Course presentation
In this course, we are going to focus on supervised machine learning and how to apply it using Python programming language. We're going to explore the most common ML models, feature importance calculation, performance metrics, hyperparameter tuning and more.
What is supervised machine learning?
Supervised Machine Learning is a branch of Artificial Intelligence that makes it possible to create predictive models that are able to learn from data and make predictions starting from what they learned. It is able to find the information that hides behind data and to use it for better understanding some business phenomena that would otherwise remain unknown.
How is supervised machine learning used?
There are several practical applications of supervised ML that span across several industries.
For example:
- Product recommendation
- Fraud detection
- Risk management
- Insurance risk
- Predictive maintenance
Is supervised machine learning a good skill to have?
Definitely yes. According to the U.S. BUREAU OF LABOR STATISTICS report of May 2019, the average annual salary for a Data Scientist in the US is $100,560. The modern IT world is increasing its demand for people that can handle data using ML techniques in order to make accurate predictions of business phenomena. These people are called Data Scientists.
What will I learn with this course?
Completing this course you will learn the basic principles of Supervised Machine Learning and its applications in Python, the most common models for regression and classification tasks, the most common performance metrics used for measuring the accuracy of the predictions, the most important techniques to calculate feature importance and how to use it for dimensionality reduction. You will also learn the importance of cross-validation techniques and hyperparameter tuning. 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)
⭐⭐⭐⭐⭐
The course is excellent and very useful. All major subjects are complete and clear. A "must have" if you want to become a skilled data scientist
Course contents
- Introduction to Linear Regression (3:36)
- Linear Regression in Python (6:20)
- Introduction to Ridge Regression (4:20)
- Ridge Regression in Python (5:34)
- Introduction to Lasso Regression (5:15)
- Lasso Regression in Python (4:34)
- Introduction to Elastic Net Regression (4:43)
- Elastic Net Regression in Python (4:07)
- Introduction to Logistic Regression for classification (7:22)
- Logistic Regression in Python (15:19)
- Student quiz
- Regression performance metrics (10:07)
- Regression performance metrics in Python (5:43)
- Pairplot in Python (5:02)
- Binary classification performance metrics (13:07)
- Binary classification performance metrics in Python (9:49)
- Introduction to ROC curve (7:38)
- ROC curve in Python (5:21)
- Multi-class classification performance metrics (7:58)
- Multi-class classification performance metrics in Python (7:38)
- When to use classification performance metrics (6:05)
- Student quiz
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
- Pre-processing techniques
- Scikit-learn Pipelines
In order to achieve the necessary knowledge of data pre-processing techniques used supervised machine learning, we strongly suggest to attend Data pre-processing for Machine Learning in Python course first.
What will I get once I enroll in the course?
After you buy the course, you'll have complete access to the video lessons of this course 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 or with Paypal.
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. Can I access the course?
Sure. As soon as you can ensure the prerequisites, you can follow this course.
Why this course is different from the other ML courses on the web?
Because in this course we're going to see, practically, how to apply Supervised Machine Learning. There is still a theory part in every lesson because you need to know what you are doing, but the course focuses on the practical approach to ML. This kind of approach can help a data scientist apply, in a real-life job, the concepts taught in this course and this is a value we really believe in.
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.