PYSpark for Big data Course
PySpark is a general-purpose, in-memory, distributed processing
engine that allows you to process data efficiently in a distributed
fashion. Applications running on PySpark are 100x faster than
traditional systems. You will get great benefits using PySpark for
data ingestion pipelines.
- Interactive training for better learning
- Pre-evaluation learn only what you need to learn
- Experienced and certified trainer
- Convenient weekday and weekend Batches available Demo.
- Timings for classes are arranged upon Flexibility of both the trainee and trainer.
- Access to the recorded videos which you have attended.
Introduction:
Starting Course
Validate and Explore:
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12
Dataframe Essentials Concept Review Quiz -
13
A little something to keep you going....
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14
Read, Write and Validate Dataframes Code Along Activity
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15
Read, Write and Validate Data HW
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16
Read, Write and Validate Data HW Solutions Code Review
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17
A little something to keep you going....
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18
Search and Filter Dataframes Code Along Activity
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19
Search and Filter Dataframes HW
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20
Search and Filter Dataframes HW Solution Code Review
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21
A little something to keep you going....
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22
SQL Options in Spark/PySpark Code Along Activity
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23
SQL Options in Spark/PySpark HW
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24
SQL Options in Spark/PySpark HW Solutions
After Intro
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25
Realistic Graphic on UE4
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26
Volta GPU for optimization.
The Tensor Core GPU Architecture designed to Bring AI to Every Industry. Equipped with 640 Tensor Cores, Volta delivers over 100 teraflops per second (TFLOPS) of deep learning performance, over a 5X increase compared to prior generation NVIDIA Pascal architecture.
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27
Deep Learning
Aggregate:
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28
Manipulating Dataframes Code Along Activity
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29
Manipulating Dataframes HW
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30
Manipulating Dataframes HW Solution
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31
A little something to keep you going....
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32
Aggregating Data in Dataframes Code Along Activity
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33
Aggregating Data in Dataframes HW
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34
Aggregating Data in Dataframes HW Solution
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35
A little something to keep you going....
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36
Joining and Appending Dataframes Code Along Activity
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37
Joining and Appending Dataframes HW
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38
Joining and Appending Dataframes HW Solution Code Review
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39
A little something to keep you going....
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40
Handling Missing Data in Dataframes Code Along Activity
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41
Handling Missing Data in Dataframes HW
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42
Handling Missing Data in Dataframes HW Solution
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43
Dataframe Essentials Coding Master Review
Introduction to MLlib:
Classification in MLib:
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48
Introduction to Classification in MLlib Concept Review
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49
Classification in MLlib Quiz
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50
Classification in MLlib Quiz
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51
Classification in MLlib Code Along Part 1: Data Formatting and Transformations
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52
Classification in MLlib Code Review Part 2.0: Train and Evaluate Models [Intro]
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53
Classification in MLlib Code Review Part 2.0: Train and Evaluate Models [Intro]
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54
Classification in MLlib Code Review Part 2.2: Train & Test Models [1 vs Rest]
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55
A little something to keep you going....
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56
Classification in MLlib Code Review Part 2.3: Train & Test Models[Multilayer PC]
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57
Classification in MLlib Code Review Part 2.4: Train & Test Models [Naive Bayes]
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58
Classification in MLlib Code Review Part 2.5: Train & Test Models [Linear SVM]
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59
Classification in MLlib Code Review Part 2.6: Train & Test Models[Decision Tree]
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60
Classification in MLlib Code Review Part 2.7: Train & Test Models[Random Forest]
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61
Classification in MLlib Code Review Part 2.8: Train & Test Models [GBT]
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62
Classification Project
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63
Remember to be creative with this project!
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64
Classification Project Solution
Kafka UI:
Natural Language Processing in Mlib:
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66
Introduction to Natural Language Processing Quiz
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67
Natural Language Processing Concept Review [Part 1: Feature Transformers]
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68
Natural Language Processing Concept Review [Part 2: Feature Extractors]
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69
Natural Language Processing Feature Extractors Quiz
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70
Natural Language Processing Code Along Activity Part 1: Data Prep
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71
Natural Language Processing Code Along Activity Part 2: Vectorize, Train & Eval
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72
Natural Language Processing Project
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73
Natural Language Processing Project Solution
Regression in Mlib:
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74
Regression in MLlib Concept Review
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75
Regression in PySpark's MLlib
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76
Regression in MLlib Code Review Introduction
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77
Regression in MLlib Code Review Part 1: Data Prep
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78
Regression in MLlib Code Review Part 2.0: Linear Regression
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79
A little something to keep you going....
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80
Regression in MLlib Code Review Part 2.1: Decision Tree Regression
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81
Regression in MLlib Code Review Part 2.2: Random Forest Regression
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82
Regression in MLlib Code Review Part 2.3: Gradient Boosted Tree Regression
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83
A little something to keep you going....
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84
BONUS: Add loop functions to your regression training and evaluation script
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85
Regression Project
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86
And finally... have FUN with this project and LOVE what you do!
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87
Regression Project Solution Code Along Activity
Clustering in Pyspark:
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88
Intro to Clustering in MLlib Concept Review
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89
Clustering Concept Review Quiz
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90
K-Means & Bisecting K-Means in MLlib Code Along Activity
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91
Latent Dirichlet Allocation in MLlib Code Along Activity
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92
A little something to keep you going....
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93
Gaussian Mixture Modeling in MLlib Code Along Activity
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94
Clustering Project Introduction
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95
Clustering Project Solution Code Review
Frequent Pattern mining in MLib:
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96
Frequent Pattern Mining in MLlib Concept Review
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97
Frequent Pattern Mining Concept Quiz
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98
Frequent Pattern Mining Code Along Activity [Part 1: FPGrowth]
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99
Frequent Pattern Mining Code Along Activity [Part 2: PrefixSpan]
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100
A little something to keep you going....
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101
Frequent Pattern Mining Project Introduction
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102
Frequent Pattern Mining Project Solution Code Review
Apache Spark is an open-source real-time in-memory cluster processing framework. It is used in streaming analytics systems such as bank fraud detection system, recommendation system, etc. Whereas Python is a general-purpose, high-level programming language. It has a wide-range of libraries which supports diverse types of applications. PySpark is a combination of Python and Spark. It provides Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course
"You will never miss a lecture at MITS You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch."
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch."
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.
All the instructors at MITS are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by MITS for providing an awesome learning experience to the participants.
RDD stands for Resilient Distributed Dataset which is the building block of Apache Spark. RDD is fundamental data structure of Apache Spark which is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster.
PySpark is not a language. PySpark is Python API for Apache Spark using which Python developers can leverage the power of Apache Spark and create in-memory processing applications. PySpark is developed to cater the huge amount of Python community.
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