Machine Learning tutorial for beginners 2020

Hello, guys how are you all? ,today we are going to learn here about machine learning and we will learn it step by step from beginning to pro.

What is machine learning?

while you listen to the term machine learning you just think it is a type of advanced technology, but believe me, while we will study in depth you will say thank you to me.Because it is going to be very easy for you and helpful content for you.

So Machine Learning is a type of application of AI(Artificial Intelligent) which get ability to perform a task.And with help of Machine Learning they can AI can automatically learn and if needed they can improve it.


It always focuses on the development of computer programs through which it can improve itself and can learn new technology which can change human life in future.


ML occurs when the machine learns something by itself such as: playing chess, OCR (optical character recognition) and other things. And the machine uses algorithms to learn things like: clusters, decision trees, neural networks and other algorithms.

Machine learning allows computers to learn new things and handle new situations through analysis, self-training, observation and experience.

For example

Now everyone is using social media or social networks,so when you see the news feed  or the stories of any profile most it shows you automatically every activity of that particular profile so it is mainly done with the help of ML.

Types of ML(machine learning algorithm)

  1. Supervised machine learning
  2. Unsupervised machine learning
  3. Semi supervised learning
  4. Reinforcement learning

Supervised machine learning:

In this learning, different  types of labeled examples and answers are given in the form of input. The algorithm learns from these examples to predict the correct result based on these inputs.

See in this type of learning mostly work on the behalf of what that machine has learned in their past the machine will implement that learning in it’s today task then this type of learning is known as supervised learning.

Supervised learning algorithms construct mathematical models of a set of data that have both input and desired output. The type of data is known as training data, and consists of a set of examples of training .

Each training example has one or more inputs and desired outputs, also known as a supervisory signal. In mathematical models, each training instance is represented by an array or vector, sometimes called a feature vector, and represented by a training data matrix. 

Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict outputs associated with new inputs.

 An optimal function would allow the algorithm to correctly determine the output of inputs that were not part of the training data. An algorithm that improves the accuracy of its output or predictions over time is said to have learned to perform that task

Let me explain you with the help of example ,

Nowadays, everyone is using email, do you think how email goes in spam .this is the example of supervised learning.

Unsupervised learning

This learning is little bit difficult to supervised learning,because correct answer and label is not given.

Unsupervised is used when information that is neither trained nor lebelled is trained. Unsupervised learning studies that such systems can perform a function. So that it can disassemble a buck structure from the unlabeled data.

 A system does not disassemble any right-output but it uploads the data and the conference draws from their data sets. So that the hidden structure can be disseminated with the help of unlabeled data.

For example

It gave an image of both the buffalo and cow which have not ever been seen .

So the machine has never seen the image of buffalo and cow so it will not be able to recognise the difference between the cow and buffalo.But the machine can categorise them into two parts according to their weight and sizes .

Semi supervised learning

Supervised teaching as the name indicates the presence of a supervisor as a teacher. Fundamentally supervised learning is a learning in which, we teach or train using a machine that is well labeled which means ,that some data is already tagged with the correct answer. After that, the machine is provided with a new set of data so that the supervised learning algorithm analyzes the training data and produces a correct result from the labeled data.

For example 

Suppose you have different types of colorful lollipops and you keep them in a bucket.So the first step will be a supervised learning to determine the different colors one by one like the lollipops which is little small is green color and the biggest lollipops is of red colours and so on.

Now the machine has completed the training process and if you give another object to the machine and ask to identify it then the machine will first identify the object with the previous learning object and compare with that object if matches then it will keep it in lollipops categories and give the output.

If it does not recognise the object it will give the output as invalid.

Reinforcement learning

Reinforcement learning is a region of machine learning.It makes an appropriate model and move to expand for an award in a specific situation.This is given various types of programming and machine to locate the ideal way for a particular issue in a particular circumstance.

It is a type of algorithm which totally interacts with the environment and discovers the rewards and errors.

For example

Suppose you are playing a game and the task is that you have to go to the top of the mount and you will get a diamond when you arrive there .you have given difference path to go there, if you go on wrong path your points will be decrease and if you go on right path then you will get a reward .So the total reward will be calculated when you reach at your destination.

