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Machine Learning Training Bootcamp

Machine Learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems.

Machine Learning algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so, however, ML is mainly being driven by new computing technologies due to its rapid evolution.

Machine Learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community.

Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive Machine Learning boom.

Organizations such as Facebook, Google and Uber make Machine Learning a central part of their operations. In fact, machine learning has become a significant competitive differentiator for many companies.

Machine Learning has been categorized by how an algorithm learns to become more accurate in its predictions. Engineers claim there are four basic approaches:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

There are many new innovations and Machine Learning Trends that are likely coming to the forefront in 2021.

These include:

  • Automated Machine Learning
  • Machine Learning in Cybersecurity
  • AI Engineering
  • Rise of Ethics

Already, there are many applications of Machine Learning in the industry such as its integration with the Internet of Things and a more prevalent use in industries such as finance, factories and medicine.

According to a Salesforce Research study, 83% of IT leaders believe that Machine Learning and Artificial Intelligence is changing the customer engagement experience for the better. This clearly shows that ML as a technology is only increasing in popularity.

Machine Learning Training Bootcamp Course by Tonex

This is a course for Data Scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.

Machine Learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning.

Machine Learning helps to automate the data analysis process by enabling computers, machines and IoT to learn and adapt through experience applied to specific tasks without explicit programming.

Attendees learn, comprehend and master ideas on machine learning concepts, key principles, and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification  and prediction.

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, 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 AI and Machine Learning can automatically process and analyze huge volumes of complex data, process logic, tasks and creating innovative automated technologies such as recommendation engines, facial recognition, robotics, financial losses from stock market and bonds, fraud protection, self-driving autonomous cars, industrial automation, military systems, UAVs and future applications.

Learning Objectives

After completing this course, the participants will:

  • 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, adapt and optimize
  • Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns
  • 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 and recommender systems (RS) used to help users find new items or services, such as books, music, transportation, people and jobs based on information about the user or the recommended item
  • 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 of machine learning
  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering and recommendation systems

Course Agenda and Topics (Selective and Customizable) 

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 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

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

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 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 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.

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

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

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 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 Machine Learning

  • Applying machine learning to IoT
  • Financial services
  • DoD
  • Government
  • Healthcare
  • Marketing and sales
  • Oil and gas
  • Renewable Energy
  • Transportation

Hands-on Activities

  • Working with Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Applying Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Applying Clustering: K-Means, Hierarchical Clustering
  • Applying Association Rule Learning: Apriori, Eclat
  • Applying Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Applying Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Working with production-ready Python frameworks
  • Working with Scikit-Learn
  • Working with TensorFlow, complex library for distributed numerical computation using data flow graph
  • Train and run a large neural networks efficiently by distributing the computations across potentially thousands of multi-GPU servers

Pre-Reading and Preparation Material (Sent before the class)

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
  • 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

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

Machine Learning Training Bootcamp

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