We see a lot of machine learning, which is a fascinating field of Artificial Intelligence. Machine learning unlocks the value of data in novel ways, such as when Facebook suggests stories for you to read on your news feed. This amazing technology aids in the learning and improvement of computer systems through the development of computer programs that can automatically accomplish tasks and access data via predictions and detections.
As you enter more data into a machine, this helps the algorithms to teach the computer, which enhances the output quality. When you ask Alexa to play your favorite music station on the Amazon Echo, she will select the one that you've listened to the most. You may further enhance and tailor your listening experience by telling Alexa to skip tracks, change the volume, and a range of other commands. All of this is made possible by machine learning and artificial intelligence's rapid growth.
Also, see - Locally Weighted Regression.
What is machine learning?
To begin with, machine learning is an essential sub-discipline of Artificial Intelligence. ML systems, like humans, learn from experience (or, to be more accurate, data) rather than direct programming. When exposed to new data, these apps learn, grow, adapt, and develop independently. To put it another way, machine learning is the process of computers discovering meaningful information without being told where to look. To accomplish this, they use algorithms that learn from data in an iterative process.
Machine learning is a concept that has been around for a long time. The idea of automating the application of sophisticated mathematical computations to massive data, on the other hand, has only been around for a few years, but it's getting popular now.
How does Machine Learning work?
The initial step in the Machine Learning process is to provide training data into the algorithm of choice. Training data can be known or unknown. The type of training data used impacts the algorithm, which will be discussed further later.
New input data is given into the machine learning system to see if it is performing appropriately. The prediction and the results are then compared.
If the prediction and the results aren't the same, the algorithm is re-trained until the data scientist gets the desired result. This enables the machine learning algorithm to learn and deliver the optimum response, gradually improving accuracy.
Machine Learning v/s Traditional Programming
Traditional programming is any manually written program that consumes input data and produces output on a computer.
In Machine Learning programming, on the other hand, the input data and output are fed into an algorithm to generate a program. This provides powerful insights that can be utilized to forecast future results.
Traditional programming is a manual procedure in which the program is created by a person (programmer). However, without anybody programming the logic, rules must be manually written or coded.
Machine learning, unlike traditional programming, is an automated process. Data prep, natural language interfaces, automatic outlier discovery, recommendations, and causality, and significance recognition are just a few of the ways it can improve the value of your embedded analytics. All of these aspects aid in the speeding up of user insights and the reduction of decision bias.
Now that we have looked at what machine learning is and its plus side against traditional programming let’s look at the types of machine learning.
Types of Machine Learning
Unsupervised learning and supervised learning are the two main categories of machine learning. Each one has a particular purpose and performs a specific operation, producing outcomes and employing various types of data. Unsupervised learning contributes to the remaining 10% to 20% of machine learning, while supervised learning accounts for over 70% of machine learning. Rest is taken by Reinforcement learning.
The training data used in supervised learning is known or labeled data. Because the data is known, the learning is supervised, leading to a successful outcome. The data is fed into the Machine Learning algorithm, which is then used to train the model. You can put unknown data in the model after it has been trained on known data to produce a new result.
The training data in unsupervised learning is unknown and unlabeled, implying that no one has ever looked at it before. The input cannot be led to the algorithm without the aspect of known data, which is where the word "unsupervised" comes from. The model is trained using this data, which is input into the Machine Learning algorithm. The trained model tries to find a pattern and give the desired response.
Like traditional kinds of data analysis, the algorithm discovers data through trial and error and then selects which action leads to more significant rewards. The agent, the environment, and the actions are the three main components of reinforcement learning. The agent is the learner or decision-maker, the environment is everything the agent interacts with, and the actions are the things the agent does.
Why is Machine learning important?
All businesses rely on data to function. Data-driven decisions are increasingly determining whether a company keeps up with the competition or falls further behind. Machine learning has the potential to unlock the value of corporate and consumer data and enable companies to make decisions that keep them ahead of the competition.
To have a better grasp of the applications of Machine Learning, consider the self-driving Google car, cyber fraud detection, and online recommendation engines like Facebook, Netflix, and Amazon. Machines can support all of these operations by sorting usable data and putting it together based on patterns to give accurate results.
Machine learning applications produce a variety of results, including web search results, real-time ads on websites and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All of these are unforeseen outcomes of evaluating vast volumes of data with machine learning.
- What is the best programming language for machine learning?
Python is the most widely used programming language for machine learning because of its widespread support and large library selection. Python is actually number one on GitHub's ranking of the top machine learning languages. Python is frequently used for data mining and data analysis, and python may use it to create a variety of machine learning models and methods.
- Give a brief description of Reinforcement Learning with an example.
Reinforcement learning is a machine learning training method based on rewarding desired behaviors and punishing undesired ones.
Consider the scenario of training your dog new tricks. You can’t directly tell in human language what to do. But we can emulate a situation, and the dog tries to respond in different ways. If the dog’s response is in the desired manner, we can reward him or the other way round. This way the dog learns to act appropriately in the expectation of more reward.
- What are the most used Machine Learning frameworks?
Some of the most used machine learning frameworks are -
TensorFlow, PyTorch, Scikit-Learn, Spark ML.
There are many of them. I have just listed the most frequently used.
- What is the use-case of ML in data analytics?
Machine learning has numerous applications in analytics, ranging from automating tedious manual data entry to more complex use cases such as insurance risk assessments or fraud detection. It also has client-facing functions such as customer service, product recommendations, and internal applications within organizations to help speed up processes and reduce manual workloads.
Cheers if you reached here!! You now understand how machine learning works.
This article aimed at explaining what machine learning is, how it works, its types, and its importance. Take a look at this article on Alpha Beta Pruning in Artificial Intelligence if you want to learn more about machine learning.
Yet learning never stops, and there is a lot more to learn. Happy Learning!!