TGx:[Coursera] Applied Machine Learning in Python

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[Coursera] Applied Machine Learning in Python - [FCO] TGx Exclusive

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English

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881.43 MB

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09404fc5d32e3d36248a64ca461bb02a8080fe47

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27-12-2018 15:03

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File	Size
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4	31.05 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt	16.07 kB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4	44.56 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt	18.82 kB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4	12.86 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt	6.11 kB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4	31.73 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt	14.83 kB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4	32.24 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt	12.05 kB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4	36.25 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt	26.19 kB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4	37.88 MB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt	22.13 kB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4	19.51 MB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt	15.81 kB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4	11.22 MB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt	6.74 kB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4	22.53 MB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt	17.09 kB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4	30.08 MB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt	21.26 kB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4	39.93 MB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt	27.19 kB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4	20.30 MB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt	17.13 kB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4	22.69 MB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt	15.54 kB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4	15.41 MB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt	8.30 kB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4	39.14 MB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt	25.60 kB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4	20.00 MB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt	13.00 kB
002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4	37.83 MB
002.Module 2 Supervised Machine Learning/018. Decision Trees.srt	28.36 kB
003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4	46.10 MB
003.Module 3 Evaluation/019. Model Evaluation & Selection.srt	30.08 kB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4	20.75 MB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt	15.85 kB
003.Module 3 Evaluation/021. Classifier Decision Functions.mp4	12.65 MB
003.Module 3 Evaluation/021. Classifier Decision Functions.srt	9.04 kB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4	9.23 MB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt	7.53 kB
003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4	19.77 MB
003.Module 3 Evaluation/023. Multi-Class Evaluation.srt	15.21 kB
003.Module 3 Evaluation/024. Regression Evaluation.mp4	17.01 MB
003.Module 3 Evaluation/024. Regression Evaluation.srt	7.83 kB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4	34.50 MB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt	18.12 kB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4	21.38 MB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt	11.20 kB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4	26.45 MB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt	17.07 kB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4	11.81 MB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt	8.44 kB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4	41.51 MB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt	27.90 kB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4	17.46 MB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt	10.34 kB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4	32.89 MB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt	16.69 kB
005.Optional Unsupervised Machine Learning/032. Introduction.mp4	10.67 MB
005.Optional Unsupervised Machine Learning/032. Introduction.srt	6.46 kB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4	16.09 MB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt	13.47 kB
005.Optional Unsupervised Machine Learning/034. Clustering.mp4	27.18 MB
005.Optional Unsupervised Machine Learning/034. Clustering.srt	19.90 kB
006.Conclusion/035. Conclusion.mp4	9.89 MB
006.Conclusion/035. Conclusion.srt	3.90 kB
Discuss.FreeTutorials.Us.html	165.68 kB
FreeCoursesOnline.Me.html	108.30 kB
FreeTutorials.Eu.html	102.23 kB
How you can help Team-FTU.txt	259.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

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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