Machine Learning Training Bootcamp

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Machine Learning Training Bootcamp
Call Us Today 1-972-665-9786 https//www.tonex.c
om/training-courses/machine-learning-training-boot
camp/
TAKE THIS COURSE
Since 1993, Tonex has specialized in providing
industry-leading training, courses, seminars,
workshops, and consulting services. Fortune 500
companies certified.
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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.
Machine Learning Training Bootcamp
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https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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Learn differences and similarities between
Machine Learning, Artificial Intelligence, Deep
Learning, Data Mining and Data Warehouse. Artifici
al 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
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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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
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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
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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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
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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
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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
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rning-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

• 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

Machine Learning Training Bootcamp
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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
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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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)

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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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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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rning-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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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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
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https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-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
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rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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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

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 Training Bootcamp
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https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
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Machine Learning Training Bootcamp
Machine Learning Training Bootcamp
TAKE THIS COURSE
https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
36
Machine Learning Training Bootcamp
Call Us Today 1-972-665-9786 https//www.tonex.c
om/training-courses/machine-learning-training-boot
camp/
TAKE THIS COURSE
Since 1993, Tonex has specialized in providing
industry-leading training, courses, seminars,
workshops, and consulting services. Fortune 500
companies certified.
37
Machine Learning Training Bootcamp
https//www.tonex.com/training-courses/machine-lea
rning-training-bootcamp/
TAKE THIS COURSE
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