Machine Learning and Computer Vision Engineering Training Workshop by Tonex
Machine Learning and Computer Vision Engineering Workshop is a comprehensive training program designed to equip participants with the essential knowledge and skills required to excel in the rapidly evolving fields of machine learning and computer vision engineering.
This workshop offers a deep dive into the theoretical foundations, practical applications, and cutting-edge techniques that drive innovation in these fields. Participants will gain hands-on experience with industry-standard tools and technologies, enabling them to develop advanced machine learning and computer vision solutions.
This Machine Learning and Computer Vision Engineering Workshop is a dynamic and hands-on training program that empowers participants to harness the potential of machine learning and computer vision in a practical and impactful way. Through this comprehensive workshop, attendees will acquire the knowledge and skills required to excel in these rapidly advancing fields and contribute to the development of innovative solutions.
Learning Objectives:
Upon completing this workshop, participants will be able to:
- Learn the fundamentals of machine learning and computer vision.
- Apply machine learning algorithms to solve real-world problems.
- Develop computer vision applications for image and video analysis.
- Implement deep learning techniques for enhanced accuracy.
- Utilize popular libraries and frameworks for machine learning and computer vision.
- Collaborate on and deliver impactful machine learning and computer vision projects.
Target Audience:
This workshop is ideal for:
- Data Scientists and Analysts looking to enhance their machine learning skills.
- Software Engineers interested in diving into the field of computer vision.
- Computer Vision Engineers seeking to expand their knowledge and expertise.
- Researchers exploring cutting-edge machine learning and computer vision techniques.
- Technical Managers and Team Leads overseeing machine learning and computer vision projects.
- Anyone interested in leveraging AI and computer vision in their work or research.
Course Outline:
Introduction to Machine Learning and Computer Vision
- Understanding the basics of machine learning
- Exploring the principles of computer vision
- Bridging the gap between ML and computer vision
- Overview of real-world applications
Machine Learning Fundamentals
- Supervised, unsupervised, and reinforcement learning
- Feature engineering and selection
- Model evaluation and performance metrics
- Hands-on exercises with popular ML algorithms
Computer Vision Essentials
- Image and video representation
- Image preprocessing and augmentation techniques
- Object detection and tracking
- Practical computer vision challenges
Deep Learning for Computer Vision
- Introduction to neural networks
- Convolutional Neural Networks (CNNs)
- Transfer learning and fine-tuning
- Advanced deep learning models
Data Preprocessing
- Data collection and cleaning
- Data exploration and visualization
- Feature selection and engineering
- Dealing with missing data and outliers
Supervised Learning
- Introduction to supervised learning
- Linear regression
- Logistic regression
- Model evaluation and metrics (MSE, MAE, RMSE, Accuracy, Precision, Recall, F1-score)
Decision Trees and Random Forests
- Decision tree fundamentals
- Ensemble methods and random forests
- Tree pruning and regularization
- Hyperparameter tuning
Support Vector Machines (SVM)
- Basics of SVM
- Linear and non-linear SVM
- Kernel functions
- SVM applications
K-Nearest Neighbors (KNN) and Naive Bayes
- K-Nearest Neighbors algorithm
- Naive Bayes for classification
- Text classification with Naive Bayes
- Model selection and comparison
Unsupervised Learning
- Introduction to unsupervised learning
- K-Means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
Dimensionality Reduction
- Curse of dimensionality
- Feature scaling and normalization
- Feature selection techniques
- Manifold learning (t-SNE, LLE)
Neural Networks and Deep Learning
- Introduction to artificial neural networks
- Feedforward neural networks
- Backpropagation and gradient descent
- Activation functions
Convolutional Neural Networks (CNN)
- Convolutional layers
- Pooling layers
- CNN architectures (LeNet, AlexNet, VGG, etc.)
- Image classification and object detection
Recurrent Neural Networks (RNN)
- Basics of sequential data
- RNN architecture
- Long Short-Term Memory (LSTM)
- Natural Language Processing (NLP) with RNNs
Model Deployment and Practical Applications
- Model deployment options (local, cloud, and edge)
- Introduction to TensorFlow and PyTorch
- Building and deploying a machine learning model
- Case studies and real-world applications
Tools and Frameworks
- Utilizing TensorFlow and PyTorch
- OpenCV for computer vision applications
- Jupyter notebooks for experimentation
- Version control with Git
Project Development and Deployment
- Collaborative project development
- Model deployment strategies
- Ethical considerations in machine learning and computer vision
- Presenting and communicating results