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Machine Learning Algorithms

  • Course Code: Artificial Intelligence - Machine Learning Algorithms
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 6 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to explore and master the most important algorithms for solving complex machine learning problems

Course Snapshot 

  • Duration: 6 days 
  • Skill-level: Foundation-level Machine Learning Algorithms skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to explore and master the most important algorithms for solving complex machine learning problems  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine Learning Algorithms helps you harness the real power of machine learning algorithms to implement smarter ways of meeting today’s overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Kera’s – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning course teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this course, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. 

Working in a hands-on learning environment, led by our Machine Learning Algorithms expert instructor, students will learn about and explore: 

  • Updated to include new algorithms and techniques 
  • Code updated to Python 3.8 & TensorFlow 2.x 
  • New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to learn and use basic machine learning algorithms and concepts. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Machine Learning Model Fundamentals 
  • Machine Learning Model Fundamentals 
  • Models and data 
  • Characteristics of a machine learning model 
  1. Loss Functions and Regularization 
  • Loss Functions and Regularization 
  • Defining loss and cost functions 
  • Regularization 
  1. Introduction to Semi-Supervised Learning 
  • Introduction to Semi-Supervised Learning 
  • Semi-supervised scenario 
  • Generative Gaussian Mixture 
  • Self-Training 
  • Co-Training 
  1. Advanced Semi-Supervised Classification 
  • Advanced Semi-Supervised Classification 
  • Contrastive Pessimistic Likelihood Estimation 
  • Semi-supervised Support Vector Machines (S3VM) 
  • Transductive Support Vector Machines (TSVM) 
  1. Graph-Based Semi-Supervised Learning 
  • Graph-Based Semi-Supervised Learning 
  • Label propagation 
  • Example of label propagation 
  • Label spreading 
  • Label propagation based on Markov random walks 
  • Manifold learning 
  1. Clustering and Unsupervised Models 
  • Clustering and Unsupervised Models 
  • K-nearest neighbors 
  • K-means 
  • Evaluation metrics 
  1. Advanced Clustering and Unsupervised Models 
  • Advanced Clustering and Unsupervised Models 
  • Fuzzy C-means 
  • Spectral clustering 
  • DBSCAN 
  1. Clustering and Unsupervised Models for Marketing 
  • Clustering and Unsupervised Models for Marketing 
  • Biclustering 
  • Introduction to Market Basket Analysis with the Apriori Algorithm 
  1. Generalized Linear Models and Regression 
  • Generalized Linear Models and Regression 
  • GLMs 
  • Other regression techniques 
  1. Introduction to Time-Series Analysis 
  • Introduction to Time-Series Analysis 
  • Time-series 
  • Introduction to linear models for time-series 
  1. Bayesian Networks and Hidden Markov Models 
  • Bayesian Networks and Hidden Markov Models 
  • Conditional probabilities and Bayes’ theorem 
  • Bayesian networks 
  • Hidden Markov Models 
  1. The EM Algorithm 
  • The EM Algorithm 
  • MLE and MAP Learning 
  • EM Algorithm 
  • Gaussian Mixture 
  1. Component Analysis and Dimensionality Reduction 
  • Component Analysis and Dimensionality Reduction 
  • Factor Analysis 
  • Principal Component Analysis 
  • Independent Component Analysis 
  • Addendum to Hidden Markov Models 
  1. Hebbian Learning 
  • Hebbian Learning 
  • Hebb’s rule 
  • Sanger’s network 
  • Rubner-Tavan’s network 
  • Self-organizing maps 
  1. Fundamentals of Ensemble Learning 
  • Fundamentals of Ensemble Learning 
  • Ensemble learning fundamentals 
  • Random forests 
  • AdaBoost 
  1. Advanced Boosting Algorithms 
  • Advanced Boosting Algorithms 
  • Gradient boosting 
  • Ensembles of voting classifiers 
  • Ensemble learning as model selection 
  1. 17Modeling Neural Networks 
  • Modeling Neural Networks 
  • The basic artificial neuron 
  • The perceptron 
  • Multilayer Perceptrons (MLPs) 
  • The back-propagation algorithm 
  1. Optimizing Neural Networks 
  • Optimizing Neural Networks 
  • Optimization algorithms 
  • Regularization and Dropout 
  • Batch normalization 
  1. Deep Convolutional Networks 
  • Deep Convolutional Networks 
  • Deep convolutional networks 
  • Convolutional operators 
  • Pooling layers 
  • Example of a deep convolutional network with TensorFlow and Keras 
  1. Recurrent Neural Networks 
  • Recurrent Neural Networks 
  • Recurrent networks 
  • Long Short-Term Memory (LSTM) 
  • Transfer learning 
  1. Autoencoders 
  • Autoencoders 
  • Autoencoders 
  • Denoising autoencoders 
  • Sparse autoencoders 
  • Variational autoencoders 
  1. Introduction to Generative Adversarial Networks 
  • Introduction to Generative Adversarial Networks 
  • Adversarial training 
  • Deep Convolutional GANs 
  • Wasserstein GAN 
  1. Deep Belief Networks 
  • Deep Belief Networks 
  • Introduction to Markov random fields 
  • Restricted Boltzmann Machines 
  • Deep Belief Networks 
  1. Introduction to Reinforcement Learning 
  • Introduction to Reinforcement Learning 
  • Fundamental concepts of RL 
  • Policy iteration 
  • Value iteration 
  • The TD(0) algorithm 
  1. Advanced Policy Estimation Algorithms 
  • Advanced Policy Estimation Algorithms 
  • TD(λ) algorithm 
  • SARSA algorithm 
  • Q-learning 
  • Direct policy search through policy gradient 
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