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Development Data Science Machine Learning

Machine Learning Made Easy : Beginner to Advanced using R

Learn Machine Learning Algorithms using R from experts with hands on examples and practice sessions. With 5 different pr
Rating: 4.2 out of 54.2 (73 ratings)
4,253 students
Created by Statinfer Solutions
Last updated 4/2018
English
English
30-Day Money-Back Guarantee

What you'll learn

  • R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.
  • Write your own R scripts and work in R environment.
  • Import, manipulate, clean up, sanitize and export datasets.
  • Understand basic statistics and implement using R.
  • Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
  • Do powerful analysis on data, find insights and present them in visual manner.
  • Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
  • Know how each machine learning algorithm works and which one to choose according to the type of problem.
  • Build more than one powerful machine learning model and be able to select the best one and improve it further.

Course content

12 sections • 129 lectures • 15h 16m total length

  • Preview05:31
  • Preview04:56
  • R Packages
    05:11
  • R Data types Vectors
    12:48
  • R Data Frames
    Preview13:17
  • List
    10:39
  • Preview04:12
  • R History and Scripts
    09:43
  • R Functions
    05:06
  • Errors
    07:29
  • Introduction to R quiz
    15 questions

  • Preview01:30
  • Preview06:00
  • Checklist
    05:14
  • Subsetting the Data
    04:41
  • Subsetting Variable Condition
    05:38
  • Calculated Fields_ifelse
    07:27
  • Preview09:02
  • Joining and Merging
    05:22
  • Exporting the Data
    03:02
  • Data handling quiz
    8 questions

  • Introduction and Sampling
    03:35
  • Descriptive Statistics
    09:09
  • Percentiles and Quartiles
    04:23
  • Box Plots
    Preview04:53
  • Creating Graphs and Conclusions
    07:01
  • Basic Statistics and graph quiz
    14 questions

  • Preview02:17
  • Preview07:19
  • Preview06:59
  • CS lab step one basic content of dataset
    10:01
  • Variable Level Exploration Catagorical
    04:35
  • Reading Data Dictionary
    10:19
  • Preview12:47
  • Step three Lab Variable Level Exploration Continues
    11:00
  • Preview07:45
  • Step four Treatment-Scenario 1
    06:38
  • Step four Treatment-Scenario 2
    09:25
  • Preview05:31
  • Some Other Variables
    02:40
  • Conclusions
    02:20

  • Preview04:19
  • LBA Correlation Calculation in R
    04:35
  • Beyond Pearson Correlation
    03:09
  • Preview09:26
  • Regression Line Fitting in R
    07:59
  • Preview11:54
  • Multiple Regression
    09:40
  • Adjusted R Squared
    04:50
  • Preview11:45
  • Preview12:42
  • Regression Conclusion
    02:09
  • Regression Quiz
    15 questions

  • Preview14:05
  • Preview09:14
  • Multiple Logistic Regression
    07:25
  • Goodness of Fit for a Logistic Regression
    Preview11:43
  • Multicollinearity in Logistic Regression
    07:18
  • Individual Impact of Variables
    04:53
  • Preview12:00
  • Logistic Regression Conclusion
    01:23
  • Logistic Regression Quiz
    9 questions

  • Preview06:31
  • Preview14:14
  • The Splitting Criterion & Entropy Calculation
    15:24
  • Information Gain & Calculation
    08:56
  • Preview10:35
  • Split for Variable & The Decision Tree Lab - Part 1
    14:24
  • The Decision Tree Lab - Part 2 & Validation
    12:04
  • The Decision Tree Lab - Part 3 & Overfitting
    16:09
  • Preview05:33
  • Preview10:44
  • Preview03:00
  • Preview14:20
  • Conclusion
    02:04
  • Decision Trees Quiz
    11 questions

  • Preview02:00
  • Sensitivity Specificity
    09:20
  • Preview09:30
  • ROC AUC
    09:57
  • Preview04:11
  • Errors
    05:50
  • Preview13:01
  • Bias_Variance Treadoff
    09:53
  • Preview05:08
  • Ten fold CV
    10:39
  • Kfold CV
    09:00
  • Preview01:43
  • Model selection cross validation Quiz
    15 questions

  • Introduction and Logistic Regression Recap
    06:45
  • Preview03:01
  • Preview06:42
  • Non Linear Decision Boundary and Solution
    10:45
  • Preview07:35
  • Neural Net Algorithm
    06:47
  • Preview06:16
  • Building a Neural Network
    10:17
  • Local Vs Global Min
    05:09
  • Preview03:35
  • Digit Recognizer second attempt part2
    06:02
  • Lab Digit Reconizer
    04:10
  • Preview05:36
  • Neural Networks
    8 questions

  • Introduction to SVM
    02:01
  • The Classifier and Decision Boundary
    Preview05:29
  • SVM- The Large Margin Classifier
    01:34
  • The SVM Alogirithm and Results
    03:55
  • Preview04:46
  • Non Linear Boundary
    03:38
  • Preview05:52
  • Preview06:45
  • Soft Margin and Validation
    03:46
  • SVM Advantage, Disadvantage and Applications
    02:56
  • Preview08:58
  • SVM Conclusion
    01:08
  • support vector machine
    7 questions

Requirements

  • Familiarity with high school mathematics.

Description

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

  • Part 1 – Introduction to R Programming.
    • This is the part where you will learn basic of R programming and familiarize yourself with R environment.
    • Be able to import, export, explore, clean and prepare the data for advance modeling.
    • Understand the underlying statistics of data and how to report/document the insights.
  • Part 2 – Machine Learning using R
    • Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
    • Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.
    • Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Features:

  • Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
  • Course includes R code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.
  • Quiz after each section to test your learning.

Bonus:

  • This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.
  • These projects will serve as your step by step guide to solve different business and data science problems.

Who this course is for:

  • Anyone interested in Data Science and Machine Learning.
  • Students who want a head start in Data Science field.
  • Data analysts who want to upgrade their skills in Machine Learning.
  • People who want to add value to their work and business by using Machine Learning.
  • People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.
  • People interested in understanding application of machine learning algorithms on real business problems.
  • People interested in understanding how a machine learning algorithm works and what's the math behind it.

Instructor

Statinfer Solutions
Data Science starts here!
Statinfer Solutions
  • 3.9 Instructor Rating
  • 642 Reviews
  • 23,926 Students
  • 5 Courses

Statinfer is the data science e-learning solutions provider. We provide online and class room training on leading data science tools and techniques.

Our focus is on data analytics, machine learning, and AI. The tools that we work on are R, Python, Tensor Flow and Spark.

Statinfer is created by data scientists who understand the dynamics of the current business.

Our courses are not merely academic, instead, there are many industrial applications and examples. The creators assembled the course, well studied the topics with a clear understanding and had designed the curriculum.

Each course has ample amount of self-practicing labs, quizzes and projects on real data to get an exposure to real world problems.

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