TGx:[UdemyCourseDownloader] Machine Learning and AI Foundations Predictive Modeling Strategy at Scale

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[UdemyCourseDownloader] Machine Learning and AI Foundations Predictive Modeling Strategy at Scale

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English

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163.99 MB

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fa243f5243c3c87ace6875365dd884a44d3f5d6c

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kirat1h

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15-12-2018 13:11

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File	Size
01 - Scaling machine learning initiatives.en.srt	2.88 kB
01 - Scaling machine learning initiatives.mp4	6.18 MB
02 - Defining terms.en.srt	3.13 kB
02 - Defining terms.mp4	3.46 MB
03 - Data and supervised machine learning.en.srt	3.65 kB
03 - Data and supervised machine learning.mp4	3.72 MB
04 - The nine big data bottlenecks.en.srt	6.44 kB
04 - The nine big data bottlenecks.mp4	5.86 MB
05 - The stages of predictive analytics data.en.srt	5.06 kB
05 - The stages of predictive analytics data.mp4	6.60 MB
06 - Why you might have too little data.en.srt	2.89 kB
06 - Why you might have too little data.mp4	2.78 MB
07 - How much data do I need.en.srt	3.72 kB
07 - How much data do I need.mp4	3.76 MB
08 - Balancing.en.srt	3.31 kB
08 - Balancing.mp4	3.22 MB
09 - Who truly has big data.en.srt	5.94 kB
09 - Who truly has big data.mp4	6.38 MB
10 - Assessing data.en.srt	5.93 kB
10 - Assessing data.mp4	8.89 MB
11 - Selecting Data that should be left out.en.srt	3.65 kB
11 - Selecting Data that should be left out.mp4	3.49 MB
12 - Seasonality and time alignment.en.srt	7.01 kB
12 - Seasonality and time alignment.mp4	6.93 MB
13 - Data and the data scientist.en.srt	4.69 kB
13 - Data and the data scientist.mp4	7.92 MB
14 - Aggregate and restructure.en.srt	3.43 kB
14 - Aggregate and restructure.mp4	3.42 MB
15 - Dummy coding.en.srt	3.70 kB
15 - Dummy coding.mp4	3.10 MB
16 - Feature engineering.en.srt	4.97 kB
16 - Feature engineering.mp4	4.21 MB
17 - Understanding the modeling process.en.srt	5.25 kB
17 - Understanding the modeling process.mp4	4.68 MB
18 - Slow algorithms Brute force.en.srt	3.16 kB
18 - Slow algorithms Brute force.mp4	2.91 MB
19 - Slow algorithms More calculations.en.srt	2.81 kB
19 - Slow algorithms More calculations.mp4	3.03 MB
20 - Slow algorithms More models.en.srt	3.97 kB
20 - Slow algorithms More models.mp4	3.65 MB
21 - How to sample properly.en.srt	4.34 kB
21 - How to sample properly.mp4	4.19 MB
22 - Modeling with missing data.en.srt	5.99 kB
22 - Modeling with missing data.mp4	8.33 MB
23 - Scoring traditional ML models.en.srt	5.04 kB
23 - Scoring traditional ML models.mp4	4.84 MB
24 - Scoring a black box model.en.srt	5.12 kB
24 - Scoring a black box model.mp4	7.99 MB
25 - Scoring an ensemble.en.srt	3.10 kB
25 - Scoring an ensemble.mp4	4.44 MB
26 - Batch vs. real-time scoring.en.srt	7.86 kB
26 - Batch vs. real-time scoring.mp4	9.51 MB
27 - Data prep and scoring.en.srt	5.38 kB
27 - Data prep and scoring.mp4	4.74 MB
28 - Combining batch and real-time scoring.en.srt	2.94 kB
28 - Combining batch and real-time scoring.mp4	3.84 MB
29 - What is model monitoring.en.srt	7.37 kB
29 - What is model monitoring.mp4	11.12 MB
30 - How often should you rebuild.en.srt	6.99 kB
30 - How often should you rebuild.mp4	8.95 MB
31 - Next steps.en.srt	1.61 kB
31 - Next steps.mp4	1.71 MB
Udemy Course downloader.txt	94.00 B
udemycoursedownloader.com.url	132.00 B

Description

Building world-class predictive analytics solutions requires recognizing that the challenges of scale and sample size fluctuate greatly at different stages of a project. How do you know how much data to use? What is too little, what is too much? How does your infrastructure need to scale with the volume and demands of the project? This course walks step by step through the strategic and tactical aspects of determining how much data is needed to build an effective predictive modeling solution based on machine learning and what volumes of data are so large that they will create challenges. Instructor Keith McCormick reviews each stage—data selection, data preparation, modeling, scoring, and deployment—with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools.