Price: $4,999.00

Course Number: 9011
Length: 4 Days
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Data Mining Training Bootcamp is the answer to your data mining needs from current to next generation applications, strategies, technologies, and methods. Our mission is to clarify highly complex data mining technical standards and topics.

Combining data mining, big data, statistics, data visualization, machine learning, deep learning and artificial intelligence this bootcamp will give you a sophisticated understanding of how to use of data, mining and quantitative techniques to help your organization improve its processes. You’ll understand the science behind the data mining  because the interdisciplinary coursework focuses on analysis, data structures and domain knowledge.

Data mining is the process of analyzing a data set to find insights and patterns. After the data is collected in a data warehouse, the data mining process begins to clean the data of incomplete records to creating visualizations of findings.

Data mining is usually associated with the analysis of the large data sets present in the fields of big data, machine learning and artificial intelligence. Learn about the process for patterns, anomalies and associations in the data with the goal of extracting value.

Elective Topics

Introduction

  • Business Intelligence Fundamentals
  • Data Warehousing Fundamentals
  • Data mining level I
  • Advanced Data mining
  • Data mining applied

Data Mining and Applications

  • Data Mining and Analysis
  • Introduction to Statistical Learning
  • Modern Applied Statistics Applied to Data Mining
  • Paradigms for Computing with Data
  • Mining Massive Data Sets
  • Information Retrieval and Search
  • Principles of Machine Learning
  • Mining Massive Data Sets
  • Information Network Analysis
  • Quantitative Methods in Data Mining
  • Algorithmic Trading and Quantitative Strategies
  • Modeling Methodology and Applications
  • Statistical Models and Statistical Methods in Risk Management

Data and Data Science

  • Principles of Data science
  • Programming, logical reasoning, mathematics and statistics
  • Data Engineering versus Data Science
  • Time series comparison
  • Neural Networks
  • Steps to Machine Learning

Review of Data Mining Terminology and Principles

  • Principles of Math Refresher
  • Concepts of linear algebra
  • Probability and statistics
  • Algorithms
  • Automation and iterative processes
  • Scalability
  • Ensemble modeling
  • Framing
  • Generalization
  • Machine Learning methods
  • Classification
  • Training and Training Set
  • Validation
  • Representation
  • Regularization
  • Logistic Regressions
  • Neutral Nets
  • Multi class Neutral Nets
  • Embeddings
  • Basic Algebra and Calculus
  • Basic Python
  • Chain rule
  • Concept of a derivative
  • Gradient or slope
  • Linear algebra
  • Logarithms, and logarithmic equations
  • Matrix multiplication
  • Mean, median, outliers and standard deviation
  • Partial derivatives
  • Sigmoid function
  • Statistics
  • Tanh
  • Tensor and tensor rank
  • Trigonometry
  • Variables, coefficients, and functions

The Basics of Data Mining

  • Data Mining vs. Machine Learning
  • What is Machine Learning?
  • Emergence and applications of Artificial Intelligence and Machine Learning
  • Basics of Artificial Intelligence
  • Basics of Machine Learning
  • Basics of Data Mining
  • Data Mining versus Machine Learning versus Data Science
  • Data Mining and patterns
  • Why is machine learning important?
  • Creating good machine learning systems

What is Data Mining?

  • Data Mining: Classification Schemes
  • Decisions in data mining
  • Databases to be mined
  • Knowledge to be discovered
  • Techniques utilized
  • Applications adapted

Data Mining Tasks

  • Descriptive data mining
  • Predictive data mining
  • Databases to be mined
  • Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.
  • Knowledge to be mined
  • Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.
  • Multiple/integrated functions and mining at multiple levels

Data Mining Techniques

  • Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
  • Applications adapted
  • Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.
  • Prediction Tasks
  • Use some variables to predict unknown or future values of other variables
  • Description Tasks
  • Find human-interpretable patterns that describe the data.
  • Common data mining tasks
  • Classification [Predictive]
  • Clustering [Descriptive]
  • Association Rule Discovery [Descriptive]
  • Sequential Pattern Discovery [Descriptive]
  • Regression [Predictive]
  • Deviation Detection [Predictive]

