Data Science Certification Training - R Programming
Simplilearn Americas LLC
Course Summary
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
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Course Description
What are the System Requirements?
You will need to download R from the CRAN website and RStudio for your operating system. These are both open source and the installation guidelines are presented in the data science course.Who are our instructors and how are they selected?
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty for data science online training.What training formats are used for this course?
We offer this data science with R certification course in the following formats:
Live Virtual Classroom or Online Classroom: With online classroom training, you have the option to attend the course remotely from your desktop via video conferencing. This format reduces productivity challenges and decreases your time spent away from work or home.
Online Self-Learning: In this mode, you’ll receive lecture videos that you can view at your own pace.What if I miss a class?
We record the class sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.Can I cancel my enrollment? Will I get a refund?
Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.Who provides the certification?
At the end of the training, subject to satisfactory evaluation of the project and passing the online exam (minimum 80%), you will receive a certificate from Simplilearn stating that you are a certified data scientist with R programming.Are there any group discounts for classroom training programs?
Yes, we offer group discounts for our online training programs. Get in touch with us over the Drop us a Query/Request a Callback/Live Chat channels to find out more about our group discount packages.What payment options are available?
Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.Visa Credit or Debit Card
MasterCard
American Express
Diner’s Club
PayPal
I’d like to learn more about this training program. Whom should I contact?
Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.What is the Expert Assistant Support provided by Simplilearn?
Expert Assistance includes:Mentoring Sessions: Live Interaction with a subject matter expert to help participants with queries regarding project implementation and the course in general
Guidance on forum: Industry experts to respond to participant queries regarding technical concepts, projects and case studies.
Teaching Assistance includes:Project Assistance: Queries related to solving and completing projects and case studies, which are part of the Data Scientist with R programming course
Technical Assistance: Queries related to technical, installation and administration issues in Data Scientist with R programming training. In cases of critical issues, support will be rendered through a remote desktop.
R Programming: Queries related to R programming while solving and completing projects and case studies
How do I contact support?
Submit a request to Simplilearn through any of following channels: Help & Support, Simplitalk, or Live Chat. A teaching assistant will get in touch with you within 48 hours.What is Global Teaching Assistance?
