PySpark Essentials for Data Scientists (Big Data + Python) Free Download


Free Download PySpark Essentials for Data Scientists (Big Data + Python), with this course you will have the option to Learn how to wrangle Big Data for Machine Learning using Python in PySpark taught by an industry expert!. It contains 109 recordings. Remember that numerous understudies are tried out this course PySpark Essentials for Data Scientists (Big Data + Python), so don't hold on to download it presently, it's totally free. You can start learning PySpark Essentials for Data Scientists (Big Data + Python) by clicking the download link below.

Before you can start learning from this course explained in English (US) you need to have Familiarity with Python is helpful but not required, Some background in data science is helpful but not required, A hunger to LEARN.

PySpark Essentials for Data Scientists (Big Data + Python) is targeting for people that have interset in Data Scientists interested in learning PySpark, PySpark developers looking to strengthen their coding skills, Python developers who need to work with big data, Data Scientists who want to learn to work with big data.

Finally you will learn how to Use Python with Big Data on a distributed framework (Apache Spark), Work with REAL datasets on realistic consulting projects, How to streaming LIVE data from Twitter using Spark Structured Streaming, Learn how to create a "Pandora Like" app that classifies songs into genres using machine learning, Flag suspicious job postings using Natural Language Processing, Use machine learning to predict optimal cement strength and the factors that affect it, Classify Christmas cooking recipes using Topic Modeling (LDA), Customer Segmentation using Gaussian Mixture Modeling (Clustering), Use cluster analysis to develop a strategy designed to increase college graduation rates for under-priveleged populations, How to use the k-means clustering algorithm to define a marketing outreach strategy, Integrate a UI to monitor your model training and development process with MLflow, Theory and application of cutting edge data science algorithms, Manipulate, Join and Aggregate Dataframes in Spark with Python, Learn how to apply Spark's machine learning techniques on distributed Dataframes, Cross Validation & Hyperparameter Tuning, Frequent Pattern Mining Techniques, Classification & Regression Techniques, Data Wrangling for Natural Language Processing, How to write SQL Queries in Spark.

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