What is Ml (Machine Learning)? Everything you need to know

Machine Learning is basically a method of teaching a computer – how to make predictions based on some data. It is a branch of artificial intelligence that automatically improves data without actual programming. It takes Historical Data As Input To give improved data as output or Machine Learning is a scientific study of algorithms and statistical models that computer systems use to perform a specific task without using external programming. It is Seen as a Subset of Artificial Intelligence 


What is Ml (Machine Learning)?

Data processing steps in machine learning

1.Data Collection – We Humans Collect information from books, Internet to increase our Knowledge. In a similar way to teach a machine historical data is collected and then data is given to the machine as input 
2. Data Preparation – After collecting data. Data is refined so that all the unnecessary data gets removed from the collected data. So that machine learns only from useful data 
3. Teaching – Now machine learning model is been taught with the collected data so that machine learning model can process that data and gives the processed data as output 
4.Testing – After successful teaching the Machine learning Model Now, The model is been tested by giving the model a specific task to perform 
5. Optimizing – Nobody is perfect at first not even the machine so after testing the machine. Machine is then optimized if any failure occurs while performing the specific Task 
6. Final Testing – After Optimising, Final Testing is done by giving machine a specific task to perform 

Types of Machine learning

1.Supervised Learning-

Supervised Learning is one of the types of machine learning in which input and output are pre defined to the machine. It infers a function from label training data consisting of a set of training examples for example you know what your teacher is going to teach you in school but you still go to school and your teacher teaches you the same thing

2.Unsupervised Learning-

 It is a type of machine learning in which the machine is not aware about any input or output , Machine Starts learning on its own without pre-existing labels. It is also known as self-organization  For Example – There is a student which has everything to learn but there is no teacher to teach him, Which means the student has to study Everything By himself

3.Reinforced Learning-

It is one of the types of Machine Learning which is only  concern with how to take actions in an environment So that they can optimize or maximize some notations of cumulative reward for example – Neither the teacher have data to teach nor the student knows how to study, Student will himself start learning from very basic that what is pencil how to write with it and so on 

Applications of Machine learning

Today Machine Learning is being used in multiple fields, And many companies and firms are opting for machine learning models instead of humans for more precision of work 
1. Image Recognition – Machine Learning is widely used in image recognization or face recognization based on the previous data 
2.Speech Recognition – Machine Learning is used in Speech Recognition. Machine learning models recognize speech and type it automatically. machine learning is also used for translating languages
3. Medical Diagnosis – Machine learning is used in the diagnosis of disease based on the symptoms of the disease
4.Fiance And Business – Machine Learning has a wide application in this field as it allows banks and Business models to make smarter decisions 
5. Prediction –  Machine learning models are used to predict weather forecast based on the historical data and current data 
And so on, there are end number of application of machine learning 

Best language for machine learning

Best Programming Language For Machine Learning – A number of programming languages can be used for machine learning but if you are looking for the best or most used programming language then below is the list of top 5 programming languages used for machine learning
Best language for machine learning





5.Java Script

Ai vs Machine learning

Machine learning is a subset of artificial intelligence which means ml is a part of Artificial Intelligence Now If you want to Know What is Artificial Intelligence you can find it here
Now What is the main difference between Ai and Machine learning
1.AI is much broader and has a wide scope on the other hand machine learning has limited scope
2. Ai models are created to solve real-life complex problems model which enacts human intelligence and can solve multiple problems on the other hand machine learning models are limited to the particular task for which they are trained for
3.AI can learn, analyze and self-correct the data but on the other hand, ml can only learn and analyze the data by itself
4.AI is divided into two subcategories – Narrow AI and Artificial General Intelligence on the other hand Machine learning is divided into three subcategories – Supervised Learning, Unsupervised Learning, Reinforced Learning
5. Example Of Ai models – Siri, Alexa, Google Assistant
, etc on the other hand examples of machine learning are – email and malware detected, Voice Recognition systems, online customer support, Autorecommenndation systems, etc

Machine Learning Engineer Salary

Machine Learning is the most in-demand field and offers one of the highest paying jobs
So there is a great scope for machine learning in the future because more and more companies are opting for machine learning models to increase their productivity and to maintain ml models there will be an increase in demand for machine learning engineers
salary of machine learning engineers vary from country to country

1. US

Salary Of a machine learning engineer in the US varies between $78k to $150k with the average salary being $114k

US Machine Learning Engineer Salary

2. India

Salary Of a machine learning engineer in India varies between Rs 373k to Rs 1582K with the average salary being Rs786K
India Machine learning Engineer Salary

3. UK

Salary Of a machine learning engineer in the UK varies between 32k to 80k pound with the average salary being 50k pound
UK Machine learning Engineer Salary

Best Courses On Machine Learning

1. Andrew ng machine learning Course – Coursera

Andrew Ng Is the co-founder of Coursera and professor at Stanford University. He is the founder of Landing AI,

Why to Choose This Course

Andrew Ng Machine learning  Courses is one of the highest-rated courses on Coursera with more than 3.6M people enrolled. This is a 60 Hours long Course and you will get a certificate after completion of this course which is too signed by Andrew ng.

What You will learn in this course

👉logistic Regression

👉Artificial Neural Network

👉Machine Learning(ML) Algorithms

👉Machine Learning

Note: This is not a free Course, You can enroll in this course for free but you won’t get any certificate in that case. If you want this course for free and a certificate along with a free course then you can apply for financial aid

2.Google machine learning crash course

Google’s Machine Learning Crash Course is one of the best Crash Course available on the internet. As this course is offered By Google itself so you can fully trust this course in terms of quality of content
Why to Choose This Course 
This is google’s very own machine learning course you will be taught by google Researchers. This is a 15 hour Long Course with more than 25 Videos, 30 Exercises to make your concepts crystal clear. The best Part Of this course is that it is a free course which means you don’t need to pay a single penny to google to get this course
Note: You don’t need any prior knowledge in machine learning to start with this course but you should know at least one programming language in-depth to understand this course

3.Machine Learning – edX

Machine learning course by edx is another great course on machine learning, Course is instructed by the professor of Columbia University . This is a 100-hour long course with more than 150k students enrolled. If you are not new to machine learning then this is the best course for you as this is not a beginner level course  there are some prerequisite to start with this course – Calculus, Linear algebra, Probability, coding, data manipulation

Now, What you will learn in this course

👉Supervised learning techniques for regression

👉Unsupervised learning techniques for data modeling and analysis

👉Probabilistic versus non-probabilistic viewpoints

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