Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as – “Field of study that gives computers the capability to learn without being explicitly programmed”.
In a very layman manner, Machine Learning(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 do we have and what kind of task we are trying to automate.

Example: Training of students during exam.
While preparing for the exams students don’t actually cram the subject but try to learn it with complete understanding. Before the examination, they feed their machine(brain) with a good amount of high-quality data (questions and answers from different books or teachers notes or online video lectures). Actually, they are training their brain with input as well as output i.e. what kind of approach or logic do they have to solve a different kind of questions. Each time they solve practice test papers and find the performance (accuracy /score) by comparing answers with answer key given, Gradually, the performance keeps on increasing, gaining more confidence with the adopted approach. That’s how actually models are built, train machine with data (both inputs and outputs are given to model) and when the time comes test on data (with input only) and achieves our model scores by comparing its answer with the actual output which has not been fed while training. Researchers are working with assiduous efforts to improve algorithms, techniques so that these models perform even much better
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Basic Difference in ML and Traditional Programming?

• Traditional Programming : We feed in DATA (Input) + PROGRAM (logic), run it on machine and get output.
• Machine Learning : We feed in DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.

What does exactly learning means for a computer?

A computer is said to be learning from Experiences with respect to some class of Tasks, if its performance in a given Task improves with the Experience.

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E

Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game

In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.

How things work in reality:-

• • Talking about online shopping, there are millions of users with an unlimited range of interests with respect to brands, colors, price range and many more. While online shopping, buyers tend to search for a number of products. Now, searching a product frequently will make buyer’s Facebook, web pages, search engine or that online store start recommending or showing offers on that particular product. There is no one sitting over there to code such task for each and every user, all this task is completely automatic. Here, ML plays its role. Researchers, data scientists, machine learners build models on the machine using good quality and a huge amount of data and now their machine is automatically performing and even improving with more and more experience and time.
    Traditionally, the advertisement was only done using newspapers, magazines and radio but now technology has made us smart enough to do Targeted advertisement (online ad system) which is a way more efficient method to target most receptive audience.

• • Even in health care also, ML is doing a fabulous job. Researchers and scientists have prepared models to train machines for detecting cancer just by looking at slide – cell images. For humans to perform this task it would have taken a lot of time. But now, no more delay, machines predict the chances of having or not having cancer with some accuracy and doctors just have to give an assurance call, that’s it. The answer to – how is this possible is very simple -all that is required, is, high computation machine, a large amount of good quality image data, ML model with good algorithms to achieve state-of-the-art results.
    Doctors are using ML even to diagnose patients based on different parameters under consideration.

• You all might have use IMDB ratings, Google Photos where it recognizes faces, Google Lens where the ML image-text recognition model can extract text from the images you feed in, Gmail which categories E-mail as social, promotion, updates or forum using text classification,which is a part of ML.

How ML works?

• • Gathering past data in any form suitable for processing.The better the quality of data, the more suitable it will be for modeling
  • Data Processing – Sometimes, the data collected is in the raw form and it needs to be pre-processed.
    Example: Some tuples may have missing values for certain attributes, an, in this case, it has to be filled with suitable values in order to perform machine learning or any form of data mining.
    Missing values for numerical attributes such as the price of the house may be replaced with the mean value of the attribute whereas missing values for categorical attributes may be replaced with the attribute with the highest mode. This invariably depends on the types of filters we use. If data is in the form of text or images then converting it to numerical form will be required, be it a list or array or matrix. Simply, Data is to be made relevant and consistent. It is to be converted into a format understandable by the machine
  • Divide the input data into training,cross-validation and test sets. The ratio between the respective sets must be 6:2:2
  • Building models with suitable algorithms and techniques on the training set.
  • Testing our conceptualized model with data which was not fed to the model at the time of training and evaluating its performance using metrics such as F1 score, precision and recall.

Pre-requisites to learn ML:

• • Linear Algebra
  • Statistics and Probability
  • Calculus
  • Graph theory
  • Programming Skills – Language such as Python, R, MATLAB, C++ or Octave

Nowadays many misconceptions are there related to the words machine learning, deep learning and artificial intelligence(AI), most of the people think all these things are same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same. Let’s see how.

Machine Learning:
Before talking about machine learning lets talk about another concept that is called data mining. Data mining is a technique of examining a large pre-existing database and extracting new information from that database, it’s easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique.

Here’s is a basic definition of machine learning –
“Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision”

Now a days many of big companies use machine learning to give there users a better experience, some of the examples are, Amazon using machine learning to give better product choice recommendations to there costumers based on their preferences, Netflix uses machine learning to give better suggestions to their users of the Tv series or movie or shows that they would like to watch.

Deep Learning:
Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.

The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.

Let’s take an example to understand both machine learning and deep learning –
Suppose we have a flashlight and we teach a machine learning model that whenever someone says “dark” the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word “dark” and as the word comes the flashlight will be on but what if someone said “I am not able to see anything the light is very dim”, here the user wants the flashlight to be on but the sentence does not the consist the word “dark” so the flashlight will not be on. That’s where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.

Artificial intelligence:
Now if we talk about AI, it is completely a different thing from Machine learning and deep learning, actually deep learning and machine learning both are the subsets of AI. There is no fixed definition for AI, you will find a different definition everywhere, but here is a definition that will give you idea of what exactly AI is.
“AI is a ability of computer program to function like a human brain ”

AI means to actually replicate a human brain, the way a human brain thinks, works and functions. The truth is we are not able to establish a proper AI till now but we are very close to establish it, one of the examples of AI is Sophia, the most advanced AI model present today. The reason we are not able to establish proper AI till now is, we don’t know the many aspects of the human brain till now like why do we dream ? etc.

Why people relate machine learning and deep learning with artificial intelligence?
Machine learning and deep learning is a way of achieving AI, which means by the use of machine learning and deep learning we may able to achieve AI in future but it is not AI.

Create a Linear Regression Model in Python using a randomly created data set.

Linear Regression Model
Linear regression geeks for geeks

Generating the Training Set

Machine Learning Model – Linear Regression

The Model can be created in two steps:-
1. Training the model with Training Data
2. Testing the model with Test Data

Training the Model
The data that was created using the above code is used to train the model

Testing the Data
The testing is done Manually. Testing can be done using some random data and testing if the model gives the correct result for the input data.

The Outcome of the above provided test-data should be, 10 + 20*2 + 30*3 = 140.
Output

Outcome : [ 140.]
Coefficients : [ 1. 2. 3.]