DataScience_Machine Learning NLP- Python-R-BigData-PySpark_1

DataScience_Machine Learning – NLP- Python-R-BigData-PySpark, available at $19.99, has an average rating of 4.7, with 179 lectures, based on 5 reviews, and has 46 subscribers.

You will learn about DataScience Machine Learning NLP Python-R BigData PySpark This course is ideal for individuals who are Beginners or Freshers or Experienced people looking for Career changes It is particularly useful for Beginners or Freshers or Experienced people looking for Career changes.

Enroll now: DataScience_Machine Learning – NLP- Python-R-BigData-PySpark

Summary

Title: DataScience_Machine Learning – NLP- Python-R-BigData-PySpark

Price: $19.99

Average Rating: 4.7

Number of Lectures: 179

Number of Published Lectures: 179

Number of Curriculum Items: 179

Number of Published Curriculum Objects: 179

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

DataScience

Machine Learning

NLP

Python-R

BigData

PySpark

Who Should Attend

Beginners

Freshers

Experienced people looking for Career changes

Target Audiences

Beginners

Freshers

Experienced people looking for Career changes

Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the #1 Best Job in America in 2018 for the third year in a row, and it still holds its #1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place.

Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data.  Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.

Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is LinkedIn’s most wanted Data Science skill in the United States.

Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-Means

Clustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Training

encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction

to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on

Media, Healthcare, Social Media, Aviation and HR.

Curriculum

Introduction to Data Science

Learning Objectives – Get an introduction to Data Science in this module and see how Data Science

helps 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 R

Introduction to Spark Introduction to Machine Learning

Statistical Inference

Learning Objectives – In this module, you will learn about different statistical techniques and

terminologies 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 in

structured 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 will

discuss 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

? Define Data Science and its various stages

? Implement Data Science development methodology in business scenarios

? Identify areas of applications of Data Science.

? Understand the fundamental concepts of Python

? Use various Data Structures of Python.

? Perform operations on arrays using NumPy library

? Perform data manipulation using the Pandas library

? Visualize data and obtain insights from data using the Matplotlib and Seaborn library

? Apply Scrapy and Beautiful Soup to scrap data from websites

? Perform end to end Case study on data extraction, manipulation, visualization and analysis using Python 

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: DS-ML-Technologies & Course-OverView

Chapter 2: Code Base

Lecture 1: Code Base

Chapter 3: Stats

Lecture 1: 22-Stats-Begining-01

Lecture 2: 22-Stats-Begining-02

Lecture 3: 22-Stats-Begining-02-new

Lecture 4: 23-Stats-FrequencyDistributionsandHistogram-New-01

Lecture 5: 23-Stats-FrequencyDistributionsandMeasuresofcentralTendency

Lecture 6: 23-Stats-FrequencyDistributionsandMeasuresofcentralTendency-Add at the Historgam

Lecture 7: 23-Stats-MeasuresofcentralTendency-New

Lecture 8: 24-Stats-Dispersion

Lecture 9: 24-Stats-Dispersion-New

Lecture 10: 25-Stats-TypeOfVariabls

Lecture 11: 26-Stats-SamplingTechniques-01

Lecture 12: 26-Stats-SamplingTechniques-02

Lecture 13: 26-Stats-SamplingTechniques-Editat-WithReplacementandwithoutReplacement

Lecture 14: 27-Stats-DataTypesAndScales

Lecture 15: 28-Stats-Hypothesis-testing

Lecture 16: 29-Stats-basics-of-Hypothesistesting-02

Chapter 4: Python day 1

Lecture 1: 01-Download Python – Python.org

Lecture 2: 02-Anaconda Python-R Distribution – Free Download

Lecture 3: 03-Anaconda Navigator

Lecture 4: 04-Spyder (Python 3.7)

Lecture 5: 05-Spyder (Python 3.7)

Lecture 6: 06-ADvantagesOfPython-Spyder (Python 3.7)

Lecture 7: 07-Python-DataTyoe-Dynamic-TypecastingSpyder (Python 3.7)

Lecture 8: 11-Python-List-inPython-Spyder (Python 3.7)

Lecture 9: 12-Python-ListInsideTuple-TupleInsideListSpyder (Python 3.7)

