Deep Learning with Python: Beginner to Advanced Professional Program

Master Deep Learning with Python through a structured journey from neural networks and mathematical foundations to CNNs, RNNs, LSTMs, Transformers, Computer Vision, NLP, and Generative AI. Gain hands-on experience with TensorFlow, Keras, and PyTorch, build real-world AI projects, deploy production-ready models, and develop industry-ready skills for AI, Deep Learning, and Machine Learning careers.

Self-Paced

90 hours

25 modules

Recommended Course Audience

Data Science Aspirants Machine Learning Engineers Data Analysts Transitioning to AI Software Developers Interested in AI Artificial Intelligence Enthusiasts Computer Science Students Engineering Graduates Data Scientists Seeking Deep Learning Skills Research Scholars and Academicians Working Professionals Looking to Build AI Solutions

Pre-Required Skills

Basic Python (mandatory),
Machine Learning fundamentals (recommended but not mandatory)

Course Highlights

25 modules

Understand the foundations of AI, Machine Learning, and Deep Learning, explore neural network evolution, industry applications, career opportunities, and the complete deep learning workflow used in modern AI systems.

Learn Python programming essentials, data structures, functions, object-oriented programming, NumPy, Pandas, and data visualization techniques required for building and understanding deep learning applications effectively.

Build strong mathematical foundations including linear algebra, vectors, matrices, probability, statistics, derivatives, gradients, optimization, and calculus concepts essential for understanding deep learning algorithms.

Learn perceptrons, neurons, activation functions, forward propagation, backpropagation, loss functions, gradient descent, and the core concepts behind training neural network models successfully.

Set up professional deep learning environments using Google Colab, Jupyter Notebook, TensorFlow, Keras, PyTorch, GPUs, and project structures for efficient model development.

Develop deep learning models using TensorFlow and Keras, understand tensors, APIs, model training, evaluation, saving, loading, and deployment workflows for production-ready solutions.

Prepare datasets through cleaning, transformation, scaling, augmentation, feature engineering, batch processing, and efficient data pipelines to improve model performance and reliability.

Design and implement ANN models for classification and regression tasks, understand network architecture, hidden layers, hyperparameters, evaluation metrics, and practical business applications.

Explore optimization algorithms including SGD, Adam, RMSProp, momentum, regularization, dropout, batch normalization, and learning rate scheduling to improve model accuracy.

Learn techniques to optimize deep learning models through hyperparameter tuning, experiment tracking, model selection, performance analysis, and automated optimization strategies.

Understand image data, image processing techniques, computer vision concepts, OpenCV basics, image transformations, augmentation methods, and dataset preparation for vision applications.

Build CNN architectures for image classification, understand convolutions, pooling, filters, feature extraction, and develop powerful image recognition systems using real-world datasets.

Leverage pretrained models such as VGG16, ResNet, MobileNet, and EfficientNet to accelerate development and improve performance on custom computer vision tasks.

Learn object detection concepts, bounding boxes, YOLO architectures, custom dataset training, evaluation techniques, and real-time object detection application development.

Implement image segmentation models including U-Net, understand semantic and instance segmentation, and solve pixel-level classification problems in healthcare and industry.

Understand sequential data processing using RNNs, tackle time-dependent problems, learn recurrent architectures, and build sequence prediction and forecasting applications.

Master LSTM architectures for handling long-term dependencies, sequence prediction, sentiment analysis, forecasting, and advanced natural language processing tasks.

Apply deep learning techniques to NLP tasks including text preprocessing, embeddings, classification, sentiment analysis, language understanding, and intelligent text analytics.

Explore attention mechanisms, self-attention, transformer architectures, BERT, GPT, and modern deep learning techniques powering state-of-the-art language models.

Learn dimensionality reduction, feature extraction, anomaly detection, latent space representation, denoising techniques, and advanced unsupervised learning approaches.

Understand foundation models, Large Language Models, prompt engineering, Hugging Face ecosystem, generative AI workflows, and emerging AI technologies shaping industries.

Deploy trained models using APIs, Docker, monitoring systems, automation pipelines, and MLOps best practices for scalable and maintainable AI solutions.

Apply acquired skills through comprehensive real-world projects covering computer vision, NLP, forecasting, object detection, segmentation, and Generative AI applications.

Prepare for deep learning careers through portfolio development, GitHub projects, interview preparation, case studies, industry best practices, and professional growth strategies.

Deep Learning with Python: Beginner to Advanced Professional Program

$250.00

This course is coming soon. Enrollment is not yet open.

For any support or technical enquiries, please reach out to info@solixacademy.com

Level : Advanced

Modules : 25

Duration : 90 hours