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
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Course Syllabus
Course preview
Data Science with R
Lesson 01 - Introduction to Business Analytics 22:29
1.1 Introduction 00:10
1.2 Objectives 00:15
1.3 Need of Business Analytics 01:28
1.4 Business Decisions 00:22
1.5 Business Decisions (contd.) 00:07
1.6 Introduction to Business Analytics 01:09
1.7 Features of Business Analytics 01:20
1.8 Types of Business Analytics 00:19
1.9 Descriptive Analytics 00:55
1.10 Predictive Analytics 01:09
1.11 Predictive Analytics (contd.) 00:41
1.12 Prescriptive Analytics 01:13
1.13 Prescriptive Analytics (contd.) 00:24
1.14 Supply Chain Analytics 00:56
1.15 Health Care Analytics 00:40
1.16 Marketing Analytics 00:44
1.17 Human Resource Analytics 00:36
1.18 Web Analytics 00:46
1.19 Application of Business Analytics - Wal-Mart Case Study 00:16
1.20 Application of Business Analytics - Wal-Mart Case Study (contd.) 00:29
1.21 Application of Business Analytics - Wal-Mart Case Study (contd.) 00:35
1.22 Application of Business Analytics - Signet Bank Case Study 00:29
1.23 Application of Business Analytics - Signet Bank Case Study (contd.) 00:46
1.24 Application of Business Analytics - Signet Bank Case Study (contd.) 00:44
1.25 Business Decisions 00:37
1.26 Business Intelligence (BI) 01:09
1.27 Data Science 00:33
1.28 Importance of Data Science 00:35
1.29 Data Science as a Strategic Asset 00:25
1.30 Big Data 00:39
1.31 Analytical Tools 00:16
1.32 Quiz
1.33 Summary 00:52
1.34 Summary (contd.) 00:39
1.35 Conclusion 00:11
Lesson 02 - Introduction to R 15:36
2.1 Introduction 00:11
2.2 Objectives 00:21
2.3 An Introduction to R 00:56
2.4 Comprehensive R Archive Network (CRAN) 00:39
2.5 Cons of R 01:00
2.6 Companies Using R 01:16
2.7 Understanding R 00:55
2.8 Installing R on Various Operating Systems 00:09
2.9 Installing R on Windows from CRAN Website 00:15
2.10 Installing R on Windows from CRAN Website (contd.) 00:20
2.11 Installing R on Windows from CRAN Website (contd.) 00:09
2.12 Demo - Install R 00:06
2.13 Install R 01:02
2.14 IDEs for R 00:50
2.15 Installing RStudio on Various Operating Systems 00:34
2.16 Demo - Install RStudio 00:06
2.17 Install RStudio 00:51
2.18 Steps in R Initiation 00:20
2.19 Benefits of R Workspace 00:40
2.20 Setting the Workplace 00:08
2.21 Functions and Help in R 00:28
2.22 Demo - Access the Help Document 00:05
2.23 Access the Help Document 01:11
2.24 R Packages 00:48
2.25 Installing an R Package 00:10
2.26 Demo - Install and Load a Package 00:05
2.27 Install and Load a Package 00:56
2.28 Quiz
2.29 Summary 00:33
2.30 Summary (contd.) 00:21
2.31 Conclusion 00:11
Lesson 03 - R Programming 25:23
3.1 Introduction 00:10
3.2 Objectives 00:20
3.3 Operators in R 00:15
3.4 Arithmetic Operators 00:21
3.5 Demo - Perform Arithmetic Operations 00:05
3.6 Use Arithmetic Operations 02:00
3.7 Relational Operators 00:16
3.8 Demo - Use Relational Operators 00:05
3.9 Use Relational Operators 01:00
3.10 Logical Operators 00:41
3.11 Demo - Perform Logical Operations 00:05
3.12 Use Logical Operators 01:22
3.13 Assignment Operators 00:13
3.14 Demo - Use Assignment Operator 00:05
3.15 Use Assignment Operator 00:32
