Advanced Machine Learning with Python Training Course

This program explores the application of AI—with a specific focus on Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are transforming digital strategy, automation, and decision-making processes within enterprise operations. The course explores core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics like communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led live training in Italy (available online or on-site) 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.

An Artificial Neural Network serves as a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This course on Applied AI from Scratch in Python provides programmers and data analysts with the essential skills needed to develop machine learning solutions entirely from the ground up using Python. It covers the fundamental concepts of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside an exploration of advanced neural network architectures. Participants will examine effective techniques for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical machine learning models, assessing the constraints of various algorithms, and completing applied projects designed to solve real-world challenges.

This instructor-led, live training in Italy (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.

This instructor-led, live training in Italy (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Italy (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

Upon completion of this training, participants will be able to:

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make informed decisions through interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. By leveraging reward-based learning, DRL enables artificial agents to master strategies, refine policies, and execute autonomous decisions through trial and error.

This instructor-led live training, available both online and onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The course focuses on building intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• For customized versions of this course (e.g., utilizing PyTorch instead of TensorFlow), please reach out to us to arrange your specific requirements.

This instructor-led, live training in Italy (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.

This instructor-led, live training in Italy (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

Upon completion 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.

This instructor-led, live training in Italy (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale them 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.

This course provides a conceptual foundation in neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, aiding you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN.
• comprehend the structure and deployment mechanisms of TensorFlow.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitoring.
• be able to implement advanced production-grade tasks such as model training, graph construction, and logging.

This instructor-led, live training in Italy (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.