Udemy – DataScience Machine Learning – NLP- Python-R-BigData-PySpark


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Udemy - DataScience Machine Learning - NLP- Python-R-BigData-PySpark
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 178 lectures (21h 48m) | Size: 10.1 GB
DataScience_Machine Learning – NLP- Python-R-BigData-PySpark

What you’ll learn:
Machine Learning
Not required
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.
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.
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.
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.
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.
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 MatDescriptionlib 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
Who this course is for
Experienced people looking for Career changes