Welcome to SciKit-Learn in Python for Machine Learning Engineers, the fourth course in a series designed to help you gain a solid foundation in the machine learning space through Python. With the ever-expanding data landscape requiring more sophisticated tools and techniques, mastery of machine learning models is becoming increasingly important for data engineers, machine learning engineers and data scientists alike.
The Course Overview
The Django Framework is one of Python's most popular web frameworks for scalable, maintainable web application development. Django is designed to make web development faster, easier, and more secure. This course is a comprehensive introduction to building web applications using Django. It covers beginner to advanced level concepts such as views, templates, authentication, testing, and deployment.
Course Content
The course content is designed to take you through the foundational concepts of SciKit-Learn using a lab-integrated approach, allowing you to program and build models in real-time. The course content covers basic terminologies in SciKit-Learn such as scoring models, the creation of traditional machine learning models, and everything in between. It is essential to take the courses serially to understand the concepts and their interrelationships. The course will help you gain core foundations of a machine learning library in Python called SciKit-Learn and apply what you learn by building many traditional machine learning models in SciKit-Learn.
The course is focused on building traditional machine learning models in SciKit-Learn. As with the previous courses, this one is also an applied course on machine learning. By completing the labs, you will get the opportunity to build on what you learned in the previous lesson and eventually build a substantial understanding of SciKit-Learn. To get the most out of the course, you are advised not to skip any lab exercises.
Upon completion of the course, you should be able to:
- Understand SciKit-Learn basics from A-Z
- Build traditional machine learning models in SciKit-Learn
- Solve real-world interview questions accurately
- Learn the vernacular of building machine learning models, which is essential to be able to communicate effectively during development discussions
Student Testimonials
Students who have already taken the course have been enthusiastic about the content and the quality of the lab exercises and have given high ratings to the course. They confirm that the instructor is very knowledgeable and explains the material clearly and concisely, making the course accessible to students with varying levels of technical backgrounds.
"Instructor is very knowledgeable about the material and explains it clearly and to the point. Also, gives very good practical examples." - Diana
"As usual, Mike provides a well made course to teach you about SciKit. The lessons are very short, so you are able to absorb the information, and the follow-up labs help anchor what you learned. I will be going over this course again because the information is a bit advanced, but I already have a great understanding and feeling for SciKit after my first run-through of the course. It is recommended you do take the 3 previous courses before you start this one because they build on each other. Mike West is a top instructor on the subject of python and data, and his courses are worth the time and spent." - Joseph
"So far, so good. The quick lectures throw out a lot of information, so I typically watch them again later. Good course thus far." - Ted
Why Learn SciKit-Learn in Python?
Python has become the gold standard for building machine learning models in the applied space, and SciKit-Learn has become the gold standard for building traditional models in Python. The term "applied" simply means real-world scenarios. The popularity of Python in the ML and data engineering fields has skyrocketed due to its user-friendliness, versatility, and the abundance of libraries, including SciKit-Learn.
Machine learning is a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed”. If you are interested in working as a machine learning engineer, data engineer, or data scientist, then you must know Python. Python is a high-level language designed with ease of learning in mind and has many applications outside the ones we are interested in.
Benefits of Learning SciKit-Learn with Python
Now that you understand why learning SciKit-Learn is essential, let's explore the various benefits of this approach.
The Growth of Data is Insane: As stated earlier, almost all real-world machine learning is supervised. That means you must point your machine learning models at clean tabular data, which is instrumental in the creation of traditional machine learning models. The amount of data being generated is growing exponentially, and thus, data and machine learning engineers are in high demand.
Machine Learning in Plain English: Data engineers and machine learning engineers are expected to build machine learning models. In this course, you will learn enough Python to build a deep learning model. Understanding the basics of machine learning is essential to landing a job in the field of data engineering.
You Want to be a Machine Learning Engineer: Machine learning engineering is one of the most desirable careers globally, with significant growth potential. It offers a lot of benefits, including the freedom to move anywhere you'd like and being compensated for your efforts. The profession also provides the opportunity to work remotely, among others. Without a fundamental understanding of Python, you will have a hard time securing a position as a machine learning engineer.
The Google Certified Data Engineer: Google is known for its cutting-edge technological advancements, and as such, they are the first and only cloud vendor to have a data engineering certification. To become a data engineer, a robust understanding of Python is essential.
You Want to be Ahead of the Curve: The data engineering and machine learning engineer roles are fairly new. By becoming certified in these courses, you are positioning yourself to take advantage of the burgeoning field. Being first to deliver the certifications and build robust skill sets is vital to receive top compensation packages and move up the career ladder fast.
Wrapping Up
In the course, you'll get a comprehensive introduction to building web applications using Django, from beginner to advanced level concepts such as views, templates, authentication, testing, and deployment. Upon completion, you'll have the basic terminologies in SciKit-Learn, score models, create traditional machine learning models, and understand the vernacular of building machine learning models.
The course mentor's expertise and the lab-integrated approach allow students to grasp and apply their learnings to real-world scenarios. Thus, enabling perfect delivery of the course for both beginners and advanced learners.
