to make the system learns something.

For example, an image processing, the practitioner needs to extract the feature manually in the image like the eyes, the nose, lips and so on.

The network applies a filter to the picture to see if there is a match, i.e., the shape of the feature is identical to a part of the image.

When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation.

Machine learning with Python: A guide to getting started

Science fiction has often been the predecessor to true scientific advancement, and in regards to artificial intelligence this is definitely the case, though not in the ways that authors and filmmakers have predicted.

If you’ve used a search engine, tagged a friend in a Facebook photo, or noticed a lack of spam in your email inbox, then you’ve used technology that utilizes machine learning.

In Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David define it as “the automated detection of meaningful patterns in data.” In other words, machine learning is a way for a computer program to comprehend data independently of a programmer.

'...machine learning is not trying to build automated imitation of intelligent behavior, but rather to use the strengths and special abilities of computers to complement human intelligence, often performing tasks that fall way beyond human capabilities.  ' A

Because machine learning is typically used to process large volumes of data, you may want to choose a powerful low-level language.

This is not a tutorial in using machine learning, but an introduction to the field, and a quick overview of resources one might use to get started as programming machine learning using Python.

While vanilla Python is not especially adapted to machine learning, it can be very easily modified to make writing machine learning algorithms much simpler.

Cutting your teeth with machine learning problems, allowing specialized libraries to handle fine details, is a great way to utilize Python while you come to grips with larger machine learning concepts.

Once you understand machine learning better as a larger field, then you might want to try moving onto other more powerful languages, such C++ and R, both of which are especially well-suited to large-scale machine learning problems.

Another great resource is Python.org, which contains a wealth of tutorials, downloads, documentation resources and a thriving community for both beginning and advanced users.

There are dozens of options for solving even simple problems, and many developers have found themselves including more libraries than they need while trying to find the best ones.

There are some libraries that specialize in machine learning itself, while others are more focused on specific aspects of machine learning, such as data analysis or visualization.

How it works: Making use of those handy N-dimensional arrays, it takes things a step further by introducing advanced algorithms for data handling and visualization.

In their own words, “with SciPy an interactive Python session becomes a data-processing and system-prototyping environment rivaling systems such as MATLAB, IDL, Octave, R-Lab, and SciLab.” Getting started: Learning the ins and outs of SciPy will make your machine learning programming that much easier, since it can handle most of the complex data manipulation for you.

How it works: Where NumPy provides foundational data structures, and SciPy provides algorithms for data manipulation, Matplotlib specializes in data visualization.

These packages are a great place to start if you want to build your machine learning program out into a working application, and do a great deal to ease the entire process.

Since Theano was developed specifically for machine learning at the Université de Montréal, it is an excellent tool for that application, even if it doesn’t handle the machine learning algorithms itself.

Keras' neural networks API was developed for fast experimentation and is a good choice for any deep learning project that requires fast prototyping.

The highlighted libraries are a great place to begin your journey, however, to understanding the complexities of the larger problem of machine learning before delving into uncharted waters.

In remarks made at the SASE conference in Berkeley, Maciej Cegłowski described machine learning as “money laundering for bias,” a means of disavowing responsibility for the results that your computer program has found.

Treated with the same caution and care that you would any other technology, learning python for machine learning can be a useful and marketable skill that will put you on the forefront of an incredible new wave of technological advancement.

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