| 1. Binomial Logistic Regression Part 1.mp4 | 89 MB | ||
| 1. Binomial Logistic Regression Part 1.srt | 7.7 KB | ||
| 1. Introduction to Pyspark Intermediate.mp4 | 18.3 MB | ||
| 1. Introduction to Pyspark Intermediate.srt | 2 KB | ||
| 1. K-Means Model.mp4 | 80.5 MB | ||
| 1. K-Means Model.srt | 7.8 KB | ||
| 1. Liner Regration.mp4 | 80.9 MB | ||
| 1. Liner Regration.srt | 8.2 KB | ||
| 2. Binomial Logistic Regression Part 2.mp4 | 65.6 MB | ||
| 2. Binomial Logistic Regression Part 2.srt | 5.8 KB | ||
| 2. Output Colomn.mp4 | 57.3 MB | ||
| 2. Output Colomn.srt | 4.6 KB | ||
| 3. Binomial Logistic Regression Part 3.mp4 | 95.3 MB | ||
| 3. Binomial Logistic Regression Part 3.srt | 5.6 KB | ||
| 3. Test Data.mp4 | 50.6 MB | ||
| 3. Test Data.srt | 5.3 KB | ||
| 4. Binomial Logistic Regression Part 4.mp4 | 87.8 MB | ||
| 4. Binomial Logistic Regression Part 4.srt | 7.5 KB | ||
| 4. Prediction.mp4 | 75.2 MB | ||
| 4. Prediction.srt | 6.2 KB | ||
| 5. Generalised Linear Regression.mp4 | 111.4 MB | ||
| 5. Generalised Linear Regression.srt | 13 KB | ||
| 5. Multinomial Logistic Regression.mp4 | 97 MB | ||
| 5. Multinomial Logistic Regression.srt | 8.2 KB | ||
| 6. Forest Regration.mp4 | 112.7 MB | ||
| 6. Forest Regration.srt | 12.3 KB | ||
| 6. Multinomial Logistic Regression Continue.mp4 | 68.6 MB | ||
| 6. Multinomial Logistic Regression Continue.srt | 6.1 KB | ||
| 7. Decision Tree.mp4 | 67.4 MB | ||
| 7. Decision Tree.srt | 7 KB | ||
| 8. Random Forest.mp4 | 70.1 MB | ||
| 8. Random Forest.srt | 7.6 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 34 total files | |||
PySpark for Data Science - Intermediate
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 16 lectures (2 hour, 9 mins) | Size: 1.19 GB
You get to learn about how to use spark python or PySpark to perform data analysis.
What you'll learn
This module on PySpark Tutorials aims to explain the intermediate concepts such as those like the use of Spark session in case of later versions and the use of Spark Config and Spark Context in case of earlier versions.
his will also help you in understanding how the Spark related environment is set up, concepts of Broadcasting and accumulator, other optimization techniques include those like parallelism, tungsten, and catalyst optimizer.
Requirements
The pre-requisite of these PySpark Tutorials is not much except for that the person should be well familiar and should have a great hands-on experience in any of the languages such as Java, Python or Scala or their equivalent. The other pre-requisites include the development background and the sound and fundamental knowledge of big data concepts and ecosystem as Spark API is based on top of big data Hadoop only. Others include the knowledge of real-time streaming and how big data works along with a sound knowledge of analytics and the quality of prediction related to the machine learning model.
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