What Is Machine Learning?
“Machine Learning(ML) at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”
How I Define ML in Simple Terms I Would Say,
Machine Learning is a Subfield of computer science and Artificialntelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task .
One Major Question Everyone Have in their Mind is How We Get Machines To Learn?
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks depending on what task you’re trying to accomplish and the type and amount of data that you have available.While emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par. Most of the time this is a problem with training data, but this also occurs when working with Machine Learning In new domains.
Research done when working on Real Applications often drives progress in the field, and Major Reasons Are:
Tendency to discover boundaries and limitations of existing methods
Researchers and developers working with domain experts and leveraging time and expertise to improve system performance.
Machines that learn are useful to humans because, with all of their processing power, they’re able to more quickly highlight or find patterns in big data that would have otherwise been missed by human beings.
The Latter Solution For Training or Learning a Machine has given Way To Deep Learning.
Clearing the Confusion between AI vs ML vs DL
AI and machine learning are often used interchangeably, especially in the realm of big data. But these aren’t the same thing, and it is important to understand how these can be applied differently.
Artificial intelligence is a broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans. When machines carry out tasks based on algorithms in an “intelligent” manner, that is AI. Machine learning is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing.
Training computers to think like humans is achieved partly through the use of neural networks. Neural networks are a series of algorithms modeled after the human brain. Just as the brain can recognize patterns and help us categorize and classify information, neural networks do the same for computers. The brain is constantly trying to make sense of the information it is processing, and to do this, it labels and assigns items to categories. When we encounter something new, we try to compare it to a known item to help us understand and make sense of it. Neural networks do the same for computers.
Artificial Intelligence is human intelligence exhibited by machines
Machine Learning is an approach to achieve Artificial Intelligence
Deep Learning is a technique for implementing Machine Learning
So, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level.
Why Is Machine Learning So Successful?
While machine learning is not a new technique, interest in the field has exploded in recent years.Deep learning Setting new records for accuracy in areas such as speech and language recognition, and computer vision.
What's made these successes possible are vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems.But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses.
Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.
As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
Building a Machine Learning Application
Machine learning is about Machine Learning algorithms as Well as Machine-learning Application.You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them.
Frame the core ML problem(s) in terms of what is observed and what answer you want the model to predict.
Collect, clean, and prepare data to make it suitable for consumption by ML model training algorithms. Visualize and analyze the data to run sanity checks to validate the quality of the data and to understand the data.
Often, the raw data (input variables) and answer (target) are not represented in a way that can be used to train a highly predictive model. Therefore, you typically should attempt to construct more predictive input representations or features from the raw variables.
Feed the resulting features to the learning algorithm to build models and evaluate the quality of the models on data that was held out from model building.
Use the model to generate predictions of the target answer for new data instances.
Major Machine-Learning Applications:
- Image Classification
Classification Of Sentences
Image Style Transfer
Speech To Text Synthesis
Best Machine Learning Mobile Apps:
So how do Snapchat filters work?
The first step is to detect a face. The program sees a photo as a set of data for the color value of each individual pixel. But how does it know which part of the image is a face?
Well, the clue is looking for areas of contrasts, between light and dark parts of the image. By repeatedly scanning through the image data calculating the difference between the grayscale pixel values underneath the white boxes and the black boxes, the program can detect faces.For instance, the bridge of the nose is usually lighter than the surrounding area on both sides, the eye sockets are darker than the forehead, and the middle of the forehead is lighter than its sides.
This kind of algorithm won’t find your face if you’re really tilted or facing sideways, but they’re really accurate for frontal faces.But in order to apply a flower crown, the app needs to do more than just detect a face. It has to locate facial features. According to the patterns, it does this with an ‘active shape model’ – a statistical model of a face shape that’s been trained by people manually marking the borders of facial features on hundreds of sample images.
The algorithm takes an average face from that trained data and aligns it with the image from your phone’s camera, scaling it and rotating it according to where it already knows your face is located. But it’s not a perfect fit so the model analyzes the pixel data around each of the points, looking for edges defined by brightness and darkness.
Once it locates your facial features, those points are used as coordinates to create a mesh – a 3D mask that can move, rotate, and scale along with your face as the video data comes in for every frame and once they’ve got that, they can do a lot with it. They can deform the mask to change your face shape, change your eye color, and accessories, and set animations to trigger when you open your mouth or move your eyebrows.
Well That's How SnapChat Works!
Solving Business Problems with Amazon Machine Learning
You can use Amazon Machine Learning to apply machine learning to problems for which you have existing examples of actual answers. For example, if you want to use Amazon Machine Learning to predict if an email is spam, you will need to collect email examples that are correctly labeled as spam or not spam. You then can use machine learning to generalize from these email examples to predict how likely new email is spam or not. This approach of learning from data that has been labeled with the actual answer is known as supervised machine learning.
You can use supervised ML approaches for these specific machine learning tasks: binary classification (predicting one of two possible outcomes), multiclass classification (predicting one of more than two outcomes) and regression (predicting a numeric value).
Examples of binary classification problems:
Will the customer buy this product or not buy this product?
Is this email spam or not spam?
Is this product a book or a farm animal?
Is this review written by a customer or a robot?
Examples of multiclass classification problems:
Is this product a book, movie, or clothing?
Is this movie a romantic comedy, documentary, or thriller?
Which category of products is most interesting to this customer?
Examples of regression classification problems:
What will the temperature be in Seattle tomorrow?
For this product, how many units will sell?
How many days before this customer stops using the application?
What price will this house sell for?