Machine Learning for Beginners. A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence

Introduction Chapter 1: What is Machine Learning? Definition of Machine Learning History of Machine Learning The Future of Machine Learning Application of Machine Learning Technology Industry Agricultural Industry Medical Industry Financial Industry Marketing Industry Human Behavior Industry Benefits of Machine Learning Practical Examples of Everyday Use of Machine Learning Chapter 2: Machine Learning Methods Supervised Learning Method Unsupervised Learning Method Semi-Supervised Learning Method Reinforcement Learning Method Other Learning Methods Chapter 3: Big Data Analysis What is Big Data? Why is Big Data Important? How is Big Data Used? Applications of Big Data in Today’s World Big Data Analysis Tools Zoho Analytics Cloudera Microsoft Power BI Oracle Analytics Cloud Pentaho Big Data Integration and Analytics SAS Institute Sisense Splunk Tableau Big Data and Machine Learning Chapter 4: Machine Learning Algorithms What is An Algorithm? What Are Machine Learning Algorithms? What is the Use of Machine Learning Algorithms? Chapter 5: K Means Clustering Algorithm What Is the K Means Clustering Algorithm? How Does This Algorithm Work? When Should This Algorithm be Used? Behavioral Segmentation Inventory Categorization Sorting Sensor Measurements Detecting Bots or Anomalies Tracking and Monitoring Classification Change Chapter 6: Artificial Neural Networks What Are Artificial Neural Networks? How Does They Work? When Should They be Used? Identification and Process Control General Game Playing Various Forms of Recognition 3D Reconstruction Diagnosis Finances Filtering Chapter 7: Decision Trees What Are Decision Trees? How Do Decision Trees Work? The Root Node Splitting The Decision Node The Leaf Node Pruning Branches Parent and Child Nodes How the Tree Works How the Tree is Read When Should Decision Trees be Used? Business Decisions Government Decisions Educational Decisions Programming Decisions Chapter 8: Naïve Bayes Classifier Algorithm What Is the Naïve Bayes Classifier Algorithm? How Does This Algorithm Work? Multinomial Naïve Bayes Bernoulli Naïve Bayes Gaussian Naïve Bayes When Should This Algorithm be Used? Filing Documents Spam and Priority Filters Chapter 9: Random Forests What Are Random Forests? How Do Random Forests Work? When Should Random Forests be Used? Chapter 10: Apriori Algorithm What Is the Apriori Algorithm? How Does This Algorithm Work? When Should This Algorithm be Used? Marketing Commerce Statistical Analysis Companies Mechanics Service Engineers Chapter 11: Linear and Logistic Regression What is Linear Regression? Multiple Linear Regression Ordinal Regression Multinominal Regression Discriminant Analysis How Does Linear Regression Work? When Should Linear Regression be Used? Budgeting Agriculture Retail – Ordering What is Logistical Regression? How Does Logistic Regression Work? When Should Logistic Regression be Used? Conclusion