Data Science
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Interpreting Data Using Descriptive Statistics with Python
This course covers measures of central tendency and dispersion needed to identify key insights in data. It also covers: correlation, covariance, skewness, kurtosis, and implementations in Python libraries such as Pandas, SciPy, and StatsModels.
Interpreting Data Using Statistical Models with Python
This course covers techniques from inferential statistics, including hypothesis testing, t-tests, and Pearson’s chi-squared test, along with ANOVA, which is used to analyze effects across categorical variables and the interaction between variables.
Intro to AI Transformers
Learn about what transformers are (the T of GPT) and how to work with them using Hugging Face libraries
Intro to Data Analysis
Understand the principles of data analysis, including techniques for data collection, cleaning, and interpretation.
Intro to Data Visualization with Python
Learn how to use Matplotlib to clarify your data with meaningful charts and other data visualizations.
Intro to Deep Learning with TensorFlow
Build basic deep learning models in TensorFlow.
Intro to Hyperparameter Tuning with Python
Improve machine learning models with hyperparameter tuning.
Intro to Inferential Statistics
Explore inferential statistics methods for making predictions and generalizations based on sample data.
Intro to Language Models in Python
Build the basic language models in Python.
Intro to PyTorch and Neural Networks
Learn how to use PyTorch to build, train, and test artificial neural networks in this course.
Intro to Regularization with Python
Improve machine learning performance with regularization.
Intro to SQL
Use SQL to create, access, and update tables of data in a relational database.