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For the courses- Topics in AI and Applications of NLP:
https://www.datacamp.com/courses/introduction-to-deep-learning-with-pytorch (Lecture 1)
https://www.datacamp.com/courses/intermediate-deep-learning-with-pytorch
https://www.datacamp.com/courses/deep-learning-for-text-with-pytorch
https://www.datacamp.com/courses/introduction-to-llms-in-python
https://app.datacamp.com/learn/code-alongs/fine-tuning-your-own-llama-2-model
https://www.datacamp.com/blog/llmops-essentials-guide-to-operationalizing-large-language-models
NLP Course
NLP Course
Topics in Data Science
Topics in Data Science
https://app.datacamp.com/learn/courses/introduction-to-statistics
https://app.datacamp.com/learn/courses/introduction-to-statistics-in-python (Lecture 1)
https://app.datacamp.com/learn/courses/foundations-of-probability-in-python
https://app.datacamp.com/learn/courses/experimental-design-in-python
https://app.datacamp.com/learn/courses/hypothesis-testing-in-python
https://app.datacamp.com/learn/projects/hypothesis_testing_with_mens_and_womens_soccer_matches
Introduction to Data Science in Python
Introduction to Data Science in Python
Course Description: Introduce python (environment, lambdas, csv files, numpy library, Data manipulation/cleaning using pandas- Series, DataFrame as data structures for data analysis, functions such as groupby, merge, and pivot tables).
Course Goal: take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Prerequisites: Basic python or programming background.
Who is this class for: Intended for learners who want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. The class is taught in a tutorial format using the pandas library, and only a minimal statistics background is expected, and the first course contains a refresh of these basic concepts. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Applied Plotting, Charting & Data Representation in Python
Course Description: Introduce information visualization basics, using the matplotlib library, design and information literacy perspective- what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. Demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.
Prerequisites: Take after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python. Basic computer science background with minimal statistics background already covered in first course https://www.coursera.org/learn/python-data-analysis?authMode=login.
Who is this class for: Learners who want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data and/or a tutorial of the matplotlib system.
Applied Machine Learning in Python
Course Description: Introduce applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. How machine learning is different than descriptive statistics?, Introduce the scikit learn toolkit . The issue of dimensionality of data, and the task of clustering data, as well as evaluating those clusters. Supervised approaches for creating predictive models, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.
Course Goal: To identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular data-set and need, engineer features to meet that need, and write python code to carry out an analysis.
Prerequisite: Basic python or programming background. Only minimal statistics background is expected. This course should be taken after Introduction to Data Science in Python https://www.coursera.org/learn/python-data-analysis?authMode=login and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python https://www.coursera.org/learn/python-plotting?authMode=login.
Who is this class for: This course is intended for learners who want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.
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