Data Mining and Machine Learning Techniques, Tools and Algorithms

  • Supervised, unsupervised, semi supervised and reinforcement learning
  • Basic tools and ideas in Machine Learning
  • Supervised Machine Learning problems and solutions
  • Supervised Machine Learning tasks subgroups: regression and classification
  • Unsupervised Machine Learning
  • Unsupervised tasks and generative modelling
  • Reinforcement Learning, Hybrids and Beyond
  • Data preparation capabilities
  • Techniques of Machine Learning
  • Polynomial regression
  • Linear regression
  • Random forest
  • Decision tree regression
  • Gradient descent and regularization
  • Classification
  • Logistic regression
  • K-nearest neighbors
  • Support vector machines
  • Naive Bayes
  • Kernel support vector machines
  • Decision tree classifier
  • Random forest classifier
  • Clustering algorithms
  • K-means clustering
  • Bias and variance trade-off
  • Representation learning
  • Data Preprocessing
  • Data preparation
  • Feature engineering and scaling
  • Data and Datasets
  • Dimensionality reduction

Applied Artificial Intelligence (AI) and Machine Learning

  • Machine Learning prediction with models
  • Artificial Intelligence behaving and reasoning
  • Applications of Machine Learning
  • Machine Learning algorithms
  • Models
  • Techniques
  • Statistics and Math
  • Algorithms
  • Programming
  • Patterns and Prediction
  • Intelligent Behavior
  • Statistics quantifies numbers
  • Machine learning generalizing information from large data sets
  • Principles to detect and extrapolate patterns
  • Machine Learning System Analysis and Design
  • Support Vector Machines

Popular Data Mining and Machine Learning Methods

  • Supervised learning and unsupervised learning
  • Supervised learning algorithms and labeled data
  • Trained using labeled examples
  • Classification, regression, prediction and gradient boosting
  • Supervised learning and patterns
  • Predicting the values of the label on additional unlabeled data
  • Using historical data to predict likely future events
  • Unsupervised learning and unlabeled data
  • Unsupervised learning against data that has no historical labels
  • Semi supervised learning
  • Using both labeled and unlabeled data for training
  • Classification, regression and prediction
  • Reinforcement learning
  • Robotics, gaming and navigation
  • Discovery through trial and error
  • The agent (the learner or decision maker)
  • The environment (everything the agent interacts with)
  • Actions (what the agent can do)

Learning Applied

  • Application of Supervised versus Unsupervised Learning
  • Case Study: credit card transactions as fraudulent charges
  • Self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition
  • Face recognition

Principal Component Analysis

  • Anomaly detection
  • Deep learning
  • Neural networks
  • Learning with deep neural networks
  • Deep neural networks and hidden layers and multiple types of hierarchies
  • Deep learning as a type of machine learning
  • Regularization
  • Machine learning models need to generalize well to new examples that the model has not seen in practice.
  • Tools to prevent models from overfitting the training data.

Principles of Supervised Algorithms

  • Machine Learning algorithms mind map
  • What is supervised machine learning?
  • How does it relate to unsupervised machine learning?
  • Classification and regression supervised learning problems
  • Clustering and association unsupervised learning problems
  • Algorithms used for supervised and unsupervised problems
  • Supervised Machine Learning as a majority of practical machine learning
  • Supervised learning problems grouping into regression and classification problems
  • Principles of “Classification”
  • Principles of “Regression”
  • Popular examples of supervised machine learning algorithms
  • Linear regression for regression problems
  • Random forest for classification and regression problems
  • Support vector machines for classification problems

Principles of Unsupervised Algorithms

  • The goal for unsupervised learning
  • Modeling the underlying structure or distribution in the data
  • Ways to learn more about the data
  • Algorithms to discover and present the interesting structure in the data
  • Unsupervised learning problems grouping into clustering and association problems
  • Principles of “Clustering”
  • Ways to discover the inherent groupings in the data
  • Principles of “Association”
  • Ways to discover rules that describe large portions of your data
  • Examples of unsupervised learning algorithms
  • K-means for clustering problems
  • Apriori algorithm for association rule learning problems
  • Semi-Supervised Machine Learning
  • Unlabeled data and a mixture of supervised and unsupervised techniques
  • Collecting and storing unlabeled data

