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 guides participants in identifying technologies suitable for various scenarios inside a vehicle, ranging from basic automation and image recognition to complex autonomous decision-making processes.

Delving into the basics of artificial intelligence demonstrates how smart technology is transforming digital strategy, automation, and decision-making processes throughout enterprise operations. This course covers fundamental concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning approaches, alongside communication, perception, and autonomous actions. It enables executives and architects to assess opportunities for AI-driven transformation, analyze emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Vietnam (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.

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

This course empowers developers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. It explores core concepts in supervised learning, including classification and regression, as well as unsupervised methods such as clustering and anomaly detection, alongside advanced neural network structures. Participants will study effective strategies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The curriculum supports professionals in deploying functional ML models, assessing algorithmic constraints, and executing applied projects designed to address real-world challenges.

This instructor-led, live training in Vietnam (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:

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

This instructor-led, live training in Vietnam (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in Vietnam (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through environmental interaction. This technology drives significant AI advancements, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make independent decisions via trial-and-error processes grounded in reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate developers and data scientists seeking to master and apply Deep Reinforcement Learning techniques for constructing intelligent agents capable of autonomous decision-making in complex settings.

Upon completing this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
• Develop core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL to practical scenarios like gaming, robotics, and decision optimization.
• Diagnose issues, visualize outcomes, and optimize training performance using contemporary tools.

Course Format

• Interactive lectures combined with guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a customized version of this course (for instance, focusing on PyTorch instead of TensorFlow), please contact us to arrange.

This instructor-led live training Vietnam (available online or on-site) is tailored for intermediate-level developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI initiatives.

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

• Comprehend the foundational aspects of TensorFlow Lite and its function in Edge AI.
• Build and optimize AI models utilizing TensorFlow Lite.
• Deploy TensorFlow Lite models onto various edge devices.
• Apply tools and techniques for model conversion and optimization.
• Create functional Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Vietnam (online or onsite) targets data scientists who aim to utilize TensorFlow for analyzing potential fraud data.

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

• Create a fraud detection model using Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This instructor-led live training in Vietnam (online or onsite) targets developers and data scientists who want to utilize Tensorflow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides a strong conceptual foundation in neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40% of the training) emphasizes foundational concepts, helping you select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part 2 (20% of the training) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40% of the training) focuses extensively on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to implement TensorFlow in their deep learning projects.

Upon completion of this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• grasp TensorFlow's architecture and deployment mechanisms
• be capable of managing installation, production environments, architectural tasks, and configurations
• evaluate code quality, perform debugging, and monitor system performance
• implement advanced production-level tasks such as training models, constructing graphs, and logging

This instructor-led, live training in Vietnam (online or onsite) is tailored for advanced professionals seeking to explore state-of-the-art XAI techniques for deep learning, focusing on the construction of interpretable AI systems.

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

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Analyze the trade-offs between model performance and transparency.