RealWorld MachineLearning Learn Python’s traditional machine learning learning algorithms.
Practically Apply Data Science to Business Problems in this Course. Using Python, learn how to create and deploy projects for machine learning, data science, artificial intelligence, automatic machine learning, deep learning, and natural language processing (NLP) (Flask, Django, Heroku).
Data science is a discipline of study that combines subject-matter knowledge, programming abilities, and competence in math and statistics to draw forth important insights from data.
Data scientists use machine learning algorithms on a variety of data types, including numbers, text, photos, video, and audio, to create artificial intelligence (AI) systems that can carry out activities that often require human intelligence. The insights these technologies produce can then be transformed into real commercial value by analysts and business users.
Data science, AI, and machine learning are becoming increasingly important to businesses. No of their size or industry, businesses must quickly create and deploy data science capabilities if they want to be competitive in the big data era. Otherwise, they run the danger of falling behind.
What you’ll discover
- Recognize classification and regression modeling techniques
- Learn how to apply machine learning at work.
- Python can be used to produce stunning statistical charts with Seaborn.
- Utilize the Anaconda data science stack environment to start up rapidly.
Exist any prerequisites or course requirements?
To learn machine learning, there is no particular precondition. However, in order to comprehend the theory and the methods employed, you must have a background in engineering, physics, math, or statistics. You must be proficient in mathematics. If not, you can still use machine learning, but you may have trouble solving challenging problems from the real world. Many claim that you must know calculus, linear algebra, and other subjects, yet despite never having taken them, I am still able to work on machine learning.
Who should take this course:
- Machine learning newbies
- switching careers from non-technical to data science
- a position in machine learning for new graduates Engineer Learning Machines for the First Time