Visualization: Machine Learning in Python
What you'll learn
- Master Visualization and Dimensionality Reduction in Python
- Become an advanced, confident, and modern data scientist from scratch
- Become job-ready by understanding how Dimensionality Reduction really works behind the scenes
- Apply robust Machine Learning techniques for Dimensionality Reduction
- Master Machine Learning Tools such as PCA, LLE, TSNE, Multidimensional Scaling, ISOMAP, Fisher Discriminant Analysis, etc.
- How to think and work like a data scientist: problem-solving, researching, workflows
- Get fast and friendly support in the Q&A area
- Practice your skills with 10+ challenges and assignments (solutions included)
- 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 Visualization/Dimensionality Reduction course online.
Whether you want to:
- build the skills you need to get your first Data Scientist job
- move to a more senior software developer position
- become a computer scientist mastering in data science and machine learning
- or just learn dimensionality reduction to be able to work on your own data science projects quickly.
...this complete Dimensionality Reduction Masterclass is the course you need to do all of this, and more.
This course is designed to give you the Visualization/Dimensionality Reduction skills you need to become an expert data scientist. By the end of the course, you will understand Visualization/Dimensionality Reduction extremely well and be able to use the techniques on your own projects and be productive as a computer scientist and data analyst.
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Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.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 Visualization/Dimensionality Reduction 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 Visualization/Dimensionality Reduction techniques and master data science. It's a one-stop shop to learn Visualization/Dimensionality Reduction. If you want to go beyond the core content you can do so at any time.
Here’s just some of what you’ll learn
(It’s okay if you don’t understand all this yet, you will in the course)
All the essential Visualization/Dimensionality Reduction techniques: PCA, LLE, t-SNE, ISOMAP... Their arguments and expressions needed to fully understand exactly what you’re coding and why - making programming easy to grasp and less frustrating.
You will learn the answers to questions like What is a High Dimensionality Dataset, What are rules and models and to reduce the dimensionality and Visualize complex decisions
Complete chapters on Dimensionality of Datasets and many aspects of the Dimensionality Reduction mechanism (the protocols and tools for building applications) so you can code for all platforms and derestrict your program’s user base.
How to apply powerful machine learning techniques using Dimensionality Reduction.
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 full 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 Visualization/Dimensionality Reduction 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 Data Science 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, Visualization is waiting!)
Who this course is for:
- Any people who want to start learning Dimensionality Reduction in Machine Learning
- Anyone interested in Machine Learning
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets
- Any students in college who want to start a career in Data Science
- Any people who are not satisfied with their job and who want to become a Data Scientist
- Any data analysts who want to level up in Machine Learning
- Any people who want to create added value to their business by using powerful Machine Learning tools
Lucas is an expert in the fields of mathematics and computer science, driven by a lifelong passion for teaching. With over a decade of experience as a science and technology instructor, he has become a renowned specialist in subjects such as Algorithms, Discrete Mathematics, Artificial Intelligence, and Machine Learning, among others.
Currently researching at the prestigious Polytechnic University of Catalonia UPC in Barcelona, Lucas is committed to advancing his knowledge and expertise even further. Throughout his life, he has given multiple conferences at universities and organizations on the teaching of mathematics., inspiring and mentoring countless students along the way.
Lucas is dedicated to a mission of advancing humanity's knowledge of technology and science, using his expertise to develop solutions that benefit society as a whole. His work is driven by a deep desire to create innovative technology that truly serves the needs of humanity.
Overall, Lucas's exceptional qualifications, extensive experience, and unwavering dedication to his mission make him a highly respected figure in the world of STEM education and research. His impact on the field is sure to be felt for many years to come.
Lucas es un experto en matemáticas y ciencias de la computación, impulsado por una pasión por la enseñanza de toda la vida. Con más de una década de experiencia como docente de ciencia y tecnología, se ha convertido en un reconocido especialista en temas como Algoritmos, Matemáticas Discretas, Inteligencia Artificial y Aprendizaje Automático, entre otros.
Actualmente cursando sus estudios en la prestigiosa Universidad Politécnica de Cataluña UPC en Barcelona, Lucas está comprometido a avanzar aún más en su conocimiento y experiencia. A lo largo de su vida, ha dado múltiples conferencias en universidades y organizaciones sobre la enseñanza de las matemáticas, inspirando y asesorando a innumerables estudiantes en el camino.
Lucas está dedicado a la misión de hacer avanzar el conocimiento de la humanidad sobre tecnología y ciencia, utilizando su experiencia para desarrollar soluciones que beneficien a la sociedad en su conjunto. Su trabajo está impulsado por un profundo deseo de crear tecnología innovadora que realmente sirva a las necesidades de la humanidad.
En general, las calificaciones excepcionales de Lucas, su amplia experiencia y su inquebrantable dedicación a su misión lo convierten en una figura muy respetada en el mundo de la educación STEM y la investigación. Su impacto en el campo seguramente se sentirá durante muchos años por venir.