What is machine learning?
Machine learning (ML) is a part of AI (Artificial Intelligence), in which we deal with and study the computer algorithms that evolve and improves automatically by use of data and experience. Machine learning uses sample data called training data and uses learning algorithms to create models that it uses to make predictions or decisions on its own without any explicit programming by us. And this way, machines learn to do things on their own, without our orders again and again. Even without our need of programming it, it can surely function well and take decisions on its own analyzing the situations and using the model created over training data and algorithms.
A subset of machine learning relates to computational statistics also. Not all machine learning uses statistics.
Thus, we can teach a computer to work some simple tasks and make decisions on its own without the aid of our programming languages. A machine can learn using algorithms and sample data and then can execute on its own and we call this process of learning computer machine learning.
Origin of machine learning
The pioneer of computer gaming and artificial intelligence, an American IBMer, coined the term Machine Learning for the first time back in 1959. Then, a representative book of machine learning research during the 1960s, was Nilsson's book focused on Machine Learning. This focused mainly on pattern classification. Then Duda and Hart continued the pattern classification in the early 1970s. Then a report came out that the neural network learns to recognize 40 characters on its own.
Tom M. Mitchell explained machine learning by quoting the way a machine learns by experience E with respect to some tasks T and then a performance by P at tasks T as measured by P and then improves with experience E.
Then, Alan Turing made a paper on “can machines think?” in “Computing Machinery and Intelligence”. Later, “can machines do what we (as thinking entities) can do?” replaced the former question.
Now, modern-day machine learning has two objectives, classifying data and making predictions and decisions. The machine classifies data on the basis of created models and then, uses these data for the second purpose based on models.
After a machine makes statistical learning, for example, it can learn the way the stock market is going, and then it can tell the owner the possible predictions for stocks in the future and can even make decisions of retrieving stocks or selling stocks on its own with owner consent.
Use of machine learning these days -!!!
Machine learning has great uses in different fields. Briefly, we use machine learning in the following fields: -
· Adaptive websites
· Affective computing
· Brain-machine interfaces
· Citizen science
· Computer networks
· Computer vision
· Credit-card fraud detection
· Data quality
· DNA sequence classification
· Financial market analysis
· General game playing
· Handwriting recognition
· Information retrieval
· Internet fraud detection
· Machine learning control
· Machine perception
· Machine translation
· Medical diagnosis
· Natural language processing
· Natural language understanding
· Online advertising
· Recommender systems
· Robot locomotion
· Search engines
· Sentiment analysis
· Sequence mining
· Software engineering
· Speech recognition
· Structural health monitoring
· Syntactic pattern recognition
· Theorem proving
· Time series forecasting
· User behavior analytics
Netflix uses machine learning to see user recommendations and establish CRM (customer relationship management). Then, the Wall Street Journal used machine learning for predicting financial crises based on statistical models. Then, Vinod Koshla predicted using machine learning doctors will lose 80% of their jobs to machine learning medical diagnostic software. In 2020, machine learning will help in creating cures and help diagnoses for COVID-19.
Future of machine learning
Machine learning possesses an enormous future side. On asking some specialists we have a tendency to find, what they consider machine learning. Here are prime three reviews: -
• Fine-tuned personalization by Ben Wald, Co-Founder & VP of Solutions Implementation at Very: -
This is what Ben said: - “With ninety p.c of all knowledge generated over the last 2 years, a lot of it grows from an associate degree array of good devices that connect our phones, wrists, and homes. As a result, firms have additional ways in which they can create relationships with their customers. victimization machine learning, companies will fine-tune their understanding of their audience to tell development, marketing, and sales. With algorithms to interrupt down precisely however their product area unit gets used, developers and designers will customize product way more exactly than ever before, increasing worth for each the corporate and also the shopper.”
• Better computer program experiences by Dorit Zilbershot, Chief Product Officer at Attivio: -
This is what Dorit said: - “Search engines are a unit progressing to improve each user and also the admin expertise by leaps and bounds over successive few years. With additional development of neural networks and deep learning, search engines in the long run are going to be higher at delivering answers and insights that are extremely relevant to the user that's looking out.
• Evolution of knowledge groups by Henrique Senra, Vice-President of Product Development at SlicingDice: -
This is what Henrique said: - “It's nearly not possible to predict the long run of milliliter and AI. If you had told technology specialists twenty years ago what we have a tendency to do with milliliters nowadays, they might in all probability be skeptical, to mention the smallest amount. Nowadays we are using bound trends in, however, milliliters and the way those cases can evolve within the near future. a milliliter is going to be one of all the foundational tools for developing and maintaining digital applications within the coming back years. This suggests IT/data groups can pay less time programming and changing applications, however rather have them learn and keep up their operations frequently.
Do you need to learn machine learning? A career in Machine Learning!
1. Machine learning engineer: - an engineer who researches various machine learning techniques using programming languages.
2. Data scientist: - a data scientist creates a model. They analyze, collect and interpret huge amounts of data and make actionable insights.
3. NLP scientist: - national language processing scientist. It involves creating away so that the machine can understand human language. An NLP scientist does human voice recognition and interpretation.
4. Business intelligence developer: - a business intelligence developer uses data analytics and machine learning to collect, analyze and interpret huge amounts of data and information. They do the same task as data scientists but in a different way and purpose.
5. Human-centered machine learning designer: - creating models for understanding human interaction with the machines and their wishes. Like Netflix uses machine learning for learning recommendations and for this human-centered designer creates models to understand humans.
Where to Learn Machine Learning? – Machine Learning process!
How do you learn ML?
Before learning ML, you need to get good command over the some of the initial basic things required like
1. Integral calculus :
You need to know the basics of integral calculus such as how calculus works, limits, differentiation, integration, etc.
2. Linear algebra :
You should know about linear algebra because while learning ML, you will use matrices and vectors in depth along with Eigenvalues and Eigenvectors.
3. Probability :
You need to have command over the probability. You need to know about higher mathematics probability in-depth or in basics to learn ML in a good way.
As you get command over this subject.
You get various platforms to learn ML learning,
1. You can either get yourself registered for diploma, bachelor's, or master's courses in university or local coaching centers
2. Or either you can get yourself registered in online courses on various platforms like Coursera, Udemy, Edx.
You can also learn ML on YouTube for free.
Thus, machine learning is a great career right now, with an average pay of 144k in the United States of America.