Shreesha Jagadeesh

Shreesha Jagadeesh is a product manager at Amazon creating data science-driven HR products for talent retention, career growth and internal mobility. He has previously worked as a manager at Ernst & Young where he led a large global team of 25+ data scientists and engineers to apply data science-driven digital transformation of their tax business units. Aside from his day job, he is a startup advisor helping young companies build out their data science functions. He has a master’s in electrical and computer engineering from the University of Toronto. He has been teaching for more than a decade and has written data science articles on Medium, reviewed other Manning courses and developed a popular Udemy course for Agile data science.

projects by Shreesha Jagadeesh

Anomaly Detection with Python

5 weeks · 5-8 hours per week average · INTERMEDIATE

In this series of liveProjects, you’ll learn different methods for detecting anomalies and outliers with Python machine learning techniques. Anomaly detection is a vital tool for tasks like spotting medical problems, and even detecting seismic events like earthquakes. You’ll explore both supervised and unsupervised learning methods for anomaly detection to master this valuable ML task.

Using Undersampling

1 week · 5-8 hours per week · INTERMEDIATE

In this liveProject, you’ll utilize undersampling techniques to balance out a seismic activity dataset. To balance this dataset, you will utilize the ClusterCentroids, NearMiss and CondensedNearestNeighbor algorithms to downsample the majority class. Then, the performance is compared using random forest, logistic regression and Naive Bayes binary classification algorithms.

Using Oversampling

1 week · 5-8 hours per week · INTERMEDIATE

In this liveProject, you’ll go hands-on with supervised learning methods for anomaly detection. You’ll explore an imbalanced dataset of seismic activity. To balance this dataset you will utilize the SMOTE and ADASYN oversampling algorithms to both generate synthetic examples of the minority class and then compare performance using random forest, logistic regression and Naive Bayes binary classification algorithms.

Using PyOD and Ensemble Methods

1 week · 5-8 hours per week · INTERMEDIATE

In this liveProject, you’ll explore a dataset with more variables and use scikit-learn and the PyOD library to build an unsupervised machine learning model for detecting cardiac arrhythmias. You’ll develop an algorithm which will detect arrhythmias from device data like EEG, using the Locally Selective Combination in Parallel Outlier Ensembles (LSCP) algorithm. A LSCP model accepts input as various other algorithms, and can be used to set up detectors with different settings.

Using PyOD

1 week · 5-8 hours per week · INTERMEDIATE

In this liveProject, you’ll use scikit-learn and the PyOD library to build an unsupervised machine learning model for detecting hyperthyroidism. PyOD is a Python toolkit for detecting outlying objects in multivariate data. You’ll compare performance between four different anomaly detection methods on a specialized thyroid dataset: PCA, Clustering-Based Local Outlier Factor (CBLOF), Histogram-Based Outlier Score (HBOS), and KNN algorithms.

Using scikit-learn

1 week · 5-8 hours per week · INTERMEDIATE

In this liveProject, you’ll explore the basics of anomaly detection by analyzing a medical dataset using unsupervised learning. You’ll create a model that can determine whether patients referred to a clinic have abnormal thyroid function. To accomplish this, you’ll download and prepare your dataset, and then utilize scikit-learn to compare different anomaly detection algorithms to find the most effective. You are going to use Isolation Forest, the Local Outlier Factor (LOF), One-Class SVM and Robust Covariance.