Learn how to write Python Classical Machine Learning Learning Algorithms.
Solve Business Problems Practically Using Data Science in this Course. Python Projects for Machine Learning, Data Science, Artificial Intelligence, AutoML, Deep Learning, and Natural Language Processing (Nlp) (Flask, Django, Heroku).
Data science is the study of extracting useful insights from data by combining subject experience, computer abilities, and understanding of mathematics and statistics.
Data scientists use machine learning algorithms for numbers, text, photos, video, audio, and other data types to create artificial intelligence (AI) systems that can execute jobs that would normally need human intelligence.
As a result, these systems generate insights that analysts and business users can employ to create meaningful commercial value.
Data science, AI, and machine learning are becoming increasingly important to businesses. Organizations that want to stay competitive in the age of big data must build and apply data science capabilities quickly, regardless of industry or size.
What you’ll discover
- Learn how to model classification and regression.
- Master Machine Learning and put it to use on the job.
- Create stunning statistical plots with Python using Seaborn.
- Set up the Anaconda data science stack environment rapidly.
Are there any course prerequisites or requirements?
There are no prerequisites for learning machine learning. To comprehend the theory and procedures employed, you must have a background in engineering, physics, math, or statistics.
You must be proficient in mathematics. If you are not, you can still use machine learning, but you will struggle to solve complicated real-world problems.
Many people say you need to know linear algebra, calculus, and so on, but I never learned it and am still able to work on machine learning.
Who should take this course:
- Machine learning novices
- Transitioning from Non-Technical to Data Science
- Job as a Machine Learning Engineer for a Fresher
- Machine Learning Beginner
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