ML_Easily_Explained

 Today we will explore the mathematical backing of the Machine Learning Algorithm LOGISTIC REGRESSION. I hope you are already well acquainted with the theory behind it. Let’s dive deep inside the pool of mathematics to know what goes behind logistic regression.  As we know Logistic Regression is a binary classifier, consider a two class classification problem consisting of red and blue as two classes, and are separated by a straight line. We assume that our data is linearly separable. The line L has a normal w and intercept b. Original direction of normal to the line is towards red points. If the L passes through origin then b equals 0. We know that a line can be represented in the form of its normal is L: wt*x +b=0 where w is a vector, x is a vector and b is a scalar Now the problem statement is given the data points consisting of red and blue points, we need to find such a line with w and b that separates red points from blue points. How can we approach this problem?? ...

 Let’s start with SQL basics. SQL is a standard language for storing, manipulating and retrieving data in databases. We will revise all the command and syntax used in the SQL. I assume that reader is well acquainted with the SQL or even has some idea about it. I hope you have worked with “imdb” dataset and know how to load it in the MySQL command line client. We will extensively use imdb database in the entire bootcamp               1.   USE : The "USE imdb " command will load the database imdb into SQL command line.              2. SHOW TABLES:             Displays all the tables present in the database.                From here we can see that there are total of 7 tables present in the imdb database.        3. DESCRIBE or DESC:         It describes the structure of ...