TGx:Lynda | Applied Machine Learning: Foundations [FCO] TGx Exclusive

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Lynda | Applied Machine Learning: Foundations [FCO] TGx Exclusive

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Category:

Other > Training

Language:

English

Total Size:

380.95 MB

Info Hash:

fa6fa3238e15b9281ac82c9a493db3a9a43faffe

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Prom3th3uS :_verified_uploader:

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13-05-2019 17:01

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File	Size
1.Introduction/01.Leveraging machine learning.mp4	19.14 MB
1.Introduction/02.What you should know.mp4	4.49 MB
1.Introduction/03.What tools you need.mp4	1.62 MB
1.Introduction/04.Using the exercise files.mp4	3.06 MB
2.1. Machine Learning Basics/05.What is machine learning.mp4	5.98 MB
2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp4	8.31 MB
2.1. Machine Learning Basics/07.Why Python.mp4	12.14 MB
2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp4	6.87 MB
2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp4	10.55 MB
2.1. Machine Learning Basics/10.Common challenges.mp4	8.98 MB
3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp4	5.20 MB
3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp4	24.23 MB
3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp4	17.86 MB
3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp4	15.07 MB
3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp4	15.14 MB
3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp4	14.29 MB
3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp4	11.02 MB
4.3. Measuring Success/18.Why do we split up our data.mp4	9.49 MB
4.3. Measuring Success/19.Split data for train_validation_test set.mp4	12.99 MB
4.3. Measuring Success/20.What is cross-validation.mp4	9.04 MB
4.3. Measuring Success/21.Establish an evaluation framework.mp4	6.98 MB
5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp4	8.11 MB
5.4. Optimizing a Model/23.What is underfitting.mp4	4.04 MB
5.4. Optimizing a Model/24.What is overfitting.mp4	4.61 MB
5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp4	5.45 MB
5.4. Optimizing a Model/26.Hyperparameter tuning.mp4	9.63 MB
5.4. Optimizing a Model/27.Regularization.mp4	4.41 MB
6.5. End-to-End Pipeline/28.Overview of the process.mp4	2.57 MB
6.5. End-to-End Pipeline/29.Clean continuous features.mp4	13.79 MB
6.5. End-to-End Pipeline/30.Clean categorical features.mp4	10.62 MB
6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp4	9.71 MB
6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp4	14.91 MB
6.5. End-to-End Pipeline/33.Tune hyperparameters.mp4	18.15 MB
6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp4	18.55 MB
6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp4	24.12 MB
7.Conclusion/36.Next steps.mp4	6.19 MB
Discuss.FTUForum.com.html	31.89 kB
Exercise Files/Ex_Files_Applied_Machine_Learning.zip	3.41 MB
FreeCoursesOnline.Me.html	108.30 kB
FTUForum.com.html	100.44 kB
How you can help Team-FTU.txt	235.00 B
NulledPremium.com.url	163.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B

Description

Author: Derek Jedamski
Language: English
Released: 5/10/2019
Torrent Contains: 43 Files, 8 Folders
Course Source: https://www.lynda.com/Python-tutorials/Applied-Machine-Learning-Foundations/751335-2.html

Description

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

Topics include:

• What is machine learning (ML)?
• ML vs. deep learning vs. AI
• Handling common challenges in ML
• Plotting continuous features
• Continuous and categorical data cleaning
• Measuring success
• Overfitting and underfitting
• Tuning hyperparameters
• Evaluating a model.

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