Advanced Machine Learning with Python Training Course

This course provides an overview of Artificial Intelligence (with a focus on Machine Learning and Deep Learning) within the automotive industry. It guides participants in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities across enterprise operations. The course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to assess AI-driven transformation opportunities, evaluate emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Malaysia (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 serves as a computational data model employed in creating Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are frequently utilized in Machine Learning (ML) applications, which represent one implementation of AI, while Deep Learning constitutes a subset of ML.

Applied AI from Scratch in Python provides programmers and data analysts with the fundamental techniques needed to construct machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The content enables professionals to implement practical machine learning models, evaluate algorithm limitations, and execute applied projects designed for real-world problem solving.

This instructor-led live training in Malaysia (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code is easy to debug.

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

• Set up the necessary development environment to begin creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Execute examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Malaysia (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 Malaysia (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.

By the end 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 advanced deep learning architectures, empowering agents to make informed decisions through continuous interaction with their surroundings. This technology drives numerous contemporary AI innovations, including autonomous vehicles, robotics control systems, algorithmic trading platforms, and adaptive recommendation engines. Through reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and execute autonomous decisions via trial and error.

This live training session, led by an experienced instructor and available online or on-site, is designed for intermediate-level data scientists and developers eager to master and apply Deep Reinforcement Learning techniques. Participants will gain the skills needed to develop intelligent agents capable of autonomous decision-making in complex environments.

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

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

Course Format

• Engaging lectures combined with guided discussions.
• Practical exercises and hands-on implementation tasks.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a tailored version of this course (for instance, focusing on PyTorch instead of TensorFlow), please reach out to us for arrangement.

This guided, live training in Malaysia (available online or on-site) is designed for intermediate-level developers, data scientists, and AI practitioners who aim to utilise TensorFlow Lite for Edge AI applications.

By the conclusion 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 Malaysia (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.

This instructor-led, live training in Malaysia (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 foundational knowledge on neural networks, machine learning algorithms, and deep learning concepts and applications.

The first part (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

The second part (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The third part (40%) is heavily based on TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to utilise TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will be able to:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• perform installation, production environment setup, architecture tasks, and configuration
• assess code quality, debug, and monitor systems
• implement advanced production-level tasks such as training models, constructing graphs, and logging

This instructor-led, live training in Malaysia (online or onsite) is designed for advanced professionals who want to explore cutting-edge XAI techniques for deep learning models, with a strong emphasis 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.