Lecture 10: 13-Python-Dictonary-Python

Lecture 11: 14-Python-Set-Pyhon

Lecture 12: 15-Python-Operators

Lecture 13: 16-Python-Conditions&Loops-LoopControl

Lecture 14: 17-Python-keybpoard-Input

Lecture 15: 18-PythonFileOperations-1

Lecture 16: 20-Python-IntroDuctionto-Class-Object-Method-OOPS

Lecture 17: 20-Python-OOPS-1

Lecture 18: 21-Python-ModulesinPython

Chapter 5: Python day 2

Lecture 1: 30-Python-Pandas

Lecture 2: 31-Python-Pandas-02

Lecture 3: 32-Python-Basic-Stats-01

Lecture 4: 33-Python-Basic-Stats-02

Lecture 5: 34-Python-Basic-Stats-03

Chapter 6: R R_Day1

Lecture 1: 01-R-R Studio Installation & Upgrading Version for Dependencies

Lecture 2: 01-R-R Studio Installation & Upgrading Version for Dependencies-2-CRAN R Project

Lecture 3: 01-R-R Studio Installation & Upgrading Version for Dependencies-CRAN R Project

Lecture 4: 02-R-Working with R Multiple Versions

Lecture 5: 03-R-Start with R Programming3

Lecture 6: 04-R-Data Types-Integer-Numeric-Logical-Character-Factor-Complex-Date

Lecture 7: 05-R-DataStructures-Vectors-matrix-array-lists-data frames

Lecture 8: 06-R-Functions-Argument Types-Apply

Lecture 9: 07-R-ApplyFamily

Lecture 10: 07-R-ApplyFamily-01

Lecture 11: 08-R-WorkWith Packeges – library in R

Lecture 12: 09-R-Library-Dependency with R version

Lecture 13: 10-Split-Data into Train and testig-R

Chapter 7: BigData

Lecture 1: 01-BigData-Introduction

Lecture 2: 02-BigData-Hadoop-Introduction

Lecture 3: 03-BigData-Hadoop-Architecture

Lecture 4: 04-BigData-Hadoop-OverView

Lecture 5: 05-BigData-MapReduce-Architecture

Lecture 6: 06-BigData-YARN

Lecture 7: 07-BigData-hadoop-ClusterModes

Lecture 8: 08-BigData-Limitations-Of-MapReduce

Lecture 9: 09-BigData-Spark-Introduction&OverView

Lecture 10: 09-BigData-Spark-Introduction&OverView-02-MRvsSpark

Lecture 11: 10-BigData-Spark-FrameWork&ExecutionModes

Lecture 12: 11-BigData-ExecutionModes-YARN-Mode

Lecture 13: 12-BigData-SPARK-APIs-RDD-DataFrame-DataSet-Introduction

Lecture 14: 13-BigData-SPARK-Typical-Archetecture of Big Data-Technologies- and Industry Sta

Lecture 15: 14-BigData-INSTALL-Hadoop – SPARK and Jupyter for Spark on Windows

Lecture 16: 14-BigData-INSTALL-Hadoop – SPARK-Using Sandbox

Lecture 17: 14-BigData-INSTALL-Hadoop – SPARK-Using Sandbox-02

Lecture 18: 15-BigData-SPARK-Transformation-Actions-Practise-PySparkShell

Lecture 19: 16-BigData-SPARK-DataFrames-SparkSQL-Jupyter

Lecture 20: 17-BigData-SPARK-Transformation-Actions-using RDD-Jupyter

Lecture 21: 18-BigData-Spark-SQL-Hive Integration

Lecture 22: 19-BIgData-PySpark-RDD-Transformations and Actions-Operations-PySpark

Lecture 23: 20-BIgData-PySpark-DataFrame-Operations-PySpark

Chapter 8: ML LinearRegression

Lecture 1: ML-LinearRegression-Intro-01

Lecture 2: ML-LinearRegression-Intro-02

Lecture 3: ML-LinearRegression-03

Lecture 4: ML-LinearRegression-04

Lecture 5: ML-LinearRegression-05

Lecture 6: ML-LinearRegression-Summery-Metrics-06

Chapter 9: ML logistic regression

Lecture 1: ML-Logistic-Regression-01

Lecture 2: ML-Logistic-Regression-02

Chapter 10: ML KNN

Lecture 1: ML-KNN-Classification

Chapter 11: ML Decession_Trees

Lecture 1: ML-Decession-Trees-01

Lecture 2: ML-Bagging-RandomForest-02

Instructors

Mythili Eragamreddy
Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 0 votes

5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!