3.16 Conditional Statements in R 00:24
3.17 Conditional Statements in R (contd.) 00:34
3.18 Conditional Statements in R (contd.) 00:32
3.19 Ifelse() Function 00:18
3.20 Demo - Use Conditional Statements 00:06
3.21 Use Conditional Statements 01:44
3.22 Switch Function 00:45
3.23 Demo - Use the Switch Function 00:05
3.24 Use Switch Function 01:39
3.25 Loops in R 00:14
3.26 Loops in R (contd.) 00:33
3.27 Loops in R (contd.) 00:18
3.28 Loops in R (contd.) 00:31
3.29 Break Statement 00:38
3.30 Next Statement 00:35
3.31 Demo - Use Loops 00:05
3.32 Use Loops 02:37
3.33 Scan() Function 01:04
3.34 Running an R Script 00:40
3.35 Running a Batch Script 00:20
3.36 R Functions 00:33
3.37 R Functions (contd.) 00:05
3.38 Demo - Use R Functions 00:06
3.39 Use Commonly Used Functions 01:37
3.40 Demo - Use String Functions 00:07
3.41 Use Commonly-USed String Functions 00:53
3.42 Quiz
3.43 Summary 00:39
3.44 Conclusion 00:11
Lesson 04 - R Data Structure 26:50
4.1 Introduction 00:10
4.2 Objectives 00:16
4.3 Types of Data Structures in R 00:41
4.4 Vectors 00:47
4.5 Demo - Create a Vector 00:05
4.6 Create a Vector 01:24
4.7 Scalars 00:12
4.8 Colon Operator 00:15
4.9 Accessing Vector Elements 00:44
4.10 Matrices 00:35
4.11 Matrices (contd.) 00:18
4.12 Accessing Matrix Elements 00:23
4.13 Demo - Create a Matrix 00:05
4.14 Create a Matrix 01:45
4.15 Arrays 00:33
4.16 Accessing Array Elements 00:14
4.17 Demo - Create an Array 00:05
4.18 Create an Array 01:31
4.19 Data Frames 00:57
4.20 Elements of Data Frames 00:13
4.21 Demo - Create a Data Frame 00:05
4.22 Create a Data Frame 01:54
4.23 Factors 00:41
4.24 Demo - Create a Factor 00:05
4.25 Create a Factor 01:49
4.26 Lists 00:20
4.27 Demo - Create a List 00:05
4.28 Create a List 01:14
4.29 Importing Files in R 00:22
4.30 Importing an Excel File 00:53
4.31 Importing a Minitab File 00:20
4.32 Importing a Table File 00:29
4.33 Importing a CSV File 00:43
4.34 Demo - Read Data from a File 00:05
4.35 Read Data from a File 03:50
4.36 Exporting Files from R 00:33
4.37 Exporting Files from R (contd.) 00:37
4.38 Exporting Files from R (contd.) 00:17
4.39 Exporting Files from R (contd.) 00:38
4.40 Quiz
4.41 Summary 00:27
4.42 Conclusion 00:10
Lesson 05 - Apply Functions 28:01
5.1 Introduction 00:13
5.2 Objectives 00:15
5.3 Types of Apply Functions 00:31
5.4 Apply() Function 00:13
5.5 Apply() Function (contd.) 00:57
5.6 Apply() Function (contd.) 00:31
5.7 Demo - Use Apply() Function 00:05
5.8 Use Apply Function 01:10
5.9 Lapply() Function 01:05
5.10 Demo - Use Lapply() Function 00:05
5.11 Use Lapply Function 00:54
5.12 Sapply() Function 00:56
5.13 Demo - Use Sapply() Function 00:05
5.14 Use Sapply Function 01:10
5.15 Tapply() Function 00:28
5.16 Tapply() Function (contd.) 00:23
5.17 Tapply() Function (contd.) 00:19
5.18 Demo - Use Tapply() Function 00:05
5.19 Use Tapply Function 01:22
5.20 Vapply() Function 00:47
5.21 Demo - Use Vapply() Function 00:05
5.22 Use Vapply Function 01:57
5.23 Mapply() Function 00:21
5.24 Mapply() Function (contd.) 00:16
5.25 Mapply() Function (contd.) 00:34
5.26 Dplyr Package - An Overview 01:08
5.27 Dplyr Package - The Five Verbs 00:51
5.28 Installing the Dplyr Package 00:15
