Machine Learning Algorithms in Julia Training Course
This instructor-led, live training (online or onsite) is aimed at people that already have a background in data science and statistics.
Format of the Course
- Interactive lecture and discussion.
- Exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Machine Learning Algorithms in Julia
Introductory concepts
- Supervised & unsupervised learning
- Cross validation and model selection
- Bias/variance tradeoff
Linear & logistic regression
(NaiveBayes & GLM)
- Introductory concepts
- Fitting linear regression models
- Model diagnostics
- Naive Bayes
- Fitting a logistic regression model
- Model disgnostics
- Model selection methods
Distances
- What is a distance?
- Euclidean
- Cityblock
- Cosine
- Correlation
- Mahalanobis
- Hamming
- MAD
- RMS
- Mean squared deviation
Dimensionality reduction
- Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent CA
- Multidimensional scaling
Altered regression methods
- Basic concepts of regularization
- Ridge regression
- Lasso regression
- Principal component regression (PCR)
Clustering
- K-means
- K-medoids
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Standard machine learning models
(NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, LIBSVM packages)
- Gradient boosting concepts
- K nearest neighbours (KNN)
- Decision tree models
- Random forest models
- XGboost
- EvoTrees
- Support vector machines (SVM)
Artificial neural networks
(Flux package)
- Stochastic gradient descent & strategies
- Multilayer perceptrons forward feed & back propagation
- Regularization
- Recurrence neural networks (RNN)
- Convolutional neural networks (Convnets)
- Autoencoders
- Hyperparameters
Requirements
This course is intended for people that already have a background in data science and statistics.
Need help picking the right course?
Machine Learning Algorithms in Julia Training Course - Booking
Machine Learning Algorithms in Julia Training Course - Enquiry
Machine Learning Algorithms in Julia - Consultancy Enquiry
Upcoming Courses
Related Courses
DataRobot
7 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.
By the end of this training, participants will be able to:
- Load datasets in DataRobot to analyze, assess, and quality check data.
- Build and train models to identify important variables and meet prediction targets.
- Interpret models to create valuable insights that are useful in making business decisions.
- Monitor and manage models to maintain an optimized prediction performance.
H2O AutoML
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
By the end of this training, participants will be able to:
- Automate the machine learning workflow.
- Automatically train and tune many machine learning models within a specified time range.
- Train stacked ensembles to arrive at highly predictive ensemble models.
AutoML with Auto-sklearn
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
- Automate the process of training highly efficient machine learning models.
- Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
- Use the power of machine learning to solve real-world business problems.
AutoML with Auto-Keras
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
- Automate the process of training highly efficient machine learning models.
- Automatically search for the best parameters for deep learning models.
- Build highly accurate machine learning models.
- Use the power of machine learning to solve real-world business problems.
Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation
21 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.
By the end of this training, participants will be able to:
- Understand advanced deep learning architectures and techniques for text-to-image generation.
- Implement complex models and optimizations for high-quality image synthesis.
- Optimize performance and scalability for large datasets and complex models.
- Tune hyperparameters for better model performance and generalization.
- Integrate Stable Diffusion with other deep learning frameworks and tools
Introduction to Stable Diffusion for Text-to-Image Generation
21 HoursThis instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.
By the end of this training, participants will be able to:
- Understand the principles of Stable Diffusion and how it works for image generation.
- Build and train Stable Diffusion models for image generation tasks.
- Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
- Optimize the performance and stability of Stable Diffusion models.
AlphaFold
7 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
Edge AI with TensorFlow Lite
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
- Develop and optimize AI models using TensorFlow Lite.
- Deploy TensorFlow Lite models on various edge devices.
- Utilize tools and techniques for model conversion and optimization.
- Implement practical Edge AI applications using TensorFlow Lite.
TensorFlow Lite for Embedded Linux
21 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
- Deploy a deep learning model on an embedded device running Linux.
TensorFlow Lite for Android
21 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
TensorFlow Lite for iOS
21 HoursThis instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
Tensorflow Lite for Microcontrollers
21 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
Deep Learning Neural Networks with Chainer
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
Distributed Deep Learning with Horovod
7 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start running deep learning trainings.
- Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
- Scale deep learning training with Horovod to run on multiple GPUs.
Accelerating Deep Learning with FPGA and OpenVINO
35 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.
By the end of this training, participants will be able to:
- Install the OpenVINO toolkit.
- Accelerate a computer vision application using an FPGA.
- Execute different CNN layers on the FPGA.
- Scale the application across multiple nodes in a Kubernetes cluster.