Regression Applied to Machines Learning

  • Linear Regression with One Variable
  • Application of linear regression
  • Method for learning
  • Linear Algebra Review
  • Refresher on linear algebra concepts
  • Models with multiple variables
  • Linear Regression with Multiple Variables
  • Implement the learning algorithms in practice
  • Logistic Regression
  • Logistic regression is a method for classifying data into discrete outcomes
  • Logistic regression to classify a credit card transaction as fraud or not fraud

Principles of Neural Networks

  • Neural Networks Representation
  • Principles behind neural networks and models
  • Neural Networks Learning
  • Backpropagation algorithm
  • Learn parameters for a neural network.
  • Implementing your own neural network for credit card fraud
  • Advice for Applying Machine Learning
  • Best practices for applying machine learning in practice
  • Best ways to evaluate performance of the learned models

Large Scale Data Mining and Machine Learning

  • Real-world case studies
  • Interactive visualizations of algorithms in action
  • Pattern Recognition
  • Accuracy
  • Case Study: Marketing Campaign
  • Working with Regression
  • Prediction
  • Classification
  • Logistic Regression
  • Unsupervised Learning with Clustering

Introduction to Deep Learning

  • Principles of Deep Learning
  • Artificial Neural Networks
  • TensorFlow
  • Learning complicated patterns in large amounts of data
  • Identifying objects in images and words in sounds
  • Automatic language translation
  • Medical diagnoses

Applying Data Mining and Machine Learning

  • Applying machine learning to IoT
  • Machine learning can be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things. This article explores the topic.
  • Financial services
  • DoD
  • Government
  • Health care
  • Marketing and sales
  • Oil and gas
  • Renewable Energy
  • Transportation

Overview of Algorithms

  • Associations and sequence discovery
  • Bayesian networks
  • Decision trees
  • Expectation maximization
  • Gaussian mixture models
  • Gradient boosting and bagging
  • Kernel density estimation
  • K-means clustering
  • Local search optimization techniques
  • Multivariate adaptive regression splines
  • Nearest-neighbor mapping
  • Neural networks
  • Principal component analysis
  • Random forests
  • Self-organizing maps
  • Sequential covering rule building
  • Singular value decomposition
  • Support vector machines

Overview of Tools and Processes

  • Comprehensive data quality and management
  • GUIs for building models and process flows
  • Interactive data exploration
  • Visualization of model results
  • Comparisons of different machine learning models
  • Identify the best machine learning models
  • Automated ensemble model evaluation
  • Repeatable and reliable results
  • Integrated, end-to-end platforms to automate data-to-decision process
  • Exploratory Data Analysis with R
  • Loading, querying and manipulating data in R
  • Cleaning raw data for modeling
  • Reducing dimensions with Principal Component Analysis
  • Identifying outliers in data
  • Working with Unstructured Data
  • Mining unstructured data
  • Building and evaluating association rules
  • Constructing recommendation engines
  • Machine learning with neural networks

Case Study and Workshops

  • Data mining skills, tools and techniques in analytics, statistics and programming
  • Self-driving cars
  • Unmanned Aerial Vehicles (UAV)
  • Six core stages of the data mining process applied
  • Anomaly detection, dependency modelling, clustering, classification, regression and report generation
  • Data mining tools
  • Spark, R and Hadoop as well as programming languages like Java and Python
  • Probabilistic and statistical models
  • Python for Data Science

Data Mining TONEX Boot Camp

TONEX Data Mining Boot Camps are intensive learning experiences that cover the essential elements of your chose subject. Boot camps are ideal for busy professionals who want to stay current in their fields but have limited time to be away from the office.

Data Mining boot camp includes:

  • Workshops
  • Experienced instructors including senior technology leaders, project managers, technical authors, engineers, educators, consultants, course developers, and CTOs.
  • Real life examples and practices.
  • Small class size.
  • Personalized instructor mentoring.
  • Pre-training discussions
  • Ongoing post-training support via e-mail, phone and WebEx.

 

Who Should Attend

IT Professionals, Analysts, Project managers and Developers

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