5.29 Functions of the Dplyr Package 00:20
5.30 Functions of the Dplyr Package - Select() 00:30
5.31 Demo - Use the Select() Function 00:06
5.32 Use the Select Function 01:35
5.33 Functions of Dplyr-Package - Filter() 00:59
5.34 Demo - Use the Filter() Function 00:05
5.35 Use Select Function 01:14
5.36 Functions of Dplyr Package - Arrange() 00:10
5.37 Demo - Use the Arrange() Function 00:06
5.38 Use Arrange Function 01:29
5.39 Functions of Dplyr Package - Mutate() 00:21
5.40 Functions of Dply Package - Summarise() 00:53
5.41 Functions of Dplyr Package - Summarise() (contd.) 00:40
5.42 Demo - Use the Summarise() Function 00:06
5.43 Use Summarise Function 01:42
5.44 Quiz
5.45 Summary 00:33
5.46 Conclusion 00:11
Lesson 06 - Data Visualization 33:48
6.1 Introduction 00:11
6.2 Objectives 00:17
6.3 Graphics in R 00:38
6.4 Types of Graphics 00:25
6.5 Bar Charts 00:34
6.6 Creating Simple Bar Charts 00:33
6.7 Editing a Simple Bar Chart 00:34
6.8 Demo - Create a Bar Chart 00:06
6.9 Create a Bar Chart 01:50
6.10 Editing a Simple Bar Chart (contd.) 00:39
6.11 Editing a Simple Bar Chart (contd.) 00:26
6.12 Demo - Create a Stacked Bar Plot and Grouped Bar Plot 00:07
6.13 Create a Stacked Bar Plot and Grouped Bar Plot 01:58
6.14 Pie Charts 00:51
6.15 Editing a Pie Chart 00:27
6.16 Editing a Pie Chart (contd.) 00:28
6.17 Demo - Create a Pie Chart 00:05
6.18 Create a Pie Chart 03:01
6.19 Histograms 00:53
6.20 Creating a Histogram 00:37
6.21 Kernel Density Plots 00:19
6.22 Creating a Kernel Density Plot 00:29
6.23 Demo - Create Histograms and a Density Plot 00:07
6.24 Create Histograms and a Density Plot 02:23
6.25 Line Charts 00:30
6.26 Creating a Line Chart 00:21
6.27 Box Plots 00:47
6.28 Creating a Box Plot 00:53
6.29 Demo - Create Line Graphs and a Box Plot 00:07
6.30 Create Line Graphs and a Box Plot 01:59
6.31 Heat Maps 00:48
6.32 Creating a Heat Map 00:28
6.33 Demo - Create a Heat Map 00:06
6.34 Create a Heatmap 01:10
6.35 Word Clouds 00:28
6.36 Creating a Word Cloud 00:52
6.37 Demo - Create a Word Cloud 00:06
6.38 Create a Word Cloud 01:23
6.39 File Formats for Graphic Outputs 00:51
6.40 Saving a Graphic Output as a File 01:02
6.41 Saving a Graphic Output as a File (contd.) 00:43
6.42 Demo - Save Graphics to a File 00:06
6.43 Save Graphics to a File 00:49
6.44 Exporting Graphs in RStudio 00:27
6.45 Exporting Graphs as PDFs in RStudio 00:17
6.46 Demo - Save Graphics Using RStudio 00:06
6.47 Save Graphics Using RStudio 00:53
6.48 Quiz
6.49 Summary 00:27
6.50 Conclusion 00:11
Lesson 07 - Introduction to Statistics 33:59
7.1 Introduction 00:10
7.2 Objectives 00:21
7.3 Basics of Statistics 02:03
7.4 Types of Data 01:20
7.5 Qualitative vs. Quantitative Analysis 00:52
7.6 Types of Measurements in Order 00:35
7.7 Nominal Measurement 00:46
7.8 Ordinal Measurement 00:43
7.9 Interval Measurement 00:49
7.10 Ratio Measurement 00:59
7.11 Statistical Investigation 00:13
7.12 Statistical Investigation Steps 01:03
7.13 Normal Distribution 00:58
7.14 Normal Distribution (contd.) 00:36
7.15 Example of Normal Distribution 00:08
7.16 Importance of Normal Distribution in Statistics 00:34
7.17 Use of the Symmetry Property of Normal Distribution 00:52
7.18 Standard Normal Distribution 00:33