Main points of reinforcement learning:

  1. Input:- The input given to a machine should be an initial state from where the model will start working.
  2. Output:- The output is many possible solutions as an output for a particular solution.
  3. The models keep learning.
  4. The reward will depend on the total work of the player.

Types of reinforcement learning

  1. Positive
  2. Negative

Machine Learning vs Deep Learning

example

 Let’s start with the classic example of dogs and cats. Do you see dog or cat in this photo? can you answer?

You can answer it ,Because you have  seen many cats and dogs. You know how they look like. Basically this is what we are trying to get the computer Learning from examples and identifying.

I want to remember Even humans sometimes misrecognize Computers are similar Is to make an error. If you want the computer to be identified by machine learning. We manually work from the image Select relevant features Select image features such as edges and corners.

Train a machine learning model And when analyzing and identifying new objects the model refers to those features.

This is an example of object recognition ,but this can be also for scene recognition and object detection.

Solve machine learning challenges Do it in a specific workflow First, start with the image Extract relevant features from the image Next, represent an object Create the expected model On the other hand.

But in deep learning No need to manually extract features from images Deep learning images instead By giving it directly to the algorithm Can predict objects Deep learning is part of machine learning Can handle images directly Sometimes it can be more complicated Since then, the term machine learning has been Means other than the category of deep learning.

When to choose machine learning or deep learning High performance GPU and large amount of labeled data Check if there is If you don’t have any of these From deep learning.

Machine learning would be suitable It ’s common for deep learning Because it is more complicated Get reliable results Because you need thousands of images.

Take a tour of Artificial Intelligent

Real world examples of Machine Learning

  • Google
  • Uber
  • Siri & Cortana
  • Netflix
  • Image Recognition
  • Speech Recognition
  • Medical diagnosis
  • Financial Services,etc.
  • Auto driving car

Advantages of ML

  • i). There are lots of applications of machine learning.For example finance,health,banking sectors etc.
  • ii). The recommendations comes from YouTube videos and Facebook advertisement. These are done with the help ML ,because it is auto suggested according to your past activity.
  • iii).With the help of ML we can complete our tasks in a peaceful manner and time utilization.

Disadvantages of ML

  • i).We can not make it confirm that the algorithm will work in an imaginable condition.
  • ii). ML required a lots of data to perform it’s task ,and we can say that with lot’s of data ,it might be difficult to execute the algorithm.
  • iii).It does not have lot’s of variety .so it is limited in work.

Machine learning algorithm

Here are some common algorithm of ML:-

  • Regression algorithm
  •  Instance-based Algorithms
  •  Regularization Algorithms
  • Decision Tree Algorithms
  • Bayesian Algorithms
  • Clustering Algorithms
  • Association Rule Learning Algorithms
  • Artificial Neural Network Algorithms
  • Deep Learning Algorithms
  • Dimensionality Reduction Algorithms
  • Ensemble Algorithms

Future scope of ML

The future of Machine Learning is brilliant .There is no limit of technology in this world ,and ML is that much important for us. Because with the help of ML we can change the life of human beings and we can create a brighter future for us.

Actually, this is the application of artificial intelligence. In addition, it allows software applications to be accurate in predicting results. In addition, machine learning focuses on the development of computer programs. … Google says “machine learning is the future”, so the future of machine learning is going to be very bright.

The future of machine learning looks promising. There is an urgent need for professionals training Deep Learning and AI jobs and match machine learning requirements. If you want to become one of those professionals, prepare yourself by being certified and industry-ready because the sooner you receive your training, the sooner you will work in this exciting and rapidly changing field.

So take a deep breath and make your career in the field of ML.

Jobs in the field of ML

  • Machine Learning Engineer
  • Analyst
  • Architect
  • Scientist
  • Business Intelligence Developer
  • Human-Centered Machine Learning Designer
  • Internet Of Things

Conclusion

Hope you like this article and may i full fill your requirement of ,your knowledge.If you like this article do like and share it with your friends also and spread knowledge to make a better future with your friends and peoples.

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