ABOUT THIS COURSE
Data science and machine learning are two concepts that fall within the field of technology and using data to further how we create and innovate products, services, infrastructural systems, and more. Both correspond with career paths that are in-demand and high-earning.
The two relate to each other in a similar way that squares are rectangles, but rectangles are not squares. Data science is the all-encompassing rectangle, while machine learning is a square that is its own entity. They are both often used by data scientists in their work and are rapidly being adopted by nearly every industry.
What is data science?
Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services, build better infrastructure and public systems.
This course will equip you with the fundamental machine learning (ML) and artificial intelligence (AI) algorithms and techniques for mining and analyzing data, and extracting insights for data-driven decision making. You will learn how to formulate a ML/AI problem, prepare data, build and optimize predictive models using a wide range of algorithms and evaluate the performance of those models. Throughout the course you will use Scikit-learn and TensorFlow hands-on to build applications that learn from data.
WHAT YOU\’LL LEARN
- Formulate machine learning and AI problems
- Learn techniques to pre-process data for modeling
- Train generalized predictive classification and regression models
- Identify clusters in data such as market segments
- Evaluate and combine models for best performance