7.19 Demo - Use Probability Distribution Functions 00:07
7.20 Use Probability Distribution Functions 06:52
7.21 Distance Measures 00:42
7.22 Distance Measures - A Comparison 00:26
7.23 Euclidean Distance 00:24
7.24 Example of Euclidean Distance 00:37
7.25 Manhattan Distance 00:31
7.26 Minkowski Distance 00:15
7.27 Mahalanobis Distance 00:27
7.28 Cosine Similarity 00:26
7.29 Correlation 00:43
7.30 Correlation Measures Explained 01:10
7.31 Pearson Product Moment Correlation (PPMC) 00:41
7.32 Pearson Product Moment Correlation (PPMC) (contd.) 00:35
7.33 Pearson Correlation - Case Study 00:35
7.34 Dist() Function in R 00:40
7.35 Demo - Perform the Distance Matrix Computations 00:08
7.36 Perform the Distance Matrix Computations 03:44
7.37 Quiz
7.38 Summary 00:35
7.39 Summary (contd.) 00:35
7.40 Conclusion 00:11
Lesson 08 - Hypothesis Testing I 19:29
8.1 Introduction 00:11
8.2 Objectives 00:22
8.3 Hypothesis 02:01
8.4 Need of Hypothesis Testing in Businesses 00:52
8.5 Null Hypothesis 00:26
8.6 Null Hypothesis (contd.) 00:34
8.7 Alternate Hypothesis 00:37
8.8 Null vs. Alternate Hypothesis 00:33
8.9 Chances of Errors in Sampling 00:30
8.10 Types of Errors 00:57
8.11 Contingency Table 01:15
8.12 Decision Making 00:24
8.13 Critical Region 00:42
8.14 Level of Significance 00:51
8.15 Confidence Coefficient 00:49
8.16 Bita Risk 00:26
8.17 Power of Test 00:28
8.18 Factors Affecting the Power of Test 00:23
8.19 Types of Statistical Hypothesis Tests 01:05
8.20 An Example of Statistical Hypothesis Tests 00:31
8.21 An Example of Statistical Hypothesis Tests (contd.) 00:17
8.22 An Example of Statistical Hypothesis Tests (contd.) 00:19
8.23 An Example of Statistical Hypothesis Tests (contd.) 00:23
8.24 Upper Tail Test 00:30
8.25 Upper Tail Test (contd.) 00:27
8.26 Upper Tail Test (contd.) 00:19
8.27 Test Statistic 00:47
8.28 Factors Affecting Test Statistic 00:12
8.29 Factors Affecting Test Statistic (contd.) 00:39
8.30 Factors Affecting Test Statistic (contd.) 00:09
8.31 Critical Value Using Normal Probability Table 00:17
8.32 Quiz
8.33 Summary 01:02
8.34 Conclusion 00:11
Lesson 09 - Hypothesis Testing II 39:45
9.1 Introduction 00:11
9.2 Objectives 00:15
9.3 Parametric Tests 00:35
9.4 Z-Test 00:23
9.5 Z-Test in R - Case Study 00:50
9.6 T-Test 00:30
9.7 T-Test in R - Case Study 00:35
9.8 Demo - Use Normal and Student Probability Distribution Functions 00:08
9.9 Use Normal and Student Probability Distribution Functions 01:32
9.10 Testing Null Hypothesis 00:50
9.11 Testing Null Hypothesis 00:08
9.12 Testing Null Hypothesis 00:09
9.13 Testing Null Hypothesis 00:20
9.14 Testing Null Hypothesis 00:14
9.15 Testing Null Hypothesis 01:00
9.16 Objectives of Null Hypothesis Test 00:58
9.17 Three Types of Hypothesis Tests 00:17
9.18 Hypothesis Tests About Population Means 00:42
9.19 Hypothesis Tests About Population Means (contd.) 00:50
9.20 Hypothesis Tests About Population Means (contd.) 00:27
9.21 Decision Rules 01:21
9.22 Hypothesis Tests About Population Means - Case Study 1 01:30
9.23 Hypothesis Tests About Population Means - Case Study 2 01:21
9.24 Hypothesis Tests About Population Means - Case Study 2 (contd.) 00:22
9.25 Hypothesis Tests About Population Proportions 00:28
