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[FreeCoursesOnline.Me] [Coursera] Applied Machine Learning in Python - [FCO]

[FreeCoursesOnline.Me] [Coursera] Applied Machine Learning in Python - [FCO]

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881 MB
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Description
Instructor : Kevyn Collins-Thompson
Offered By : University of Michigan
Language : English
Subtitle : Included
Torrent Contains : 76 Files, 6 Folders
Course Source : https://www.coursera.org/learn/python-machine-learning

About this Course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

About University of Michigan

The mission of the University of Michigan is to serve the people of Michigan and the world through
preeminence in creating, communicating, preserving and applying knowledge, art, and academic
values, and in developing leaders and citizens who will challenge the present and enrich the future.

WHAT YOU WILL LEARN

• Build features that meet analysis needs
• Create and evaluate data clusters
• Describe how machine learning is different than descriptive statistics
• Explain different approaches for creating predictive models.

SKILLS YOU WILL GAIN

• Python Programming
• Machine Learning (ML) Algorithms
• Machine Learning
• Scikit-Learn

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File list
  • [FreeCoursesOnline.Me] [Coursera] Applied Machine Learning in Python - [FCO]
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 31.1 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt 16.1 KB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 44.6 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 18.8 KB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 12.9 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt 6.1 KB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 31.7 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt 14.8 KB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 32.2 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt 12.1 KB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 36.2 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 26.2 KB
  • 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 37.9 MB
  • 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 22.1 KB
  • 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 19.5 MB
  • 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt 15.8 KB
  • 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11.2 MB
  • 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt 6.7 KB
  • 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 22.5 MB
  • 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 17.1 KB
  • 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 30.1 MB
  • 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 21.3 KB
  • 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 39.9 MB
  • 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 27.2 KB
  • 002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20.3 MB
  • 002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 17.1 KB
  • 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 22.7 MB
  • 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt 15.5 KB
  • 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 15.4 MB
  • 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt 8.3 KB
  • 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 39.1 MB
  • 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 25.6 KB
  • 002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 20 MB
  • 002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt 13 KB
  • 002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 37.8 MB
  • 002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 28.4 KB
  • 003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 46.1 MB
  • 003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 30.1 KB
  • 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 20.8 MB
  • 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt 15.8 KB
  • 003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 12.7 MB
  • 003.Module 3 Evaluation/021. Classifier Decision Functions.srt 9 KB
  • 003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9.2 MB
  • 003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt 7.5 KB
  • 003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 19.8 MB
  • 003.Module 3 Evaluation/023. Multi-Class Evaluation.srt 15.2 KB
  • 003.Module 3 Evaluation/024. Regression Evaluation.mp4 17 MB
  • 003.Module 3 Evaluation/024. Regression Evaluation.srt 7.8 KB
  • 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 34.5 MB
  • 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 18.1 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 21.4 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt 11.2 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 26.4 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 17.1 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 11.8 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt 8.4 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 41.5 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 27.9 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 17.5 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt 10.3 KB
  • 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 32.9 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 16.7 KB
  • 005.Optional Unsupervised Machine Learning/032. Introduction.mp4 10.7 MB
  • 005.Optional Unsupervised Machine Learning/032. Introduction.srt 6.5 KB
  • 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16.1 MB
  • 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt 13.5 KB
  • 005.Optional Unsupervised Machine Learning/034. Clustering.mp4 27.2 MB
  • 005.Optional Unsupervised Machine Learning/034. Clustering.srt 19.9 KB
  • 006.Conclusion/035. Conclusion.mp4 9.9 MB
  • 006.Conclusion/035. Conclusion.srt 3.9 KB
  • Discuss.FreeTutorials.Us.html 165.7 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FreeTutorials.Eu.html 102.2 KB
  • How you can help Team-FTU.txt 259 B

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