Machine Learning-Everything you need to know

Machine Learning enables computers to handle tasks that can only be handled by humans.

From driving cars to translating speech or talking to robots in a human way, machine learning based on AI capabilities helps software and machines understand and handle the chaotic and unexpected real world.

What is Machine Learning and its importance?

Simply put, machine learning is the process of teaching a computer and machines how to make accurate predictions when fed by data.

These predictions can be, for example:

1. Answer whether the type of fruit in an image is a banana or apple.

2. Ability to identify people crossing the road in front of a self-driving car.

3. Find out whether using the word Book in a sentence is about a paper book or about wanting to book hotels because the word carries two meanings.

4. Find out if an email is undesirable or must be answered as with Gmail.

The main difference between it and traditional computer software is that the human developer of the software has not written a code or code that guides the software to know the difference between bananas and apples.

Instead, the software programming method that works machine learning technology or Machine Learning trains machines and software on how to reliably differentiate fruit by training it on a large amount of data.

So data or a lot of data, is key to making machine learning or Machine Learning possible.

Machine Learning: Everything you need to know

Difference between AI and Machine Learning

Machine learning may have been a tremendous success right now, but it’s just one way to achieve AI.

When AI started in the 1950s, AI was defined as the machine capable of doing a particular task that usually requires human intelligence to do.

AI systems will generally show at least some of the following features: planning, learning, thinking, problem solving, representing knowledge, perception, movement, manipulation, social intelligence and creativity. Along with machine learning, there are many other ways used to build AI systems, including evolutionary computing so-called evolutionary computation, where algorithms undergo random intergenerational mutations in an attempt to “evolve” optimal solutions. Also expert systems are called expert systems, where computers are programmed with rules that allow them to mimic human behavior in a particular field, for example an autopilot system.

Machine learning under supervision

Everything starts with the training of Machine Learning (Machine Learning Model) machines, a mathematical function capable of frequently adjusting how they operate so that machines can make accurate predictions when given new data.

Before starting training, you must first choose the data to collect and determine its important features.

A highly simplified example of data features is given in this explanation from Google, where the machine learning model (machines taught) is trained to recognize the difference between Pepsi and mineral water, for example, based on two features, the color and size of beverages.

Each drink is classified as Pepsi or water, then the relevant data is collected between them, using a spectrometer to measure its color and a hydrometer to measure its content.

An important point to note is that the data must be balanced.

The data collected are then divided into a larger training ratio of about 70 per cent, and a smaller evaluation ratio, for example, the remaining 30 per cent. These evaluation data allow the trained model (machine or software) to be tested for how well it performs on real-world data.

Before the start of training, there will also be a step to prepare the data and prepare it for the trained machine, where processes such as cancellation of duplicate data and correction of errors will be carried out. The next step will be to choose a suitable model for machine learning from the large dataset available. Each has data type-based strengths and weaknesses, for example, some of which are suitable for image processing, others for text, and others for purely digital data.

How does ML training work under the supervision of the machine or software?

Basically, the machine training process we want to automatically teach involves how to automatically modify its functions so you can make accurate data predictions, as in Google’s previous example.

A good way to explain the training process is to take an example using a simple ML model, known as linear regression with a graded regression (linear regression with gradient descent). In the following example, the (machine learning model) is used to estimate how many ice cream will be sold based on external temperature.

Imagine that all previous data showing ice cream sales and external temperatures, and planning that data with each other is taken on a scattered graph.

To predict how many ice creams will be sold in the future based on the external temperature, you can draw a line that runs through all these points, as illustrated below. Once done, ice cream sales can be expected at any temperature by finding the point at which the line passes through a certain temperature and reading the total expected sales at that point.

Why ML is a very successful field?

While ML is not a very modern area, interest in this area has exploded in the world of modern technological industries in recent years only.

This spread comes against the backdrop of the success of deep learning or deep learning from breaking records in many areas such as having speech conversations like humans and learning about different languages and its remarkable role in computer science.

These successes are primarily due to two factors:

1. One is the vast quantities of images, speeches, videos and texts available on the Internet that researchers can use in training machine learning systems.

2. The other factor is the tremendous speed provided by processors that greatly helps in those fields, as well as modern graphics processing speeds (GPUs), which can be linked to each other in groups to form machine learning centers.

With the use of machine learning looming large and large scale, companies are now creating specialized devices designed to operate and train machine learning models.

Examples include Google’s Tensor Processing Unit (TPU), the latest version of which offers a huge speed of machine learning created using Google’s TensorFlow software library.

These processors are used not only for machine training using Google DeepMind and Google Brain, but also for training the popular translation site Google Translate and Google Photos image recognition, as well as services that allow the public to create machine learning models using Google’s TensorFlow Research Cloud.

Using Machine Learning Technology

Machine learning systems are used everywhere around us, and they are heavily deployed in the modern Internet now.

ML systems are used to suggest the product you may want to buy afterwards as is the case on Amazon or the video you can want to watch on Netflix.

Every Google search uses many machine learning systems, to understand the language in your inquiry to show better results, so that searches are not mixed with each other and give you the perfect result for your search and search area. Similarly, Gmail systems are used to identify spam and SPAM messages.

Among the most visible features of ML power now are virtual assistants, such as:

1. Apple Siri

2. Alexa in Amazon

3. Google Assistant

4. Microsoft Cortana

Each relies heavily on machine learning for voice recognition and the ability to understand the language we speak, as well as to help answer queries and questions we ask them.

This technology has begun to be used in much larger areas, for example:

1. Used in self-driving cars, drones, and deliverie robots.

2. Used in speech and language recognition, chatbots programming and human auxiliary robots.

3. It is used in facial recognition technology in some countries such as China.

4. Help radiologist select X-ray tumors, helping researchers identify disease-related genetic sequences and identify molecules that can lead to the use of more effective drugs.

5. Use in IoT.

Other examples are too many to mention.

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