Data Analysis Using Machine Learning Techniques
Analyze data quickly and easily with several machine-learning techniques.
Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This course empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.
In this course, you'll learn
- Learn to handle missing values and duplicates
- Learn to scale and standardize values
- Reveal underlying patterns
- Learn to apply classification techniques
- Learn to apply regression techniques
- Learn to reduce data
By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.
This course follows a recipe-based approach. Here each lecture resents you with a step-by-step approach to performing many important data analytics tasks.
Class Curriculum
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StartLecture 1 Welcome (2:03)
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StartLecture 2 Reading Data from CSV Files (6:23)
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StartLecture 3 Reading XML and JSON Data (5:57)
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StartLecture 4 Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries (6:26)
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StartLecture 5 Removing and Replacing Missing Values (6:03)
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StartLecture 6 Removing Duplicate Cases (1:54)
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StartLecture 7 Rescaling a Variable (2:05)
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StartLecture 8 Normalizing or Standardizing Data in a Data Frame (2:54)
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StartLecture 9 Binning Numerical Data (3:17)
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StartLecture 10 Creating Dummies for Categorical Variables (3:38)
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StartLecture 11 Creating Standard Data Summaries (3:19)
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StartLecture 12 Extracting Subset of a Dataset (5:36)
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StartLecture 13 Splitting a Dataset (1:46)
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StartLecture 14 Creating Random Data Partitions (7:37)
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StartLecture 15 Generating Standard Plots (5:14)
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StartLecture 16 Generating Multiple Plots (1:41)
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StartLecture 17 Selecting a Graphics Device (1:43)
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StartLecture 18 Creating Plots with the Lattice and ggplot2package (8:54)
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StartLecture 19 Creating Charts that Facilitate Comparisons (2:30)
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StartLecture 20 Creating Charts that Visualize Possible Causality (1:23)
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StartLecture 21 Creating Multivariate Plots (2:02)
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StartLecture 22 Generating ErrorClassification-Confusion Matrices (4:19)
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StartLecture 23 Generating ROC Charts (3:38)
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StartLecture 24 Building, Plotting, and Evaluating – Classification Trees (5:55)
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StartLecture 25 Using random Forest Models for Classification (4:09)
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StartLecture 26 Classifying Using the Support Vector Machine Approach (5:16)
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StartLecture 27 Classifying Using the Naïve Bayes Approach (2:11)
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StartLecture 28 Classifying Using the KNN Approach (4:51)
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StartLecture 29 Using Neural Networks for Classification (4:07)
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StartLecture 30 Classifying Using Linear Discriminant Function Analysis (2:36)
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StartLecture 31 Classifying Using Logistic Regression (3:48)
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StartLecture 32 Using AdaBoost to Combine Classification Tree Models (3:19)
Bill Gates Says These Are the Jobs He Would Drop Out of College for Today
If he were to go into computer sciences today, Gates said, the area that he thought had the most potential was artificial intelligence. Gates brought up a recent victory by Google DeepMind over the top player in the world at Go, a game some predicted a computer could never master. He called it a remarkable achievement that signaled there is more to come in advancement in artificial intelligence. And he said the research being done in the field now is "profound" and on the verge of making new breakthroughs. "The ability for artificial agents to read and understand material is going to be phenomenal," says Gates. "Anything connected with that would be an exciting lifetime career."