- Background Reading: Start with understanding the basic principles of machine learning and computer vision. Books like “Pattern Recognition and Machine Learning” by Christopher Bishop and “Computer Vision: Algorithms and Applications” by Richard Szeliski are great resources.
- Online Courses: Enroll in online courses on websites like Coursera, Udemy, and edX.
- “Machine Learning” by Andrew Ng on Coursera for machine learning basics.
- “Introduction to Computer Vision” from Georgia Tech on Udacity for computer vision basics.
- Programming Skills: Brush up your Python programming, and get comfortable with libraries like NumPy and Pandas.
- Machine Learning Libraries: Learn to use machine learning libraries such as scikit-learn, TensorFlow, and PyTorch.
- Computer Vision Libraries: Familiarize yourself with computer vision libraries like OpenCV for basic image processing, object detection, and feature extraction tasks.
- Deep Learning for Computer Vision: Understand the principles of Convolutional Neural Networks (CNNs) and get hands-on experience in implementing them using TensorFlow or PyTorch.
- Special Topics: Dive deeper into specific topics relevant to your project, like object detection models (YOLO, SSD), segmentation models (U-Net), or pose estimation models.
- Projects: Implement small projects or contribute to open-source projects to get hands-on experience.
- Stay Updated: Keep yourself updated by following relevant journals, blogs, or researchers.
- Join Forums and Networks: Participate in communities such as:
- StackOverflow: Use the ‘machine-learning’, ‘computer-vision’, ‘tensorflow’, ‘opencv’, etc., tags to ask and answer related questions.
- Reddit: Join subreddits like r/MachineLearning, r/computervision, r/learnmachinelearning, and r/deeplearning.
Starting any new venture is challenging, but with consistent learning and practice, you’ll certainly make progress. Good luck on your journey into computer vision and machine learning!
