Machine Learning For Beginners From Zero Level

Machine Learning is the term which we have been Hearing Now everywhere. When I Started My ML Journey Back in April 2016 I was like complete Noob having No Idea what ML is I used to Think does ML means that Machine are learning ?But Then how can Machine learns By themselves? All Such silly questions came to My Mind when I started with. We Have heard people talking about machine learning but only have a fuzzy idea of what that means.

So this is a kind of Guide for them who are willing to start there Journey with ML here we will have Different Modules each Covering Topic of ML.

Targeted Audience: Beginners and/or Machine Learning Fresh Bloods.

Road Map:

Module-1 Introduction To ML

1. What is Machine learning ?
2. What are Real Scenarios where ML is Used?
3. Types of ML Algorithm?
4. Difference between Supervised Learning, Unsupervised Learning?
5. Steps Involved In Machine Learning?
6. List Of Common Machine Learning Algorithm

“Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines” — Yann LeCun, Director of AI Research, Facebook

So Starting With First question what is ML?

Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. and later improve from experience without being explicitly programmed. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

So How Does Process of Learning Begins ?

The process of learning begins with feeding of data, Giving instruction, in order to look for patterns/Observation in data and later make better decisions in the future on new examples/Data that we provide.

Machine “learns” really by using old/Past data to get information about whats the most likelihood that will happen. If the old data looks a lot like the new data, then the things you can say about the old data will probably be relevant to the new data. It’s like looking back to look forward.

Application of Machine Learning Algorithm in Real Scenarios?

1. Retail :

Machine learning algorithms are probably behind some of online retailers Companies such as Amazon use this technology to offer a highly-personalized service:Online recommendations. Machine learning allows retailers to offer you personalized recommendations based on your previous purchases or activity.

2.Voice recognition systems such as Siri and Cortana use machine learning to imitate human interaction.

3 Google Maps analyze speed of Traffic through anonymous location data from smartphones .This enables Google to reduce travel time by suggesting Faster Routes.

4. Google Search engine offers recommendations based on previous user search

5 . Spam Detection: Given email in an inbox, identify those email messages that are spam and those that are not. Having a model of this problem would allow a program to leave non-spam emails in the inbox and move spam emails to a spam folder.

Types of ML Algorithm?

Machine Learning Algorithm Basically can be Divided into three

1. Supervised Learning(Task Driven)
2. Unsupervised Learning(Data Driven)
3. Reinforcement Learning(Learning From Environment)

Suppose we have Data set having columns X,Y where X is input variable and Y Is output variable

Supervised learning is where we have input variables (x) and an output variable (Y) and weuse an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that we can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Supervised learning Example:

• suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place.

• suppose the fruits are apple,banana,cherry,grape.

• so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place.here your previous work is called as train data in data mining.

• so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit.

• This type of data you will get from the train data.

• This type of learning is called as supervised learning.

• So you already learn the things so you can do you job confidently.

unsupervised learning unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place.

• This time you don’t know any thing about that fruits, you are first time seeing these fruits so how will you arrange the same type of fruits.

• What you will do first you take on fruit and you will select any physical character of that particular fruit. suppose you taken color.

• Then you will arrange them base on the color, then the groups will be some thing like this.

• RED COLOR GROUP: apples & cherry fruits.

• GREEN COLOR GROUP: bananas & grapes.

• so now you will take another physical character as size, so now the groups will be some thing like this.
• here you didn’t know learn any thing before means no train data and no response variable.
• This type of learning is known as unsupervised learning.

Difference between Supervised Learning, Unsupervised Learning?

Supervised Learning: When teaching an agent something you provide with an answer sheet where agent can see the answer after its own calculation if answer is right it will do the the same thing for That Particular range of Input. Example Teacher Teaching to Students.

Unsupervised Learning: There is no answer sheet and agent is all his own .It will try to curb itself. Self-Study No teacher available.

Common Steps Involved in Machine learning?

There are 5 basic steps used to perform a machine learning task:

1. Collecting data:
2. Preparing the data
3. Training a model: This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts — train and test (proportion depending on the prerequisites); the first part (training data) is used for developing the model. The second part (test data), is used as a reference.
4. Evaluating the model
5. Improving the performance.

List of Common Machine Learning Algorithms

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM
5. Naive Bayes
6. KNN

7. Random Forest

8. Dimensionality Reduction Algorithms

9. Gradient Boost & Ada boost

10. Time Series Modelling

In Next Module we will discuss Reinforcement Learning and Steps Involved In Machine Learning in Detail.Happy Learning

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