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Machine Learning: Concepts and Applications
Master machine learning theory and practice. Learn Python, Pandas, Scikit-learn, and TensorFlow for ML modeling.
Course Cost
₹ 2,699
Intermediate
Skill Level
33 Hours
Self-paced lessons
Dive into the world of machine learning with this comprehensive course from the University of Chicago. Covering both theory and practice, you'll learn to use Python and industry-standard libraries like Pandas, Scikit-learn, and TensorFlow to ingest, explore, and prepare data for modeling. Master a wide range of techniques including linear regression, logistic regression, SVMs, decision trees, ensembles, clustering, PCA, HMMs, and deep learning. Gain hands-on experience with real-world datasets, focusing on public policy applications. Ideal for those with basic Python and linear algebra knowledge, this course provides a strong foundation for deeper, specialized study in machine learning.
3,233 Enrolled
English
What you'll learn
Understand the machine learning pipeline from data ingestion to model deployment
Master linear regression techniques including OLS, regularization, and polynomial expansion
Apply classification methods such as logistic regression, SVMs, and Naive Bayes
Implement and evaluate decision trees and ensemble methods like random forests
Explore unsupervised learning techniques including various clustering methods
Utilize dimensionality reduction techniques, focusing on Principal Component Analysis (PCA)
Develop skills in temporal modeling using Hidden Markov Models
Gain practical experience with deep learning, including feed-forward and convolutional neural networks
Skills you'll gain
This course includes:
3.56 Hours PreRecorded video
20 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 9 modules in this course
This comprehensive course covers the full spectrum of machine learning concepts and applications. Starting with the fundamentals of data ingestion and exploration using Pandas, it progresses through various modeling techniques. Topics include linear and logistic regression, regularization, SVMs, decision trees, ensemble methods, clustering, PCA, hidden Markov models, and deep learning. The course emphasizes both theoretical understanding and practical implementation using Python and libraries like Scikit-learn and TensorFlow. Real-world datasets, primarily from public policy, are used throughout, providing a strong foundation for applied machine learning.
Machine Learning and the Machine Learning Pipeline
Module 1 · 4 Hours to complete
Least Squares and Maximum Likelihood Estimation
Module 2 · 5 Hours to complete
Basis Functions and Regularization
Module 3 · 3 Hours to complete
Model Selection and Logistic Regression
Module 4 · 3 Hours to complete
More Classifiers: SVMs and Naive Bayes
Module 5 · 4 Hours to complete
Tree-Based Models, Ensemble Methods, and Evaluation
Module 6 · 5 Hours to complete
Clustering Methods
Module 7 · 3 Hours to complete
Dimensionality Reduction and Temporal Models
Module 8 · 3 Hours to complete
Deep Learning
Module 9 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
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Frequently asked Questions
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