Pinned Repositories
-NLP-with-Python-for-Machine-Learning-Essential-Training
With the increased amount of data publicly available and the increased focus on unstructured text data, understanding how to clean, process, and analyze that text data is tremendously valuable. If you have some experience with Python and an interest in natural language processing (NLP), this course can provide you with the knowledge you need to tackle complex problems using machine learning. Instructor Derek Jedamski provides a quick summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers. During this last step, Derek shows how to build two different types of machine learning models, as well as how to evaluate and test variations of those models.
Advanced-Decision-Trees
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Analysis_on_pets_Data
Technical Challenge: To answer the Data Analysis questions using sql and R
Analyzing-Big-Data-with-Hive
Businesses thrive by making informed decisions that target the needs of their customers and users. To make such strategic decisions, they rely on data. Hive is a tool of choice for many data scientists because it allows them to work with SQL, a familiar syntax, to derive insights from Hadoop, reflecting the information that businesses seek to plan effectively. This course shows how to use Hive to process data. Instructor Ben Sullins starts by showing you how to structure and optimize your data. Next, he explains how to get Hue, the Hadoop user interface, to leverage HiveQL when analyzing data. Using the newly configured option, he then demonstrates how to load data, create aggregate tables for fast query access, and run advanced analytics. He also takes you through managing tables and putting functions to use. This course is designed to help you find new ways to work with datasets so you can answer the tough data science questions that come your way.
Analyzing-Big-Data-with-Hive-702-702-likes-Save-5-539-Share-Share-video-with-title-Why-use-Hi
Businesses thrive by making informed decisions that target the needs of their customers and users. T
BuildItBigger-P4
BuildItBigger-Udacity
Capstone_Stage_1
Capstone_Stage_2
Movies
Udacity popular movies stage 2
hiteshsantwani's Repositories
hiteshsantwani/Movies
Udacity popular movies stage 2
hiteshsantwani/-NLP-with-Python-for-Machine-Learning-Essential-Training
With the increased amount of data publicly available and the increased focus on unstructured text data, understanding how to clean, process, and analyze that text data is tremendously valuable. If you have some experience with Python and an interest in natural language processing (NLP), this course can provide you with the knowledge you need to tackle complex problems using machine learning. Instructor Derek Jedamski provides a quick summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers. During this last step, Derek shows how to build two different types of machine learning models, as well as how to evaluate and test variations of those models.
hiteshsantwani/Advanced-Decision-Trees
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
hiteshsantwani/Analysis_on_pets_Data
Technical Challenge: To answer the Data Analysis questions using sql and R
hiteshsantwani/Analyzing-Big-Data-with-Hive
Businesses thrive by making informed decisions that target the needs of their customers and users. To make such strategic decisions, they rely on data. Hive is a tool of choice for many data scientists because it allows them to work with SQL, a familiar syntax, to derive insights from Hadoop, reflecting the information that businesses seek to plan effectively. This course shows how to use Hive to process data. Instructor Ben Sullins starts by showing you how to structure and optimize your data. Next, he explains how to get Hue, the Hadoop user interface, to leverage HiveQL when analyzing data. Using the newly configured option, he then demonstrates how to load data, create aggregate tables for fast query access, and run advanced analytics. He also takes you through managing tables and putting functions to use. This course is designed to help you find new ways to work with datasets so you can answer the tough data science questions that come your way.
hiteshsantwani/Certificates
Certificates for the courses that I completed online
hiteshsantwani/DemoPluginFrontApp
hiteshsantwani/DIC_LAB_2
hiteshsantwani/Extending-Hadoop
Extend your Hadoop data science knowledge by learning how to use other Apache data science platforms, libraries, and tools. This course goes beyond the basics of Hadoop MapReduce, into other key Apache libraries to bring flexibility to your Hadoop clusters. Coverage of core Spark, SparkSQL, SparkR, and SparkML is included. Learn how to scale and visualize your data with interactive Databricks clusters and notebooks and other implementations. This course is designed to help those working data science, development, or analytics get familiar with attendant technologies.
hiteshsantwani/Extending-Hadoop-for-Data-Science
Extend your Hadoop data science knowledge by learning how to use other Apache data science platforms, libraries, and tools. This course goes beyond the basics of Hadoop MapReduce, into other key Apache libraries to bring flexibility to your Hadoop clusters. Coverage of core Spark, SparkSQL, SparkR, and SparkML is included. Learn how to scale and visualize your data with interactive Databricks clusters and notebooks and other implementations. This course is designed to help those working data science, development, or analytics get familiar with attendant technologies.
hiteshsantwani/FairMarkit
Interview with FairMarkit
hiteshsantwani/FixturePlugin
hiteshsantwani/frontapp.github.io
Front's developer resources
hiteshsantwani/insight_Data_Engineering_fellowship_challenge
Insight Data Engineering coding challenge: pharmacy_counting
hiteshsantwani/LinkedinCertificates
hiteshsantwani/luma-eng-interview
hiteshsantwani/Luma_Health
hiteshsantwani/Machine_Learning_Project_3
hiteshsantwani/Maps
clone of Google maps(along with visualisation and performance assessments of multiple graph algorithms used for implementing path finding feature)
hiteshsantwani/ML_HW2_Collab_Notebooks
hiteshsantwani/MlisaDevelopmentDemo
Plugin
hiteshsantwani/ModalDemo
hiteshsantwani/Movie_Recommender_System
Prototyping a Recommender System Step by Step : KNN Item-Based Collaborative Filtering and Alternating Least Square (ALS) Matrix Factorization in Collaborative Filtering
hiteshsantwani/News-Article-collection-and-Translation
hiteshsantwani/NumPy_Data_Science_Essential_Training
NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. Here Charles Kelly shows how to work with NumPy and Python within Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.
hiteshsantwani/PluginDemo
hiteshsantwani/Predictive-Model-for-Student-Performance-Data-Set
Project on Data Mining Semester 1
hiteshsantwani/project-0
hiteshsantwani/RFP-Customer-Churn-Analysis
Part 3 of Data Intensive Computing: Customer Churn Analysis for Orange using Spark MLlib
hiteshsantwani/Udacity_Machine_Learning_NanoDegree