About Course

Python is the go-to programming language for deep learning and computer vision, offering simplicity, flexibility, and a rich ecosystem from basic to advanced.

List of Python Topics for Deep Learning and Computer Vision
Python Basics
Python Syntax and Variables
Data Types (strings, lists, tuples, dictionaries, sets)
Control Flow (if-else, loops)
Functions and Lambda Expressions
File Handling (read, write, manage datasets)
Numerical and Data Libraries
NumPy (arrays, broadcasting, matrix operations)
Pandas (data manipulation, metadata management)
Data Visualization
Matplotlib (basic plots, image visualizations)
Seaborn (advanced statistical visualizations)
Image Processing
Pillow (image loading, resizing, cropping)
OpenCV (image filtering, transformations, video processing)
Machine Learning Basics
scikit-learn (basic ML models, data preprocessing)
Deep Learning Frameworks
TensorFlow/Keras (defining and training models, transfer learning)
PyTorch (dynamic computation graphs, custom models)
PyTorch’s torchvision (pre-trained models, dataset management)
Dataset Handling
os and glob (managing file paths)
Image Libraries (loading, saving, preprocessing images)
Data Augmentation and Preprocessing
Albumentations (advanced augmentation)
torchvision.transforms (image preprocessing pipeline)
Specialized Libraries for Computer Vision
OpenCV (advanced image processing, face and feature detection)
TensorFlow Hub and PyTorch Hub (access pre-trained models)
Optimization and Model Training
Callbacks (early stopping, learning rate adjustments)
Multiprocessing for Data Loading
Model Evaluation and Metrics
Advanced Topics
Object Detection Frameworks (YOLO, Faster R-CNN)
Image Segmentation Frameworks (UNet, Mask R-CNN)
Vision Transformers (ViTs)
Generative Models (GANs, Autoencoders)

Course Content

Python Basics
Python Syntax and Variables Data Types (strings, lists, tuples, dictionaries, sets) Control Flow (if-else, loops) Functions and Lambda Expressions File Handling (read, write, manage datasets)

Numerical and Data Libraries
NumPy (arrays, broadcasting, matrix operations) Pandas (data manipulation, metadata management)

Data Visualization
Matplotlib (basic plots, image visualizations) Seaborn (advanced statistical visualizations)

Image Processing
Pillow (image loading, resizing, cropping) OpenCV (image filtering, transformations, video processing)

Machine Learning Basics
scikit-learn (basic ML models, data preprocessing)

Deep Learning Frameworks
ensorFlow/Keras (defining and training models, transfer learning) PyTorch (dynamic computation graphs, custom models) PyTorch’s torchvision (pre-trained models, dataset management)

Dataset Handling
os and glob (managing file paths) Image Libraries (loading, saving, preprocessing images)

Data Augmentation and Preprocessing
Albumentations (advanced augmentation) torchvision.transforms (image preprocessing pipeline)

Specialized Libraries for Computer Vision
OpenCV (advanced image processing, face and feature detection) TensorFlow Hub and PyTorch Hub (access pre-trained models)

Optimization and Model Training
Callbacks (early stopping, learning rate adjustments) Multiprocessing for Data Loading Model Evaluation and Metrics

Advanced Topics
Object Detection Frameworks (YOLO, Faster R-CNN) Image Segmentation Frameworks (UNet, Mask R-CNN) Vision Transformers (ViTs) Generative Models (GANs, Autoencoders)

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Pyhton for machine/Deep Learning

About Course

What Will You Learn?

Course Content

Python Basics
Python Syntax and Variables Data Types (strings, lists, tuples, dictionaries, sets) Control Flow (if-else, loops) Functions and Lambda Expressions File Handling (read, write, manage datasets)

Numerical and Data Libraries
NumPy (arrays, broadcasting, matrix operations) Pandas (data manipulation, metadata management)

Data Visualization
Matplotlib (basic plots, image visualizations) Seaborn (advanced statistical visualizations)

Image Processing
Pillow (image loading, resizing, cropping) OpenCV (image filtering, transformations, video processing)

Machine Learning Basics
scikit-learn (basic ML models, data preprocessing)

