Master Python Data Science and Machine Learning skills for career growth and real-world applications
Sub Category
- Data Science
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Objectives
- What is Data Science?
- Why Python for Data Science?
- Setting Up Your Environment (Jupyter Notebook, Anaconda, VSCode)
- Exploring Datasets (Head, Tail, Describe, Info)
- Introduction to Matplotlib and Seaborn
- Customizing Plots (Labels, Colors, Themes)
- Creating Visual Stories With Data
- Summary Statistics & Data Distributions
- Descriptive vs Inferential Statistics
- Confidence Intervals and P-Values
- Mini-Quizzes & Practice Problems
- What is Machine Learning?
- Linear Regression with Scikit-Learn
- Logistic Regression
- k-Nearest Neighbors
- Decision Trees and Random Forest
- Model Accuracy, Confusion Matrix, ROC Curves
- Web Scraping With BeautifulSoup and Requests and more....
Pre Requisites
- No coding experience required
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)