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Learning path presentation

This learning path is a bundle made by the Data pre-processing in Python modules. In this learning path, we are going to focus on pre-processing techniques for machine learning projects.


What is pre-processing?

Pre-processing is the set of manipulations that transform a raw dataset to make it used by a machine learning model.


Why is pre-processing useful?

Pre-processing is necessary to make our data suitable for some machine learning models, to reduce the problem dimensionality, to better identify the relevant data, and to increase model performance. It's the most important part of a machine learning pipeline and it's strongly able to affect the success of a project. In fact, if we don't feed a machine learning model with the right-shaped data, it won't work at all.


Is pre-processing a good skill to have?

Definitely yes. Sometimes, aspiring Data Scientists start studying neural networks and other complex models and forget to study how to manipulate a dataset in order to make it used by their models. So, they fail in creating good models and only at the end they realize that good pre-processing would make them save a lot of time and increase the performance of their models. So, handling pre-processing techniques is a very important skill.


What's the purpose of a course based only on data pre-processing?

Data pre-processing is the most important part of a machine learning pipeline. Including these lessons inside a larger machine learning course would reduce the perceived value of such topics. Some people think that pre-processing is boring and useless and start with machine learning without caring about how to manage data for their model. That's a great mistake because they don't understand how pre-processing can make their models produce better results. That's why we have created an entire course that focuses only on data pre-processing.


What will I learn with this learning path?

Completing this course you will learn the basic principles of Data-preprocessing and its applications in Python. You'll learn how to fill the blanks in a dataset, how to encode the categorical variables and several types of transformations for the numerical features. You're going to learn how to use Python's Pipelines and how to perform filter-based feature selection and oversampling. Every lesson is made by a brief, theoretical introduction followed by a practical example in Python programming language using Jupyter notebooks.

What people say about this learning path


Javed Shaikh (Udemy user)

⭐⭐⭐⭐⭐

So far, its amazing


Join the course

$100

One-time purchase

Who is this learning path for?


This learning path has been made for:

  • aspiring, junior and senior data scientists
  • machine learning engineers
  • data analysts
  • researchers
  • students
  • everybody who is interested in machine learning and data science


Prerequisites

In order to benefit from the learning path, it's useful to have this previous knowledge:

  • Python programming language
  • Numpy library
  • Pandas library


What will I get once I enroll in the learning path?

After you buy the course, you'll have complete access to the video lessons of its modules and you'll be able to start discussions with the teacher and the other students using the comment section under every lesson.

Your Instructor


My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.

I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.

I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).

I've written many articles about Machine Learning, R and Python and I've been a Top Writer on Medium.com in Artificial Intelligence category.

Frequently Asked Questions

Does the course have a start and a finish date?

No. Once you enroll, you can follow the recorded video lessons when you want.

How can I pay for the course?

You can pay with a credit card using Teachable's payment gateway.

How can I follow the lessons?

Once you pay for your enrollment, you can access the recorded video lessons of the course when you want from your computer using this website. These videos are given in streaming, so you'll need to connect to this website and have an Internet connection in order to watch them. After you create your account and log in, you can use the My Courses link in top of every page to see all the courses you have enrolled in.

I don't know anything about machine learning and data pre-processing. Can I access the course?

Sure. As soon as you can ensure the prerequisites, you can follow this course.

What language will be used?

During this course, the spoken and written language is English.

What if I'm not satisfied?

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.

Join the course

$100

One-time purchase