9.26 Hypothesis Tests About Population Proportions (contd.) 00:29
9.27 Hypothesis Tests About Population Proportions (contd.) 01:03
9.28 Hypothesis Tests About Population Proportions - Case Study 1 00:22
9.29 Hypothesis Tests About Population Proportions - Case Study 1 (contd.) 00:55
9.30 Chi-Square Test 00:28
9.31 Steps of Chi-Square Test 00:38
9.32 Steps of Chi-Square Test (contd.) 00:30
9.33 Important Points of Chi-Square Test (contd.) 00:31
9.34 Degree of Freedom 00:35
9.35 Chi-Square Test for Independence 00:51
9.36 Chi-Square Test for Goodness of Fit 00:42
9.37 Chi-Square Test for Independence - Case Study 00:28
9.38 Chi-Squar Test for Independence - Case Study (contd.) 00:26
9.39 Chi-Square Test in R - Case Study 00:38
9.40 Chi-Square Test in R - Case Study (contd.) 00:31
9.41 Demo - Use Chi-Squared Test Statistics 00:10
9.42 Use Chi-Squared Test Statistics 02:35
9.43 Introduction to ANOVA Test 01:03
9.44 One-Way ANOVA Test 01:10
9.45 The F-Distribution and F-Ratio 01:22
9.46 F-Ratio Test 00:37
9.47 F-Ratio Test in R - Example 00:22
9.48 One-Way ANOVA Test - Case Study 00:20
9.49 One-Way ANOVA Test - Case Study (contd.) 00:45
9.50 One-Way ANOVA Test in R - Case Study 00:49
9.51 One-Way ANOVA Test in R - Case Study (contd.) 00:29
9.52 One-Way ANOVA Test in R - Case Study (contd.) 00:35
9.53 Demo - Perform ANOVA 00:07
9.54 Perform ANOVA 02:55
9.55 Quiz
9.56 Summary 01:12
9.57 Conclusion 00:11
Lesson 10 - Regression Analysis 20:48
10.1 Introduction 00:11
10.2 Objectives 00:14
10.3 Introduction to Regression Analysis 00:53
10.4 Use of Regression Analysis - Examples 00:24
10.5 Use of Regression Analysis - Examples (contd.) 00:23
10.6 Types Regression Analysis 00:39
10.7 Simple Regression Analysis 00:27
10.8 Multiple Regression Models 00:25
10.9 Simple Linear Regression Model 00:37
10.10 Simple Linear Regression Model Explained 00:29
10.11 Demo - Perform Simple Linear Regression 00:06
10.12 Perform Simple Linear Regression 02:13
10.13 Correlation 00:20
10.14 Correlation Between X and Y 00:27
10.15 Correlation Between X and Y (contd.) 00:24
10.16 Demo - Find Correlation 00:06
10.17 Find Correlation 01:23
10.18 Method of Least Squares Regression Model 01:02
10.19 Coefficient of Multiple Determination Regression Model 00:29
10.20 Standard Error of the Estimate Regression Model 00:44
10.21 Dummy Variable Regression Model 01:07
10.22 Interaction Regression Model 00:23
10.23 Non-Linear Regression 00:29
10.24 Non-Linear Regression Models 01:24
10.25 Non-Linear Regression Models (contd.) 01:03
10.26 Non-Linear Regression Models (contd.) 00:23
10.27 Demo - Perform Regression Analysis with Multiple Variables 00:07
10.28 Perform Regression Analysis with Multiple Variables 01:46
10.29 Non-Linear Models to Linear Models 00:13
10.30 Algorithms for Complex Non-Linear Models 00:53
10.31 Quiz
10.32 Summary 00:26
10.33 Summary (contd.) 00:28
10.34 Conclusion 00:10
Lesson 11 - Classification 33:43
11.1 Introduction 00:10
11.2 Objectives 00:17
11.3 Introduction to Classification 00:40
11.4 Examples of Classification 00:23
11.5 Classification vs. Prediction 00:45
11.6 Classification System 00:10
11.7 Classification Process 00:54
