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Skillset, topics, projects and virtual internships for DS

This post is for those who are beginner and do not have any idea about topics that they need as a beginner DATA SCIENCE/ DATA ANALYST.  I am also facing the same problem before a year ago and till date I have some relevant knowledge about data science and also have some projects.  People are saying the we need so many skills like Mathematics, Programming language, some cloud concepts too. Actually they are right. Being a Data Scientist is not like being a web developer or a front-end developer that have limited skill set.  In this post I will tell you the exact topics that you need to learn at beginner level. MATHEMATICS Descriptive Statistics, distributions, hypothesis testing and regression analysis. Bayesian Thinking, conditional probability, priors, maximum likely hood. Vectors and matrices Matrices operations Eigenvalues and eigenvectors Linear and non linear functions Multivariable calculus  PROGRAMMING LANGUAGE(Python or R)    Data types, String operations, Expressions and varia
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GRADIENT DESCENT IN MACHINE LEARNING | WORKING OF GRADIENT DESCENT ALGORITHM

Gradient descent is the most popular algorithm and its concept is used in so many machine learning topics like regression etc.


After reading this post, you have an idea about the following topics.
1. What is gradient descent ?
2. How does it work ?
3. How gradient descent are used in machine learning.

Gradient Descent

Gradient descent is an optimization algorithm that works iteratively  and use for finding local minimum or global minimum of an differentiable function.

Working of gradient descent


Suppose a person is on the top of the mountain(let A in above figure) and he want to come down to the ground. 
From top of the mountain, he will go to that point which will less then his previous height.
After that again, he will search for that point which will less than his current height.
After the number of jumps, he will come down to the ground, that's how gradient descent algorithm works iteratively.

Let's see formally,


This is the mathematical formula for calculating gradient descent.

:= is the assignment operator that is assigning new point to the previous point.

If the value of 'alpha' should not be very large otherwise our model never reach on convergence.

Here is the 3d graph that is showing, how this algorithm works.

 "The above showing picture is the snapshot from Andrew ng Machine learning"

In vertical axes, we have our cost function J(Theta 0, Tbeeta 1).

How Gradient Descent use in machine learning 

As we know this algorithm is use for getting local mnimum, let's take an example of linear regression. We want to draw a best fit line in for a dataset... then gradient descent work for coming points near the line by applying above mentioned algorithm. 
In this figure, we will minimize the distance of the red circle points ( the same apply for all points) from the best fit line. 

I hope, now you have better understanding regarding gradient descent algorithm. 

If you have any doubt, let me know in comment section. 

Thank you for reading this post.
















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