Data Science & Machine Learning With Python | Learn it with Python
Data Science & Machine Learning Course Description
Data Science & Machine Learning Course Learning Outcomes
- Hands-on experience setting up an integrated analysis environment for doing data science with Python.
- An understanding of how to use the Python standard library to write programs, access the various data science tools, and document and automate analytic processes.
- Orientation to some of the most powerful and popular Python libraries for data science including Pandas (data preparation, analysis, and modeling; time series analysis), scipy.stats (statistics), scikit-learn (machine learning), and Matplotlib (data visualization).
- Working knowledge of the Python tools ideally suited for data science tasks, including:
- 1. Accessing data (e.g., text files, databases)
- 2. Cleansing and normalizing data
- 3. Exploring data (e.g., simple statistics, correlation matrices, visualization)
- 4. Modeling data (e.g., statistics, machine learning)
Data Science & Machine Learning Training - Suggested Audience
- Software developers
- Software Engineers
- Data Analysts
- Python developers
Data Science & Machine Learning Training Duration
- Open-House F2F (Public): 3/4 days
- In-House F2F (Private): 3/4 days, for commercials please send us an email with group size to firstname.lastname@example.org
Data Science & Machine Learning Training - Prerequisites
- Participants must already have a good understanding of Python’s basics, including the built-in types, functions, list/set/dict comprehensions, and objects.
- A basic understanding of statistics will be useful, but not mandatory.
- Data structures
- Sorting, searching and retrieving • Boolean indexing techniques
- Importing and exporting data
- Filtering data by row and column • Data manipulation
- Time series
- Chart types
- Chart styles
- Output to a file vs. the screen
- Multiple plots
- Standalone Matplotlib vs. integrated with Panda
- Why is it important?
- What tools does scikit-learn provide to identify features?
- How to choose
- Why you shouldn’t be too confident
- Avoiding overfit models
- Evaluating model success
- Comparing models
- Using the same classifier with different hyperparameters
- Simple classification
- Textual classification
This is great
I really love the course editor in LearnPress. It is never easier when creating courses, lessons, quizzes with this one. It's the most useful LMS WordPress plugin I have ever used. Thank a lot! Testing quiz is funny, I like the sorting choice question type most.