A brief description
Are you looking for a high-paying Data Scientist job?
Or are you an accomplished AI professional trying to advance your career?
Or are you a budding computer scientist who needs to learn Data Science and Artificial Intelligence from the ground up?
This course is for you if you answered yes to all of these questions.
Computer Science is one of the most in-demand engineering areas right now! Opportunities and job possibilities abound in this area. Data science is also commonly used in a number of fields, including banking, healthcare, airlines, logistics, and technology.
The aim of this course is to provide you with realistic, simple, and enjoyable knowledge of key aspects of data science applications in industry. Students can obtain realistic hands-on experience with real-world databases in this course.
1.Task #1 @Predict Prices in the Airlines Industry: Create an AI model to predict airline fares on different routes.
2.Task #2 @Predict the Password’s Strength: Decide if the password is Solid, Decent, or Bad.
3.Task #3 @Establish Time Series Forecasting Models to Predict Future Stock Prices: Develop time series forecasting models to predict future stock prices.
What are the benefits of taking this course?
It describes Projects based on real-world data and problems. There are no toy data! This is the quickest and most effective way to become a Data Scientist, AI Engineer, or Machine Learning Engineer.
It displays and describes the whole set of real-world data. Starting with importing sloppy data, cleaning it, combining and concatenating it, sorting and aggregating it, Exploratory Data Analysis, and finally planning and analyzing it for Statistics, Machine Learning, Natural Language Processing, and Time Series and Data Presentation.
It provides you with many chances to learn and code independently. Doing is the best way to learn.
This course is intended for the following individuals:
Anyone interested in Data Science, Artificial Intelligence, Machine Learning, Natural Language Processing, and Time Series Analysis.
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