1Part-I
101Chapter 11. Tools Used In Data Mining
2Overview Of Big Data
10211.1 MonkeyLearn
3Part-II
10311.2 RapidMiner
4DATA MINING
10411.3 Oracle Data Mining
5Part-III
10511.4 IBM SPSS Modeler
6MACHINE LEARNING
10611.5 Weka
7Part 1
10711.6 KNIME
8Part 1
10811.7 H2O
9Overview of Big Data
10911.8 Orange
10Chapter 1. Exploring Big Data
11011.9 Apache Mahout
111.1 History of Big Data
11111.10 SAS Enterprise Mining
121.2 Examples of Big Data
11211.11 Rattle
131.3 Impacts of Big data
11311.12 Python
141.4 Future Expectations
11411.13 Summary
151.5 Career Scope
11511.14 Exercise
161.6 Tools Used
116Chapter 12. Data Mining Algorithms
171.7 Summary
11712.1 C4.5
181.9 Exercise
11812.2 k-means
19Chapter 2. Types of Big Data
11912.3 Support vector machines
202.1 Structured Data
12012.4 Apriori
212.2 Unstructured Data
12112.5 EM
222.3 Semi-Structured Data
12212.6 Page Rank
232.4 Difference between types of big data
12312.7 AdaBoost
242.5 Summary
12412.8 kNN
252.6 Exercise
12512.9 Naive Bayes
26Chapter 3. Characteristics of Big Data
12612.10 CART
273.1 Volume
12712.13 Summary
283.3 Veracity
12812.14 Exercise
293.4 Value
129Chapter 13. Applications and Drawbacks of Data Mining
303.5 Velocity
13013.1 Future Healthcare
313.6 Summary
13113.2 Market Based Analysis
323.8 Exercise
13213.3 Manufacturing Engineering
33Chapter 4. Working of Big Data
13313.4 Education
344.1 Integration
13413.5 CRM
354.2 Management
13513.6 Fraud Detection
364.3 Analysis
13613.7 Intrusion Detection
374.4 Big Data practices
13713.8 Lie Detection
384.5 Summary
13813.9 Customer Segmentation
394.6 Exercise
13913.10 Financial Banking
40Chapter 5. Advantages and Disadvantages of Big Data
14013.11 Corporate Surveillance
415.1 Benefits
14113.12 Research Analysis
425.2 Advantages of Big Data
14213.13 Criminal Investigation
435.3 Disadvantages of Big Data
14313.14 BioInformatics
445.4 Summary
14413.15 Companies extensively using Data Mining
455.5 Exercise
14513.16 Drawbacks of Data mining
46Chapter 6. Real-Time Applications
14613.17 Summary
476.1 Fields where big data is being used
14713.18 Exercise
486.2 Companies that use big data
148Part 3
496.3 Companies that provide big data
149Part 3
506.4 Summary
150Machine Learning
516.5 Exercise
151Chapter 14. Introduction to Machine Learning
52Chapter 7. Case Studies
15214.1 What is ML?
537.1 What are case studies?
15314.2 History of ML
547.2 Case Study 1 – Walmart
15414.3 How is it different from AI?
557.3 Case Study 2 – Uber
15514.4 Examples of Machine Learning
567.4 Case Study 3 – Netflix
15614.5 Demand Of Machine Learning
577.5 Case Study 4 – eBay
15714.6 Summary
587.6 Case Study 5 – Procter & Gamble
15814.7 Exercise
597.7 Case Study 6 – LinkedIn
159Chapter 15. Types of Machine Learning
607.8 Summary
16015.1 Supervised Learning
61Part 2
16115.2 Examples of Supervised Learning
62Part 2
16215.3 Unsupervised Learning
63Data Mining
16315.4 Examples of Unsupervised Learning
64Chapter 8. Overview of Data Mining
16415.5 Reinforcement Learning
658.1 What do you mean by Data Mining?
16515.6 Examples of Reinforcement Learning
668.2 History of Data Mining
16615.7 Summary
678.3 Tasks Related
16715.8 Exercise
688.3. Summarization
168Chapter 16. Models in Machine Learning
698.4 Career Scope
16916.1 Classification models
708.5 Future of Data Mining
17016.2 Regression models
718.6 Data Mining V/S Big Data
17116.3 Clustering models
728.7 Summary
17216.3.4 EM with GMM
738.8 Exercise
17316.4 Association models
74Chapter 9. Stages Of Data Mining Process
17416.5 Reinforcement learning models
759.1 Data Purification
17516.6 Difference between ML models
769.2 Data Integration
17616.7 Summary
779.3 Data Selection
17716.8 Exercise
789.4 Data Transformation
178Chapter 17. Emerging Trends in ML
799.5 Pattern Evaluation
17917.1 Intersection Of ML and IoT
809.6 Knowledge Representation
18017.2 Automated Machine Learning
819.7 Summary
18117.3 Machine Learning in Cyber Security
829.8 Exercise
18217.4 Rise of AI Ethics
83Chapter 10. Data Mining Techniques
18317.5 AI Engineering
8410.1 Tracking patterns
18417.6 AI-driven Biometric Security Solution
8510.2 Classification
18517.7 AI Analysis for the business forecast
8610.3 Clustering
18617.8 Companies dependent on ML
8710.4 Outlier detection
18717.9 Summary
8810.5 Association
18817.10 Exercise
8910.6 Regression
189Chapter 18. Model building in ML
9010.7 Prediction
19018.1 Defining the problem
9110.8 Sequential patterns
19118.2 Collecting Data
9210.9 Decision trees
19218.3 Measure of success
9310.10 Statistical techniques
19318.4 Evaluation Protocol
9410.11 Visualization
19418.5 Preparing the Data
9510.12 Neural networks
19518.6 Benchmark model
9610.13 Data warehousing
19618.7 Tunning the hyperparameters
9710.14 Long-term memory processing
19718.8 Summary
9810.15 Machine learning and AI
19818.9 Exercise
9910.16 Summary
199Glossary
10010.17 Exercises
200Index
