What is Machine Learning (ML)?
Machine learning (ML) is an exciting branch of Artificial Intelligence (AI), and it’s all around us. ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from data. Once you have trained the model, you can use it to reason over data that it hasn't seen before and make predictions about those data. ML can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed, i.e., without any human assistance. The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.
- What Should You Do About Machine Learning (ML)?
- How does Machine Learning (ML) Work?
- What are the Different Types of Machine Learning (ML)?
- Why is Machine Learning (ML) Important?
What Should You Do About Machine Learning (ML)?
If you got to this point and are convinced that ML engineering skills will be crucial over the next few years, you are probably wondering how you can learn some of that.
Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model.
Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML).
How does Machine Learning (ML) Work?
Undoubtedly, machine learning is one of the most exciting subsets of Artificial Intelligence. It completes the task of learning from data with specific inputs to the machine. It’s essential to understand what makes Machine Learning work and, thus, how it can be used in the future. The Machine Learning process starts with inputting training data into the selected algorithm. Training data is known or unknown data used to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm.
New input data or test data is fed into the machine learning algorithm to test whether the algorithm works correctly. The prediction and results are then checked against each other. As you input more data into a machine, this helps the algorithms better teach the computer, thus improving the delivered results. When you ask Alexa to play your favorite music station on Amazon Echo, she will go to the station you played most often. You can further improve and refine your listening experience by telling Alexa to skip songs, adjust the volume, and many more possible commands. Machine Learning and the rapid advance of Artificial Intelligence make this all possible.
What are the Different Types of Machine Learning (ML)?
Machine Learning is complex, which is why it has been divided into three primary areas, supervised learning, unsupervised learning, and reinforcement learning. Each one has a specific purpose and action, yielding results and utilizing various forms of data. Approximately 70 percent of machine learning is supervised learning, while unsupervised learning accounts for anywhere from 10 to 20 percent. Other areas like reinforcement learning take up the remainder.
1. Supervised Learning
In supervised learning, we use known or labeled data for the training data. Since the data is known, the learning is supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model. Once the model is trained based on the known data, you can use unknown data into the model and get a new response. In this case, the model tries to figure out whether the data is an apple or another fruit. Once the model has been trained well, it will identify that the data is an apple and give the desired response.
Here is the list of top algorithms currently being used for supervised learning are:
- Random forest
- Linear regression and Polynomial regression
- Logistic regression
- Decision trees
- K-nearest neighbors
- Naive Bayes
2. Unsupervised Learning
In unsupervised learning, the training data is unknown and unlabeled - meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model searches for a pattern and gives the desired response. In this case, it is often like the algorithm is trying to break code like the Enigma machine but without the human mind directly involved but rather a machine. In this case, the unknown data consists of apples and pears that look similar. The trained model tries to put them all together so that you get the same things in similar groups.
3. Reinforcement Learning
Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does. Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.
Why is Machine Learning (ML) Important?
To better understand the uses of Machine Learning, consider some instances where Machine Learning is applied: the self-driving Google car; cyber fraud detection; and online recommendation engines from Facebook, Netflix, and Amazon. Machines can enable all of these things by filtering useful pieces of information and piecing them together based on patterns to get accurate results.