MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 110 lectures (15h 7m) | Size: 7.2 GB
Data Science_Machine Learning with R Programming
What you’ll learn:
Data Science
Machine Learning
R Programming
Data Science
Requirements
Not required
Description
Data Science with R Training lets you gain expertise in Machine Learning Algorithms like K-Means
Clustering, Decision Trees, Random Forest, and Naive Bayes using R. 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
Who this course is for
Beginners
Freshers
Experienced people looking for Career changes
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