Advanced Machine Learning with Python Training Course

This course explores the application of AI, with a particular focus on Machine Learning and Deep Learning, within the automotive industry. It guides participants in identifying technologies that can be (potentially) applied across various scenarios in a vehicle, ranging from basic automation and image recognition to autonomous decision-making processes.

An exploration of artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

This instructor-led, live training in Nigeria (online or onsite) is designed for 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 employed in the creation of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. Neural Networks are frequently utilized in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a specific subset of Machine Learning.

This course empowers developers and data analysts with the essential skills to construct machine learning solutions from the ground up using Python. It explores the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning techniques like clustering and anomaly detection, alongside advanced neural network structures. Participants will learn proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program guides professionals in deploying functional ML models, assessing algorithmic constraints, and delivering applied projects that address real-world challenges.

This instructor-led, live training in Nigeria (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code is 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 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 Nigeria (online or onsite) is designed for advanced professionals seeking to expand their computer vision knowledge and explore TensorFlow’s capabilities for developing advanced vision models using Google Colab.

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

• Construct and train Convolutional Neural Networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable, 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 Nigeria (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.

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning architectures, empowering agents to make informed decisions by interacting with their surroundings. It serves as the foundation for numerous modern AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading, and personalized recommendation engines. Through reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and make independent choices via trial and error.

This instructor-led live training, available both online and onsite, is designed for intermediate developers and data scientists keen on mastering and applying Deep Reinforcement Learning techniques to create intelligent agents capable of autonomous decision-making in complex settings.

Upon completing this training, participants will be equipped to:

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Code essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL in practical applications ranging from gaming and robotics to decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

• Engaging lectures coupled with guided discussions.
• Practical exercises and real-world implementations.
• Live coding sessions and project-based learning.

Customization Options

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

This instructor-led, live training in Nigeria (online or onsite) is tailored for 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 Nigeria (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 Nigeria (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.

This course starts by providing you with a solid conceptual foundation in neural networks, as well as an overview of machine learning algorithms and deep learning (covering both algorithms and their practical applications).

The first part (40%) of this training concentrates on fundamentals, yet it will equip you to select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

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

Audience

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

Upon completion of this course, delegates will:

• possess a strong understanding of Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN)
• understand TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, setting up production environments, designing architectures, and configuring systems
• be able to assess code quality, perform debugging, and implement monitoring
• be able to implement advanced production tasks such as training models, building computational graphs, and handling logging

This instructor-led live training in Nigeria (online or onsite) targets advanced professionals interested in exploring state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

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

• Comprehend the challenges associated with explainability in deep learning.
• Deploy advanced XAI techniques for neural networks.
• Decode decisions made by deep learning models.
• Assess the trade-offs between model performance and transparency.