Course presentation

In this course, 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 I have created an entire course that focuses only on data pre-processing.



What will I learn with this course?

  1. 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 course


Roberto (Udemy student)

⭐⭐⭐⭐⭐

This course is excellent. I recommend it to anyone who wants to become a data scientist. The teacher covers the topics with clarity and synthesis, both in theory and in practice. I particularly liked the practical examples in Python.


Javed Shaikh (Udemy student)

⭐⭐⭐⭐⭐

So far, its amazing


Chrislen  (Student)

⭐⭐⭐⭐⭐

This is very good course


Ojo Babalola  (Student)

⭐⭐⭐⭐⭐

This course is really good.


Course contents


  Introduction
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  Data cleaning
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  Encoding of the categorical features
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  Transformations of the numerical features
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  Pipelines
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  Scaling
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  Principal Component Analysis
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  Filter-based feature selection
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  A complete pipeline
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  Oversampling
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  General guidelines
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  End of course
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Join the course

$200

One-time purchase

6 payments of $40/month

Monthly payment plan

6 monthly transactions. Please note that once started, the payment schedule cannot be interrupted until the final transaction.

Who is this course for?


This course 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 course, it's useful to have this previous knowledge:

  • Python programming language
  • Numpy library
  • Pandas library


What will I get once I join the course?


After you buy the course, you'll have complete access to the video lessons 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.


I can't afford the whole price. What can I do?

You can choose the 6-month payment plan. Each month, a transaction is automatically charged to you for a total of 6 monthly transactions. You can access the course immediately and pay during the next months according to the schedule. Please note that once started, the payment schedule cannot be interrupted until the last transaction. Once selected, the payment method (Paypal, credit card) cannot be changed. If a transaction fails, you'll be unenrolled from the course. More details here.


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

$200

One-time purchase

6 payments of $40/month

Monthly payment plan

6 monthly transactions. Please note that once started, the payment schedule cannot be interrupted until the final transaction.