11.8 Classification Process - Model Construction 01:03
11.9 Classification Process - Model Usage in Prediction 00:22
11.10 Issues Regarding Classification and Prediction 00:15
11.11 Data Preparation Issues 01:06
11.12 Evaluating Classification Methods Issues 00:34
11.13 Decision Tree 00:51
11.14 Decision Tree - Dataset 00:14
11.15 Decision Tree - Dataset (contd.) 00:15
11.16 Classification Rules of Trees 00:34
11.17 Overfitting in Classification 01:13
11.18 Tips to Find the Final Tree Size 01:13
11.19 Basic Algorithm for a Decision Tree 00:42
11.20 Statistical Measure - Information Gain 01:16
11.21 Calculating Information Gain - Example 00:08
11.22 Calculating Information Gain - Example (contd.) 00:05
11.23 Calculating Information Gain for Continuous-Value Attributes 01:44
11.24 Enhancing a Basic Tree 00:32
11.25 Decision Trees in Data Mining 00:18
11.26 Demo - Model a Decision Tree 00:05
11.27 Model a Decision Tree 02:06
11.28 Naive Bayes Classifier Model 01:02
11.29 Features of Naive Bayes Classifier Model 00:41
11.30 Bayesian Theorem 00:40
11.31 Bayesian Theorem (contd.) 00:14
11.32 Naive Bayes Classifier 00:29
11.33 Applying Naive Bayes Classifier - Example 00:14
11.34 Applying Naive Bayes Classifier - Example (contd.) 00:25
11.35 Naive Bayes Classifier - Advantages and Disadvantages 00:28
11.36 Demo - Perform Classification Using the Naive Bayes Method 00:07
11.37 Perform Classification Using the Naive Bayes Method 02:31
11.38 Nearest Neighbor Classifiers 01:05
11.39 Nearest Neighbor Classifiers (contd.) 00:20
11.40 Nearest Neighbor Classifiers (contd.) 00:12
11.41 Computing Distance and Determining Class 00:34
11.42 Choosing the Value of K 00:21
11.43 Scaling Issues in Nearest Neighbor Classification 00:35
11.44 Support Vector Machines 01:19
11.45 Advantages of Support Vector Machines 00:29
11.46 Geometric Margin in SVMs 00:47
11.47 Linear SVMs 00:08
11.48 Non-Linear SVMs 00:26
11.49 Demo - Support a Vector Machine 00:05
11.50 Support a Vector Machine 01:51
11.51 Quiz
11.52 Summary 00:36
11.53 Conclusion 00:09
Lesson 12 - Clustering 25:12
12.1 Introduction 00:11
12.2 Objectives 00:10
12.3 Introduction to Clustering 00:42
12.4 Clustering vs. Classification 00:58
12.5 Use Cases of Clustering 00:33
12.6 Clustering Models 01:47
12.7 K-means Clustering 01:29
12.8 K-means Clustering Algorithm 00:57
12.9 Pseudo Code of K-means 00:33
12.10 K-means Clustering Using R 00:40
12.11 K-means Clustering - Case Study 00:26
12.12 K-means Clustering - Case Study (contd.) 00:44
12.13 K-means Clustering - Case Study (contd.) 01:23
12.14 Demo - Perform Clustering Using K-means 00:05
12.15 Perform Clustering Using Kmeans 01:38
12.16 Hierarchical Clustering 01:12
12.17 Hierarchical Clustering Algorithms 00:36
12.18 Requirements of Hierarchical Clustering Algorithms 01:15
12.19 Agglomerative Clustering Process 00:37
12.20 Hierarchical Clustering - Case Study 00:37
12.21 Hierarchical Clustering - Case Study (contd.) 00:10
12.22 Hierarchical Clustering - Case Study (contd.) 00:22
12.23 Demo - Perform Hierarchical Clustering 00:05
12.24 Perform Hierarchical Clustering 01:24
12.25 DBSCAN Clustering 01:01
12.26 Concepts of DBSCAN 00:54
12.27 Concepts of DBSCAN (contd.) 00:51
