Master Data Science & AI in 100 Days with Hands-On Projects, Real Case Studies, and Career-Ready Skills
Sub Category
- Data Science
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Objectives
- Master Python programming, statistics, and data handling as the foundation for Data Science & AI
- Perform data cleaning, feature engineering, and exploratory data analysis (EDA) with real-world datasets
- Build and evaluate machine learning models for regression, classification, clustering, and forecasting
- Apply deep learning with Neural Networks, CNNs, RNNs, and LSTMs using TensorFlow/Keras.
- Work with Large Language Models (LLMs), practice prompt engineering, and explore generative AI use cases
- Solve industry-level case studies such as churn prediction, sales forecasting, and recommendation systems.
- Develop an end-to-end capstone project with data pipeline, model, dashboard, and business insights
- Build a portfolio and resume that showcase your skills and prepare you for career opportunities in Data Science & AI.
Pre Requisites
- No prior experience in Data Science or AI is required — the course starts from the basics
- A basic understanding of high-school level math (algebra, probability, and statistics) will be helpful
- Familiarity with Python programming is a plus, but not mandatory — core concepts are covered early on
- A computer with internet access and the ability to install software such as Python, Jupyter Notebook, and required libraries.
- Most importantly: a growth mindset, curiosity, and commitment to completing the 100 days of structured learning.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)