Simple Way To Learn Machine Learning.

  Part - 4  :-  Students, so now let's add a new tool to our preprocessing toolkit, which is taking care of missing data. So indeed, if we have a look again at our data set data that CSI, we noticed that there is a missing salary here for this specific customer from Germany of 40 years old and who purchased a product. Download Data.csv :-  Download S o generally you don't want to have any missing data in your data set for the simple reason that it can cause some errors when training your machinery model and therefore you must handle them. A first way is to just ignore the observation by deleting it. That's one method and this actually works. If you have a large dataset and you know, if you have only one percent missing data, you know, removing one percent of the observations won't change much the learning quality of your model. So one percent is fine, but sometimes you're going to have a lot of missing data and therefore you must handle them the right way. So th...

Part - 3  :- Students, Now let's learn together how to import a data set. As a reminder, we're going to learn how to import the following data set data that CSV, which is a very simple data set of, let's say, a retail company that is doing some analysis on which clients purchased one of their products. Download Data.csv :-  Download So the rows in this data set correspond to different customers of this employee. And for each of these customers, we have the country, they live in their age, their salary and whether or not they purchased the product. OK, so we're going to learn how to import that GSV on Python, using, of course, to Pendas library. Importing The Dataset :-  So let's first create a new code cell and now let's import this dataset. So the first thing we have to do is to create a new variable and this variable will contain exactly the dataset. Since now we're importing the data set and we want to integrate the data set in a variable, I'm going t...

Part - 2 :-   Students ,  T his is the first very important step of this journey, because indeed, any time you build a machine learning model, you always have a data processing phase to work on. Right. You have to pre-process the data in the right way so that the machine learning model that you're going to build can be trained the right way on the data. Requirements :- Use any python IDE. Ex  :- PyCharm , Google Collab , Jupyter notebook. Learning model, you always have a data processing phase to work on. You know, you're going to learn by doing in this part and therefore for this implementation and each future implementation, we will reimplemented from scratch. And so there you go. Our first implementation will be for all the data processing tools using some Machine Learning Module .   Importing The Libraries / Modules :- These are the libraries we will always use in any machine learning model implementation. So we will include them in the template so that the...

Part -1 :-  Students , In this part we will see little bit of theory about leaning paths , Difference between  AI ,ML and DL as well as regression and  types of regression. CONTENT 1: Learning Paths Hey Data Scientist, Simple Way Learn Machine Learning  is bringing you a new learning experience. We know how difficult it is to carve out a career track so we’re introducing the Simple Way Learn Machine Learning  to guarantee your way to success. This Skill Track is a perfect fit if you: Struggle to determine the skills you need to succeed in this field, Are unsure which courses are right for you, Desire to arrange your learning curve efficiently and on your schedule. Built to deliver streamlined on-the-job success, the Simple Way To Learn Machine Learning    provides structured curriculums for in-demand Machine Learning skills. After completion, All Parts Track students will walk away with the required Machine Learning skills and a complete portfolio...