Data Science & Machine Learning Course Description
Python is well known as a programming language used in numerous domains— from system administration to Web development to test automation. In recent years, Python has become a leading language in data science and machine learning. Whether you’re looking through logfiles, calculating statistics, finding similarities between documents, or identifying buying patterns among your customers, Python provides numerous tools to solve these problems. This data science machine learning course introduces the concepts of data science and machine learning, using commonly used open-source tools built in Python. This is not a course in the Python language; participants must already have a good understanding of Python’s basics, including the built-in types, functions, list/set/dict comprehensions, and objects. The course concentrates on the practical use of data science libraries to understand existing data and make predictions with new data.
Participants in this course will learn about NumPy, SciPy, Pandas, and Matplotlib as the building blocks, and how to use them to import, transform, and filter data — all essential day-to-day aspects of a data scientist’s job.
The course then turns to machine learning, creating computer-based statistical models that allow us to make predictions using the powerful, popular, open-source “scikit-learn” library. The course looks at both supervised and unsupervised learning, using real-world data from a variety of domains. Participants will learn how to work with numeric, categorical, and textual data.
An important part of developing machine-learning models is the testing of models, to ensure that they aren’t “overfit” to the training data. Participants will learn what tools scikit-learn provides, to test their data, and to check which of their models are the most appropriate.
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
This data science machine learning training is aimed at programmers who have day-to-day practical experience working with Python. Suggested attendees are:
- Software developers
- Software Engineers
- Data Analysts
- Python developers
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 Science Machine Learning In-house/Corporate Group Training
Available for a group size of more than 4-5. For commercials please send us an email with group size to firstname.lastname@example.org
|1. Overview of data science in Python|
|2. Jupyter notebook as an environment for data-science work (and collaboration)|
|Data structures Details||00:00:00|
|Sorting, searching and retrieving • Boolean indexing techniques Details||00:00:00|
|Importing and exporting data Details||00:00:00|
|Filtering data by row and column • Data manipulation Details||00:00:00|
|Time series Details||00:00:00|
|Chart types Details||00:00:00|
|Chart styles Details||00:00:00|
|Output to a file vs. the screen Details||00:00:00|
|Multiple plots Details||00:00:00|
|Standalone Matplotlib vs. integrated with Panda Details||00:00:00|
|6. Machine learning|
|7. What is machine learning?|
|9. Retrieving and using public data sets|
|10. Feature selection• What is it?|
|Why is it important? Details||00:00:00|
|What tools does scikit-learn provide to identify features? Details||00:00:00|
|11. Standardization of data|
|12. Machine-learning algorithms|
|How to choose Details||00:00:00|
|Why you shouldn’t be too confident Details||00:00:00|
|13. Supervised machine learning|
|Avoiding overfit models Details||00:00:00|
|Evaluating model success Details||00:00:00|
|Comparing models Details||00:00:00|
|Using the same classifier with different hyperparameters Details||00:00:00|
|14. Supervised classification problems|
|Simple classification Details||00:00:00|
|Textual classification Details||00:00:00|
|15. Supervised regression problems|
|16. Feature selection|
|17. Clustering with unsupervised learning|
|18. Future trends in machine learning|
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