Machine learning & artificial intelligence. Machine learning is playing an increasingly important role in computing and artificial intelligence. Suits any article on AI, algorithms, machine learning, quantum computing, artificial intelligence. Machine learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI). Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction. Learning Objectives: Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) List similarities and differences between AI, Machine Learning and Data Mining Learn how Artificial Intelligence uses data to offer solutions to existing problems Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn. Clarify how Data Mining can serve as foundation for AI and machine learning. List the various applications of machine learning and related algorithms Learn how to classify the types of learning such as supervised and unsupervised learning Implement supervised learning techniques such as linear and logistic regression Use unsupervised learning algorithms including deep learning, clustering , etc. Learn about classification data and Machine Learning models Select the best algorithms applied to Machine Learning Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering more... Course Agenda and Topics: The Basics of Machine Learning Machine Learning Techniques, Tools and Algorithms Data and Data Science Review of Terminology and Principles Applied Artificial Intelligence (AI) and Machine Learning Popular Machine Learning Methods Learning Applied to Machine Learning Principal Component Analysis Principles of Supervised Machine Learning Algorithms Principles of Unsupervised Machine Learning Regression Applied to Machines Learning Principles of Neural Networks Large Scale Machine Learning Introduction to Deep Learning Applying Machine Learning Overview of Algorithms Overview of Tools and Processes Call us today at +1-972-665-9786. Learn more about course audience, objectives, outlines, pricing. Visit our website links below. Machine Learning Training Bootcamp https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

1. Call Us Today: +1-972-665-9786
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
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Since 1993, Tonex has specialized in providing industry-leading training, courses, seminars,
workshops, and consulting services. Fortune 500 companies certified.
Machine Learning
Training Bootcamp

2. Since 1993, Tonex has specialized in providing industry-leading training,
courses, seminars, workshops, and consulting services.
Why Tonex ?
--Presenting highly customized learning solutions is what we do. For over 30
years TONEX has worked with organizations in improving their
understanding and capabilities in topics often with new development,
design, optimization, regulations and compliances that, frankly, can be
difficult to comprehend.
--Ratings tabulated from student feedback post-course evaluations show an
amazing 98 percent satisfaction score.
--Reasonably priced classes taught by the best trainers is the reason all kinds
of organizations from Fortune 500 companies to government’s most
important agencies return for updates in courses and hands-on workshops

3. Machine learning & artificial intelligence. Machine
learning is playing an increasingly important role
in computing and artificial intelligence. Suits any
article on AI, algorithms, machine learning,
quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day
technical training course that covers the
fundamentals of machine learning, a form and
application of artificial intelligence (AI).
Attendees will learn, comprehend and master
ideas on machine learning concepts, key
principles, techniques including: supervised and
unsupervised learning, mathematical and
heuristic aspects, modeling to develop algorithms,
prediction, linear regression, clustering,
classification, and prediction.
TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

4. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

5. TAKE THIS COURSE
Learn differences and similarities between Machine Learning, Artificial Intelligence, Deep
Learning, Data Mining and Data Warehouse. Artificial Intelligence uses models built by Machine
Learning to create intelligent behavior applied to businesses, marketing and sales, operations,
autonomous cars, games and industrial automation by prediction based on rules and using
programming languages and algorithms.
Machine learning based on artificial intelligence provides the ability to learn about newer data
sets without being programmed explicitly using methods of data analysis. Machine Learning takes
advantages of Data Mining techniques, statistics, other key principles and learning algorithms to
build models to predict future outcomes. Math and programming are the basis for many of the
machine learning algorithms. Using machine learning as a tool, the machine must automatically
learn the parameters of models from the data. Using larger datasets, better accuracy and
performance is achieved.
Machine learning and data mining can use the same key algorithms to discover patterns in your
data and dataset. In machine learning, the computers, machines and IoT devices must
automatically learn the parameters of models from the data using self-learning algorithms to
reveal insights and provide feedback in near real-time.
Machine Learning Training Bootcamp
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Machine learning, for example, can be used in proactive maintenance to continuously monitor
the performance of simple or complex industrial systems, applications and events. Using the
ability to learn and adapt, makes it the optimal choice for improvements in ongoing processes,
and to automatically predict and prevent failures.
Learn how Machine Learning can automatically process and analyze huge volumes of complex
data. Machine learning powers innovative automated technologies such as recommendation
engines, facial recognition, financial losses from stock market and bonds, fraud protection, self-
driving autonomous cars, robotics, industrial automation and future applications.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

7. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

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Learning Objectives
• Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
• List similarities and differences between AI, Machine Learning and Data Mining
• Learn how Artificial Intelligence uses data to offer solutions to existing problems
• Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
• Clarify how Data Mining can serve as foundation for AI and machine learning.
• List the various applications of machine learning and related algorithms
• Learn how to classify the types of learning such as supervised and unsupervised learning
• Implement supervised learning techniques such as linear and logistic regression
• Use unsupervised learning algorithms including deep learning, clustering , etc.
• Learn about classification data and Machine Learning models
• Select the best algorithms applied to Machine Learning
• Make accurate predictions and analysis to effectively solve potential problems
• List Machine Learning concepts, principles, algorithms, tools and applications
• Learn the concepts and operation of support neural networks, vector machines, kernel SVM,
naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest
neighbors, K-means and clustering
• Comprehend the theoretical concepts and how they relate to the practical aspects
• Be able to model a wide variety of robust machine learning algorithms
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

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Course Agenda and Topics:
• The Basics of Machine Learning
• Machine Learning Techniques, Tools and Algorithms
• Data and Data Science
• Review of Terminology and Principles
• Applied Artificial Intelligence (AI) and Machine Learning
• Popular Machine Learning Methods
• Learning Applied to Machine Learning
• Principal Component Analysis
• Principles of Supervised Machine Learning Algorithms
• Principles of Unsupervised Machine Learning
• Regression Applied to Machines Learning
• Principles of Neural Networks
• Large Scale Machine Learning
• Introduction to Deep Learning
• Applying Machine Learning
• Overview of Algorithms
• Overview of Tools and Processes
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

10. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
Training Bootcamp

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The Basics of 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
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

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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
Machine Learning Training Bootcamp
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• 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
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

14. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

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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
Machine Learning Training Bootcamp
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Review of Terminology and Principles:
• 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
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
• 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

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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
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

18. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
Training Bootcamp

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Popular 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)
Machine Learning Training Bootcamp
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Learning Applied to Machine Learning:
• 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.
Machine Learning Training Bootcamp
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21. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
Training Bootcamp

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Principles of Supervised Machine Learning 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
Machine Learning Training Bootcamp
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Principles of Unsupervised Machine Learning:
• 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
Machine Learning Training Bootcamp
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24. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
Training Bootcamp

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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
Machine Learning Training Bootcamp
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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
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

27. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

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Large Scale 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
Machine Learning Training Bootcamp
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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
Machine Learning Training Bootcamp
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Applying Machine Learning:
• Applying machine learning to IoT
• Financial services
• DoD
• Government
• Health care
• Marketing and sales
• Oil and gas
• Renewable Energy
• Transportation
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/

31. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
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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
Machine Learning Training Bootcamp
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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
• Constructing recommendation engines
• Machine learning with neural networks
Machine Learning Training Bootcamp
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34. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

35. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp

36. Call Us Today: +1-972-665-9786
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
TAKE THIS COURSE
Since 1993, Tonex has specialized in providing industry-leading training, courses, seminars,
workshops, and consulting services. Fortune 500 companies certified.
Machine Learning
Training Bootcamp

37. TAKE THIS COURSE
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning Training Bootcamp

38. Contact Tonex Training Expert
Dallas, TX:
1400 Preston Rd., Suite 400
Plano, Texas 75093
Tel: +1-972-665-9786
Washington, DC:
1025 Connecticut Ave, NW Suite 1000
Washington, DC 20036
Tel: +1-202-327-5404
International: +1-214-762-6673
Fax: +1-972-692-7492
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