Computer Vision + NLP Guide Bundle

Computer vision vs. NLP?

Why not both!

The AI development is accelerating as you're reading this. In January 2023, it was already too late to get started.

Luckily, the next suitable time is now, so let me take you together to the journey of AI and computer vision and NLP.

I combined two of my most popular mini roadmaps into one because AI is becoming capable of processing both computer vision and NLP simultaneously.

That's why it's important to stay on top of your game in both cases!

Here's what's included

NLP Roadmap Summary

1. Foundations of NLP
   • Core concepts: syntax, semantics, pragmatics.
2. Text Preprocessing
   • Cleaning and transforming raw text data.
3. Statistical NLP Techniques
   • BoW, TF-IDF, N-grams, language models.
4. Machine Learning for NLP
   • Supervised learning, text classification, clustering.
5. Deep Learning for NLP
   • RNNs, LSTMs, transformers, attention mechanisms.
6. Transfer Learning and Fine-Tuning
   • Adapting pre-trained models for specific tasks.
7. Specialized NLP Applications
   • Domain-specific uses in healthcare, finance, legal.
8. Building and Deploying Large-Scale NLP Systems
   • Scalability, cloud deployment, real-world integration.
9. Ethics and Fairness in NLP
   • Addressing bias, fairness, ethical considerations.
10. Portfolio Building and Interview Preparation
    • Showcasing skills, preparing for NLP-specific interviews.
11. Bonus 1: Learn Large Language Models
    • A chapter dedicated to mastering LLMs and Generative AI
12. Bonus 2: NLU - Natural Language Understanding
    • Dive into the concept of NLU and combine it with NLP

What's Included in Computer Vision Roadmap

1. Fundamentals of Computer Vision:
   • Introduction to computer vision and understanding images and pixels.
   • Basics of image processing with OpenCV and PIL.
   • Essential mathematical concepts for computer vision.
2. Advanced Image Processing Techniques:
   • Feature extraction and image segmentation.
   • Hands-on practice with traditional and deep learning-based methods.
3. Deep Learning for Computer Vision:
   • Detailed study of CNNs and advanced architectures like U-Net, SegNet, YOLO, and Mask R-CNN.
   • Training and deploying deep learning models for classification, detection, and segmentation tasks.
4. 3D Vision and Generative Models:
   • Explore 3D vision techniques such as stereo vision, structure from motion, and depth estimation.
   • Dive into generative models like VAEs and GANs for image generation and enhancement.
5. Practical Applications and Projects:
   • Tips for identifying real-world problems and planning impactful projects.
   • Guidance on choosing the right algorithms and overcoming common challenges like overfitting.
   • Building a strong portfolio with well-documented, diverse projects.
6. Free resources

·         Handpicked best free courses and tutorials for computer vision

Get access to over 100 project ideas for your portfolio, guides and 40+ resources for learning!