Data Science: t-Stochastic Neighbor Embedding in Python
What you'll learn
- Master t-Stochastic Neighbor Embedding in Python
- Become an advanced, confident, and modern data scientist from scratch
- Become job-ready by understanding how t-SNE really works behind the scenes
- Apply robust Data Science techniques for t-Stochastic Neighbor Embedding
- How to think and work like a data scientist: problem-solving, researching, workflows
- Get fast and friendly support in the Q&A area
- No data science experience is necessary to take this course.
- Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.
You’ve just stumbled upon the most complete, in-depth t-Stochastic Neighbor Embedding course online.
Whether you want to:
- build the skills you need to get your first data science job
- move to a more senior software developer position
- become a computer scientist mastering in data science
- or just learn t-SNE to be able to create your own projects quickly.
...this complete t-Stochastic Neighbor Embedding Masterclass is the course you need to do all of this, and more.
This course is designed to give you the t-SNE skills you need to become a data science expert. By the end of the course, you will understand the t-SNE method extremely well and be able to apply it in your own data science projects and be productive as a computer scientist and developer.
What makes this course a bestseller?
Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.
Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete t-Stochastic Neighbor Embedding course. It’s designed with simplicity and seamless progression in mind through its content.
This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the t-SNE technique. It's a one-stop shop to learn t-SNE. If you want to go beyond the core content you can do so at any time.
What if I have questions?
As if this course wasn’t complete enough, I offer full support, answering any questions you have.
This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.
There’s no risk either!
This course comes with a guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.
You either end up with t-SNE skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…
You literally can’t lose.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Ready to get started, developer?
Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced t-SNE brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.
See you on the inside (hurry, t-SNE is waiting!)
Who this course is for:
- Any people who want to start learning t-SNE in Data Science
- Anyone interested in Machine Learning
- Anyone who want to understand how to apply t-SNE in datasets using Python
Lucas is an expert in mathematics and computer science who from a very young age showed a great passion for teaching.
He currently has more than 10 years of experience as a science and technology instructor. He is a specialist in Algorithms, Discrete Mathematics, Artificial Intelligence, Machine Language, among other topics.
Lucas is doing research at the prestigious Polytechnic University of Catalonia UPC in Barcelona.
Throughout his life, he has given multiple lectures at universities and organizations on the teaching of mathematics.
Lucas es un experto en matemáticas y ciencias de la computación que desde muy pequeño mostró una gran pasión por la enseñanza.
Actualmente cuenta con más de 10 años de experiencia siendo instructor de ciencias y tecnología. Es especialista en Algoritmos, Matemática Discreta, Inteligencia Artificial, Lenguaje Máquina, entre otros temas.
Lucas se encuentra investigando en la prestigiosa Universidad Politécnica de Cataluña UPC en Barcelona.
A lo largo de su vida, ha dado múltiples conferencias en universidades y organizaciones sobre la enseñanza de las matemáticas.