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Course Skill Level:

Foundational to Intermediate

Course Duration:

6 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience & basic IT & Linux skills seeking to use basic ML algorithms and concepts to solve complex ML problems

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who are intending to learn and use basic machine learning algorithms and concepts and to explore and master the most important algorithms for solving complex machine learning problems.
  • Foundation-level Machine Learning Algorithms skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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 Keras, 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.
In Machine Learning you will learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you will start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You will then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you are done working through these fun and informative projects, you will have a comprehensive machine learning skill set you can apply to practical on-the-job problems.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning Algorithms expert instructor, students will learn about and explore:
  • New algorithms and techniques
  • Updated Python 3.8 TensorFlow 2.x code
  • New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

Course breakdown / modules

  • Models and data
  • Characteristics of a machine learning model

  • Defining loss and cost functions
  • Regularization

  • Semi-supervised scenario
  • Generative Gaussian Mixture
  • Self-Training
  • Co-Training

  • Contrastive Pessimistic Likelihood Estimation
  • Semi-supervised Support Vector Machines (S3VM)
  • Transductive Support Vector Machines (TSVM)

  • Label propagation
  • Example of label propagation
  • Label spreading
  • Label propagation based on Markov random walks
  • Manifold learning

  • K-nearest neighbors
  • K-means
  • Evaluation metrics

  • Fuzzy C-means
  • Spectral clustering

  • Biclustering
  • Introduction to Market Basket Analysis with the Apriori Algorithm

  • GLMs
  • Other regression techniques

  • Time-series
  • Introduction to linear models for time-series

  • Conditional probabilities and Bayes’ theorem
  • Bayesian networks
  • Hidden Markov Models

  • MLE and MAP Learning
  • EM Algorithm
  • Gaussian Mixture

  • Factor Analysis
  • Principal Component Analysis
  • Independent Component Analysis
  • Addendum to Hidden Markov Models

  • Hebb rule
  • Sanger network
  • Rubner-Tavan network
  • Self-organizing maps

  • Ensemble learning fundamentals
  • Random forests
  • AdaBoost

  • Gradient boosting
  • Ensembles of voting classifiers
  • Ensemble learning as model selection

  • The basic artificial neuron
  • The perceptron
  • Multilayer Perceptrons (MLPs)
  • The back-propagation algorithm

  • Optimization algorithms
  • Regularization and Dropout
  • Batch normalization

  • Convolutional operators
  • Pooling layers
  • Example of a deep convolutional network with TensorFlow and Keras

  • Long Short-Term Memory (LSTM)
  • Transfer learning

  • Denoising autoencoders
  • Sparse autoencoders
  • Variational autoencoders

  • Adversarial training
  • Deep Convolutional GANs
  • Wasserstein GAN

  • Introduction to Markov random fields
  • Restricted Boltzmann Machines
  • Deep Belief Networks

  • Fundamental concepts of RL
  • Policy iteration
  • Value iteration
  • The TD(0) algorithm

  • TD(λ) algorithm
  • SARSA algorithm
  • Q-learning
  • Direct policy search through policy gradient