data science course in kochi Best Data Science Course Training in Kochi, Kerala| Luminar Technolab
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Big Data Analytics - Machine Learning & Data Science Training Course Details


Big Data Hadoop Implementation

Java, Unix commands, SQL, Apache Maven, IntelliJ IDE, Git, Bashscript, AWS EMR, Big Data, Big Data Analytics, Cloudera Hadoop, Hadoop Architecture,Hadoop Installation Mode and HDFS, Hadoop Clustering, Map Reduce (Version 1 & 2), YARN Application, Pig, Sqoop, Hive, HBase, Project


Apache Spark

Python, Introduction to PySpark, Spark Basics, Spark Installation, Spark RDDs & Pair RDDs, Spark Application Deployment, Parallel Processing, Spark SQL, Spark - MLlib(Machine Learning), KNN, Kmeans, GMM, Naive Bayes, Spark – Data Frames, Spark Streaming , Spark Advanced Concepts, Tableau Visualization, Spark Project.


Python - Data Science

Python basics and essentials, Types (strings, lists, dictionaries, tuple etc), Control Flow (if-then statements, looping), Organizing code (functions, modules, packages), Reading and writing files, Numpys, Understanding the N-dimensional data structure Creating arrays Indexing arrays by slicing or more generally with indices or masks, Basic operations and manipulations on N-dimensional arrays, Plotting with Matplotlib, Accessing Data From Multiple Sources Reading and writing data from local files (.txt,.csv,.xls, .json, etc), Reading data from database, Scraping tables from web pages (.html), Pandas and data frames, Working with Pandas data structures: Series and DataFrames Accessing your data: indexing, slicing, fancy indexing, Boolean indexing, Data wrangling, including dealing with dates and times and missing data, Adding, dropping, selecting, creating, and combining rows and columns, Data summarization and aggregation methods, Pandas powerful group by method, Reshaping, pivoting, and transforming your data, Seaborn - statistical data visualization, Project.


Python - Machine Learning and Deep Learning

Introduction to Python, Jupyter Notebook, Basic Packages and Data Preprocessing, Pandas and Numpy, Missing Data, Categorical Data, Splitting of Data, Feature Scaling, Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Evaluating Regression Model Parameters, Classification, Logistic Regression, KNN, SVM, Evaluating Classification Model Parameters, Clustering, KMeans Clustering, Hierarchical Clustering, Dimensionality Reduction, Principal Component Analysis, Keras, Tensorflow, CNN, NLP, TFIDF, Course Project.

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