Introduction to Machine Learning

Introduction:

Machine Learning or automatic learning is a scientific field, and more specifically a subcategory of artificial intelligence.It consists of letting algorithms discover “patterns”, namelyrecurring patterns, in data sets. This data can be numbers, words,images, statistics.Anything that can be stored digitally can serve as data for Machine Learning.By detecting patterns in this data, algorithms learn and improve their performance in performing a specific task.

Machine Learning (ML) is The science of automate a task using some autonomous bots created by several programming languages.

(python, C++, Java, R,  LISP ... and many others )

Basics of Machine Learning.

1.Mathematics:

·         Linear Algebra

·         Statistics

·         Calculus

·         Probability Concepts.

These are the basic Mathematics that are required for Machine Learning even though all the Math concepts are important Most important of them is ‘Linear Algebra.’

2. Programing Language:

Learn a Programming Language and try to master that Language there are two important & easy Language are :

·         Python

·         R

“The Recommended Language is the Python because its very easy to learn and in machine learning it is very helpful for applying the algorithm.”

We can also use the c++ language for machine learning but it becomes very complex in many things.

Also the programming for machine Learning you can learn from:

·         Tensor Flow

·         Scikit Learn.

3.Libraries of Python.

Learn the Libraries of Python the highly recommended and useful are:

·         Pandas

·         Numpy

·         Matplotlib ,etc

4.SQL:

SQL stands for (Structured query Language).

Why SQL?

Answer: Machine learning doesn't happen without data. You need it to train your algorithms, and the more data, the better. So, you need a place to store large quantities of information, and a way to get it into your algorithms so they can analyze and learn from it. That's where SQL comes in.

 5. Machine Learning Algorithms:

Learn the machine learning Algorithms which you can Learn from the book:

Recommended Book:

Name: ”Hands on Machine Learning with scikit learn and Tensorflow”.

6.Learn Deployment:

Machine-learning (ML) deployment involves placing a working ML model into an environment where it can do the work it was designed to do. The process of model deployment and monitoring takes a great deal of planning, documentation and oversight, and a variety of different tools.

SO, guys these are some basics For the Macine Learning that are Compulsory.

Also given below are some types and Algorithms of Machine Learning.

Types of ML :

·         -Supervised Learning

·         -Unsupervised Learning

·         -Reinforcement Learning

List of Common Machine Learning Algorithms :

1.      Linear Regression

2.      Logistic Regression

3.      Decision Tree

4.      SVM

5.      Naive Bayes

6.      kNN

7.      K-Means

8.      Random Forest

9.      Dimensionality Reduction Algorithms

10.  Gradient Boosting algorithms.


Summary:

·        Introduction to Machine Learning.

·        Basics of Machine Learning.

·        Types of Machine Learning.

·        List of common Machine Learning Algorithm.

·        Road Map to Machine Learning Engineer Photo.

 

So, Thank you guys if at any point I am wrong in this Blog please correct me!