Each time a company releases a new product that employs machine learning techniques and algorithms to serve the consumer specifically, it creates a lot of excitement because machine learning is one of the most challenging fields in advanced technology.
What is the Machine Learning?
Machine learning is a branch of artificial intelligence that focuses on teaching computers how to learn without being explicitly programmed. In simpler terms, ML is a way to teach computers to think as humans do. One of the best examples of ML is self-driving cars. These cars use sensors to collect information about their environment and then make decisions based on what they learned.
History of Machine Learning
The history of machine learning begins in 1943 with the publication of the first mathematical model of neural networks by Walter Pitts and Warren McCulloch in their academic paper, “A logical calculus of the ideas immanent in nervous activity.”
Arthur Samuel created the first-ever computer learning program in 1952. The checkers game was the software, and as it played more games, the IBM computer got better at the game by analyzing the moves that went into winning tactics and adding those movements to its program. Then, in 1957, Frank Rosenblatt created the perceptron, the first neural network for computers that replicated the workings of the human brain.
A knowledge-driven approach to machine learning gave way to a data-driven method in the 1990s. In order to process and derive conclusions from massive amounts of data, scientists started developing computer programs. Additionally, IBM’s Deep Blue stunned the world in 1997 by defeating the chess world champion.
- Geoffrey Hinton first used the phrase “deep learning” in 2006 to describe new methods that enable computers to recognize objects and text in photos and movies. Microsoft unveiled its Kinect technology, which lets users interact with computers, in 2010.
- When Google Brain was created in 2011, its deep neural network was capable of learning to find and classify items much as a cat does. Facebook created DeepFace in 2014 as a software system that can recognize.
- In 2015, Amazon released its own platform for machine learning. In the Chinese board game Go, Google’s artificial intelligence program defeated a professional player in 2016. While the rest of the world was dealing with the pandemic in 2020, open AI unveiled GPT-3, a revolutionary natural language processing algorithm.
Working principles of Machine Learning
The first step in the ML process is feeding the chosen algorithm with training data. The final machine learning algorithm is developed using training data, which might be known or unknown data. The method is affected by the type of training data input, and that idea will be discussed in more detail shortly. The ML algorithm is fed fresh input data to see if it functions properly. Then, the prediction and outcomes are cross-checked. The algorithm is repeatedly retrained if the prediction and results don’t line up until the data scientist achieves the desired result. As a result, the machine learning algorithm is able to continuously train on its own and produce the best solution, steadily improving in accuracy.
Types of Machine Learning
Due to its complexity, machine learning has been split into three main categories: supervised learning, unsupervised learning, and reinforcement learning. The types of machine learning are picturized below, an original image is taken from spicewoek.com.
Each one has a distinct goal and course of action that produces outcomes and makes use of different types of data. Supervised learning makes up over 70% of machine learning, whereas unsupervised learning makes up somewhere between 10% and 20%. Reinforcement learning takes up the remainder.
Supervised learning is a type of machine learning where we give the algorithm examples of correct answers. We tell the algorithm what the right answer should be and let it figure out the rest. The algorithm learns from the examples and tries to predict the outcome for future inputs.
Between supervised and unsupervised machine learning, semi-supervised learning is a crucial subcategory. It refers to a learning issue when a model must learn and make predictions on fresh examples from a limited share of labeled examples and a huge number of unlabeled examples.
Unsupervised learning is a type of ML where we don’t provide the algorithm with examples of the correct answer. Instead, we just give the algorithm a bunch of data and let it figure out what’s going on. Unsupervised learning is useful for finding clusters in data sets.
Reinforcement learning is a type of unsupervised learning where we give the system positive rewards for good behavior and negative rewards for bad behavior. The system figures out what actions lead to good results and avoids bad ones.
Advantages of Machine Learning
We should be aware of the advantages and disadvantages of learning technologies. The purpose is so that we can comprehend that subject’s capabilities.
For the prediction or interpretation of the results, no human intervention is necessary.
In many spheres of life, including education, medicine, engineering, etc., ML is used.
With the use of this technology, different trends and patterns can be found within a vast amount of data.
Amazingly helpful for students.
Disadvantages of Machine Learning
We need to be aware of the drawbacks of ML in addition to its benefits. You won’t be aware of the dangers of ML if you don’t know the drawbacks. Consequently, let’s look at these drawbacks:
The collection of data is one of the most difficult aspects of machine learning.
The ML procedure could take a long period for good results and accuracy.
More data is needed for interpretation, more space is needed to store the data.
Chances of error and faults are more.
Machine Learning algorithm
The algorithm is a collection of finite rules or instructions to be followed in calculations or other problem-solving procedures. In other words, the algorithm is a finite-step process for solving a mathematical problem that frequently uses recursive operations. In these highly dynamic times, a wide variety of ML algorithms have been developed to assist in resolving challenging situations in the real world. Depending on what you want to do, algorithms might range from simple to sophisticated. The most popular ML algorithms are listed below. Almost every data problem can be solved using these algorithms:
- Linear Regression (LiR
- Logistic Regression (LR)
- Decision Tree (DT)
- Support Vector Machine (SVM)
- Naive Bayes (NB)
- K- Nearest Neighbors (KNN)
- K-Means algorithm (KM)
- Random Forest (RF)
- Dimensionality Reduction (DR)
- Gradient Boosting (GB)
Applications of Machine Learning
- Filtering of spam and viruses in emails
- Traffic alerts
- Online fraud detection
- Social Media platforms
- Search engine result refining
- Products recommendation
- Google translation
- Computational biology
- Dynamic pricing
- Image recognition
- Virtual assistance
- Sentiment analysis
- Potential Heart Failure Prediction
- Automating Access Control for Employees
ML is the sub-part of artificial intelligence, algorithms that can learn from data and make predictions are designed and developed. Automating the creation of analytical models and enabling computers to learn from data without explicit programming are the goals of machine learning. Making predictions from data is a strong use of machine learning. But it’s crucial to keep in mind that ML is only as effective as the data used to train the algorithms.
Question: What came first AI or ML?
Answer: ML is the branch of AI, The original concept of AI came first, followed by machine learning.
Question: What was the first ML program?
Answer: Playing games and plotting routes, written by Arthur Samuel in 1952.
Question: What are the algorithms in machine learning?
Answer: Machine learning algorithms can be classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning.
Question: What are the 3 types of machine learning?
Answer: Supervised learning, unsupervised learning, and reinforcement learning.