DataScience_Machine Learning – NLP- BigData – Spark- PySpark

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Data Science with Spark(Big Data – Hadoop) Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using Spark(Big Data – Hadoop). Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. urriculum Introduction to Data Science Learning Objectives – Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools. Topics: What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data Science Life cycle of Data Science Tools of Data Science Introduction to Big Data and Hadoop Introduction to RIntroduction to Spark Introduction to Machine Learning Statistical Inference Learning Objectives – In this module, you will learn about different statistical techniques andterminologies used in data analysis. Topics: What is Statistical Inference? Terminologies of Statistics Measures of Centers Measures of Spread Probability Normal Distribution Binary Distribution Data Extraction, Wrangling and Exploration Learning Objectives – Discuss the different sources available to extract data, arrange the data instructured form, analyze the data, and represent the data in a graphical format. Topics: Data Analysis Pipeline What is Data Extraction Types of Data Raw and Processed Data Data Wrangling Exploratory Data Analysis Visualization of Data Introduction to Machine Learning Learning Objectives – Get an introduction to Machine Learning as part of this module. You willdiscuss the various categories of Machine Learning and implement Supervised Learning Algorithms. Topics: What is Machine Learning? Machine Learning Use-Cases Machine Learning Process Flow Machine Learning Categories Supervised Learning algorithm: Linear Regression and Logistic Regression