Data Exploration and regression
Exploratory data analysis (EDA) techniques, clustering, dimensionality reduction, regression, trend analysis, time-series analysis.
Machine learning methodology, bias vs. variance, model evaluation, tree-based algorithm (decision tree, random forest), linear classifier (perceptron, logistic regression, SVM, kernel SVM), ensemble learning (bagging, boosting)
Image processing and OCR
OpenCV, optical character recognition
Deep learning for image classification
Neural networks, stochastic gradient descent (SGD), building a CNN for image classification, deep learning + image classification projects.
Using Dash by Plotly, tables, charts, effective visualizations, visualization projects.
Key Learning Points:
After completing the course, the participants will be able to
Who should attend the course?
Key benefits of attending the course:
Knowing how to push a few buttons to apply the most common techniques can only get you so far. This action-packed short course goes much further. Through real-world case studies, we’re going to the deep end of data analytics and modeling. Depending on which modules you choose, you’ll learn how each standard technique works under the hood, enough to know what techniques to try in what scenarios, why to use them, and how to interpret the outcomes.