UChicago - Machine Learning: Concepts and Applications
- Offered byCoursera
Machine Learning: Concepts and Applications at Coursera Overview
Duration | 38 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning: Concepts and Applications at Coursera Highlights
- Earn a Certificate upon completion
Machine Learning: Concepts and Applications at Coursera Course details
- This course gives you a comprehensive introduction to both the theory and practice of machine learning
- You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques
- Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning
Machine Learning: Concepts and Applications at Coursera Curriculum
Machine Learning and the Machine Learning Pipeline
Course Introduction
The Data Science Pipeline
Data Ingestion and Exploration
Lab Walkthrough: Data Exploration with Pandas
Supervised Learning, Linear Models, and Least Squares
Lab Walkthrough: Linear Regression
Working with Data
Introduction to Linear Regression
Least Squares and Maximum Likelihood Estimation
Linear Regression and Least Squares
Lab Walkthrough: Linear Regression on the Prostate Cancer Dataset
Maximum Likelihood Estimation
Lab Walkthrough: Linear Regression and Maximum Likelihood Estimation
Linear Regression
Maximum Likelihood Estimation
Basis Functions and Regularization
Basis Functions
Lab Walkthrough: Features and Basis Functions
Regularization and the Bias-Variance Tradeoff
Lab Walkthrough: Linear Regression: Regularization
Polynomial Feature Expansion
Regularization
Model Selection and Logistic Regression
Model Selection and Cross Validation
Lab Walkthrough: Model Selection and Pipelines
Logistic Regression
Lab Walkthrough: Logistic Regression
Model Tuning and Selection
Logistic Regression
More Classifiers: SVMs and Naive Bayes
Support Vector Machines
Lab Walkthrough: Support Vector Machines
Naive Bayes Classification
Naive Bayes Classification Example
Classification with SVMs
Naive Bayes Classifiers
Graded Quiz: Model Evaluation
Tree-Based Models, Ensemble Methods, and Evaluation
Tree-Based Models
Ensembles, Bagging, and Boosting
Lab Walkthrough: Trees and Forests
Evaluation Metrics
Lab Walkthrough: Evaluation
Trees and Ensembles
Evaluating Models
Trees and Forests Quiz
Clustering Methods
Unsupervised Learning (K-Means, Hierarchical)
Lab Walkthrough: Clustering
Clustering (KDE, Meanshift, DBSCAN)
Lab Walkthrough: Density and Distribution-Based Clustering
K-Means and Hierarchical Clustering
Clustering II
Dimensionality Reduction and Temporal Models
Principal Component Analysis (PCA)
Lab Walkthrough: Principal Component Analysis
Temporal Models and Hidden Markov Models
Lab Walkthrough: Hidden Markov Models
Principal Component Analysis
HMMs
Deep Learning
Feed-Forward Neural Networks
Lab Walkthrough: Feed Forward Neural Networks
Convolutional Neural Networks
Lab Walkthrough: Convolutional Neural Nets
Neural Networks
Convolutional Neural Nets
Machine Learning: Concepts and Applications at Coursera Admission Process
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