37. Tokenization Basic Part - 2 | Skyhighes | Data Science & Machine Learning in Python

What is in the course?
Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!
This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientist can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hireable in today's workplace environment.
We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.
We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.
This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:
• Programming with Python
• NumPy with Python
• Deep dive into Pandas for Data Analysis
• Full understanding of Matplotlib Programming Library
• Deep dive into seaborn for data visualizations
• Machine Learning with SciKit Learn, including:
• Linear Regression
• Regularization
• Lasso Regression
• Ridge Regression
• Elastic Net
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Natural Language Processing
• Support Vector Machines
• Hierarchal Clustering
• DBSCAN
• PCA
• Model Deployment
and much, much more!
As always, we're grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!
Who this course is for:
Beginner Python developers curious about Machine Learning and Data Science with Python