Deep Learning Frameworks
ensorFlow/Keras (defining and training models, transfer learning) PyTorch (dynamic computation graphs, custom models) PyTorch’s torchvision (pre-trained models, dataset management)

Dataset Handling
os and glob (managing file paths) Image Libraries (loading, saving, preprocessing images)

Data Augmentation and Preprocessing
Albumentations (advanced augmentation) torchvision.transforms (image preprocessing pipeline)

Specialized Libraries for Computer Vision
OpenCV (advanced image processing, face and feature detection) TensorFlow Hub and PyTorch Hub (access pre-trained models)

Optimization and Model Training
Callbacks (early stopping, learning rate adjustments) Multiprocessing for Data Loading Model Evaluation and Metrics

Advanced Topics
Object Detection Frameworks (YOLO, Faster R-CNN) Image Segmentation Frameworks (UNet, Mask R-CNN) Vision Transformers (ViTs) Generative Models (GANs, Autoencoders)

About Course

What Will You Learn?

Course Content

Python Basics Python Syntax and Variables Data Types (strings, lists, tuples, dictionaries, sets) Control Flow (if-else, loops) Functions and Lambda Expressions File Handling (read, write, manage datasets)

Numerical and Data Libraries NumPy (arrays, broadcasting, matrix operations) Pandas (data manipulation, metadata management)

Data Visualization Matplotlib (basic plots, image visualizations) Seaborn (advanced statistical visualizations)

Image Processing Pillow (image loading, resizing, cropping) OpenCV (image filtering, transformations, video processing)

Machine Learning Basics scikit-learn (basic ML models, data preprocessing)

Deep Learning Frameworks ensorFlow/Keras (defining and training models, transfer learning) PyTorch (dynamic computation graphs, custom models) PyTorch’s torchvision (pre-trained models, dataset management)

Dataset Handling os and glob (managing file paths) Image Libraries (loading, saving, preprocessing images)

Data Augmentation and Preprocessing Albumentations (advanced augmentation) torchvision.transforms (image preprocessing pipeline)

Specialized Libraries for Computer Vision OpenCV (advanced image processing, face and feature detection) TensorFlow Hub and PyTorch Hub (access pre-trained models)

Optimization and Model Training Callbacks (early stopping, learning rate adjustments) Multiprocessing for Data Loading Model Evaluation and Metrics

Advanced Topics Object Detection Frameworks (YOLO, Faster R-CNN) Image Segmentation Frameworks (UNet, Mask R-CNN) Vision Transformers (ViTs) Generative Models (GANs, Autoencoders)

Student Ratings & Reviews

Python Basics
Python Syntax and Variables Data Types (strings, lists, tuples, dictionaries, sets) Control Flow (if-else, loops) Functions and Lambda Expressions File Handling (read, write, manage datasets)

Numerical and Data Libraries
NumPy (arrays, broadcasting, matrix operations) Pandas (data manipulation, metadata management)

Data Visualization
Matplotlib (basic plots, image visualizations) Seaborn (advanced statistical visualizations)

Image Processing
Pillow (image loading, resizing, cropping) OpenCV (image filtering, transformations, video processing)

Machine Learning Basics
scikit-learn (basic ML models, data preprocessing)

Deep Learning Frameworks
ensorFlow/Keras (defining and training models, transfer learning) PyTorch (dynamic computation graphs, custom models) PyTorch’s torchvision (pre-trained models, dataset management)

Dataset Handling
os and glob (managing file paths) Image Libraries (loading, saving, preprocessing images)

Data Augmentation and Preprocessing
Albumentations (advanced augmentation) torchvision.transforms (image preprocessing pipeline)

Specialized Libraries for Computer Vision
OpenCV (advanced image processing, face and feature detection) TensorFlow Hub and PyTorch Hub (access pre-trained models)

Optimization and Model Training
Callbacks (early stopping, learning rate adjustments) Multiprocessing for Data Loading Model Evaluation and Metrics

Advanced Topics
Object Detection Frameworks (YOLO, Faster R-CNN) Image Segmentation Frameworks (UNet, Mask R-CNN) Vision Transformers (ViTs) Generative Models (GANs, Autoencoders)