The new buzzword is “machine learning,” which is a subfield of AI. Everyone seems to be taking the next Machine Learning course they can find. You, too, are in love with this kind of AI. Not sure what machine learning is, though. Don’t know where to start or how to move forward? We’ve got you taken care of!
This article will explain what machine learning is and how it works. It will also explain what machine learning concepts are. We will talk about the most important topics and subtopics in machine learning, as well as what these machine learning topics mean. We shall also look at the machine learning process flow.
1. Introduction to Machine Learning
The basic ideas of machine learning include the following:
- What is Artificial Intelligence?
- What is Machine Learning?
- Types of Problems in Machine Learning
- Types of Machine Learning
- Algorithms of Machine Learning
- Applications of Machine Learning
2. Artificial Learning (AI)
Artificial Intelligence, or AI, is a branch of computer science that tries to make machines think like humans. The cognitive functions of the human brain are studied and put into a machine or system that can act like a human.
AI is based on rules and is static. It uses if-then rules to make decisions. It is used to solve hard problems and make routine tasks easier. Machine Learning is a part of Artificial Intelligence. Deep Learning, which comes after neural networking, is one of the subtopics of machine learning.
3. Machine Learning (ML)
Machine Learning (ML) is a type of Artificial Intelligence that lets a system learn from experience and get better without being explicitly programmed. Machine Learning is not based on hard-coded rules. Instead, it changes over time.
4. Types and Algorithms of Machine Learning
Machine Learning can be used to solve three main kinds of problems. These depend on what comes out:
- Regression: The output is always the same in regression problems. Want to guess the amount of the loan, the speed of the wind, or the speed of the car?
- Classification: In this case, putting an event or group of data into a group that has already been set up. A categorical value is what comes out. For example, putting emails into two groups: spam and not spam, or whether or not a person has diabetes.
- Clustering: In this problem, we need to divide the data into n classes, where n is not known ahead of time. The difference between this and the classification problem is that the number of groups is not set. For example, customers could be put into similar groups based on their age, gender, interests, and purchases.
Regression and Classification are both examples of Supervised Learning, while Clustering is an example of Unsupervised Learning. We shall discuss below these basic machine learning concepts.
5. Types and Algorithms of Machine Learning
What are the different kinds of machine learning? is an important question in machine learning. There are also algorithms in the topic of machine learning. Here are the four main types of learning, along with their corresponding algorithms:
5.1 Supervised Learning
In supervised learning, the algorithm learns from a set of data that has been labeled and for which the answer is known. This acts as a “supervisor” to train the model. It gives the algorithm an answer key that it can use to judge how accurate it is on training data. This is used to guess the values of data that haven’t been seen yet. This method is based on the task.
Supervised learning includes things like customer churn, employee attrition, catching fraud transactions, predicting sales prices, predicting subscription renewals, filtering spam, and recognizing handwriting.
These are the algorithms for supervised learning:
- Linear Regression
- Logistic Regression
- Choice trees
- Most Close Neighbors
- Bayes naive
- Support Vector Machines (SVMs)
- Ensemble Learning Techniques
5.2 Semi-Supervised Learning
Semi-supervised learning is a method of learning that is in between supervised and unsupervised learning. It uses both labeled and unlabeled data for training. Labeled data is usually small, while unlabeled data is usually a lot.
It can be used to analyze speech, sort web content, tag photos, and document text. Pseudo Labeling and Semi-Supervised Generative Adversarial Network are algorithms for semi-supervised learning (SGAN).
5.3 Unsupervised Learning
In unsupervised learning, the model uses unlabeled data to figure out what the hidden structure or pattern is. There is no response or target variable in the data, so the analysis can’t be used to figure out what is right or wrong.
The machine looks for the pattern and then answers. In the absence of the desired output, the data is categorized or segmented using clustering. The algorithm learns to tell the difference between a person’s face and that of a horse or a cat. This method is based on how people act.
Customer segmentation, image segmentation, market basket analysis, delivery store optimization, and finding places where accidents are likely to happen are all examples of unsupervised learning.
K-means Clustering, Agglomerative (Hierarchical) Clustering, Spectral Clustering (DBSCAN), Association Analysis, and Principal Component Analysis are all algorithms for learning without being watched.
5.4 Reinforcement Learning
The feedback loop between an agent and its environment is the foundation of reinforcement learning. This method is based on the reinforcements that the dog has learned through trial and error.
Here, the agent learns how to act in a certain environment by doing certain things and watching how they are rewarded and what happens as a result. Learning how to ride a bike is an example of this. This method can improve the operational efficiency of systems like robotics, education, logistics for the supply chain, and manufacturing.
Q-Learning, State-Action-Reward-State-Action (SARSA), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient are some of the reinforcement learning algorithms (DDPG).
6. Neural Network or Artificial Neural Network (ANN)
Machine Learning also includes Artificial Neural Networks (ANN). These ideas about machine learning are statistical models that are based on how brain cells called neurons work.
ANN can mathematically model how the biological brain works, allowing the machine to mimic the human brain. ANNs can think and learn like humans, so they can recognize things like speech, objects, and animals in the same way people do.
Artificial neural networks are the basis of deep learning (ANNs). This part of Machine Learning lets the machine teach itself to do a task by showing the multi-layered neural network a lot of data.