Deep Learning with Python
Deep Learning is revolutionizing industries by enabling machines to learn and make decisions like humans. This comprehensive course covers the fundamentals to advanced concepts of deep learning, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative AI. Participants will gain hands-on experience with Python-based deep learning frameworks such as TensorFlow, Keras, and PyTorch. The course is designed with real-world industry projects to equip learners with practical AI and deep learning skills. Upon completion, students will be prepared for careers in AI, computer vision, NLP, and machine learning engineering.
Software You Will Learn
- Python
- TensorFlow & Keras
- PyTorch
- OpenCV
- NLTK & spaCy
- Google Colab & Jupyter Notebook
Ideal For
- AI enthusiasts & beginners looking to start with deep learning
- IT professionals & engineers transitioning into AI and machine learning
- Business analysts & data scientists aiming to specialize in deep learning
- Researchers & students exploring AI-powered innovation
- Software developers looking to integrate deep learning into applications
Career Opportunity
- Deep Learning Engineer
- AI Research Scientist
- Machine Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- AI Product Developer
Course Module
Introduction to Deep Learning & Neural Networks
- Introduction to AI & Deep Learning
- Python Basics for Deep Learning
- Linear Algebra & Probability for Deep Learning
- Understanding Neural Networks & Activation Functions
- Forward & Backward Propagation in Deep Learning
- Gradient Descent & Optimization Algorithms (SGD, Adam, RMSprop)
- Building Your First Neural Network with TensorFlow & Keras
- Regularization Techniques (Dropout, L1/L2, Batch Normalization)
- Hyperparameter Tuning & Model Optimization
- Project: Training a Basic Neural Network for Classification
Convolutional Neural Networks (CNNs) & Computer Vision
- Introduction to CNNs & Image Processing
- Convolutional Layers, Pooling, and Feature Extraction
- Building CNN Models from Scratch in TensorFlow/Keras
- Transfer Learning with Pretrained Models (VGG, ResNet, EfficientNet)
- Object Detection using YOLO & Faster R-CNN
- Face Recognition & Image Segmentation with Deep Learning
- Data Augmentation & Improving Model Performance
- Deploying Deep Learning Models for Image Classification
- AI in Healthcare: Medical Image Analysis with CNNs
- Project: Creating an AI-Based Image Recognition System
Recurrent Neural Networks (RNNs) & Natural Language Processing (NLP)
- Introduction to NLP & Sequence Data
- Understanding RNNs & Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRU) & Advanced RNN Models
- Word Embeddings (Word2Vec, GloVe) & Text Representation
- Text Classification with Deep Learning
- Transformer Models & Attention Mechanisms
- BERT & GPT Models for NLP Tasks
- Speech Recognition & Text-to-Speech Systems
- Chatbot Development with Deep Learning
- Project: AI-Powered Sentiment Analysis System
Generative AI, Reinforcement Learning & Career Readiness
- Introduction to Generative Adversarial Networks (GANs)
- Building GANs for Image & Video Generation
- Variational Autoencoders (VAEs) for Deep Learning Applications
- Deep Reinforcement Learning with Q-Learning & Policy Gradient
- Deep Learning in Business Applications (Finance, Healthcare, Gaming, etc.)
- Ethics & Bias in AI & Deep Learning
- Final Capstone Project: End-to-End Deep Learning System
- Resume Building & Deep Learning Job Market Trends
- Mock Interview & Technical Test Preparation
- Industry Networking & Career Guidance Session
Course Study Plan
- Month 1: Neural Networks & Fundamentals of Deep Learning
- Month 2: CNNs & Computer Vision Applications
- Month 3: RNNs, NLP, & Transformers
- Month 4: GANs, Reinforcement Learning, & Career Preparation
Projects
- 3+ Industry Projects (Computer Vision, NLP, Generative AI)
- 10+ Hands-On Practice Labs
Materials
- 100+ Hours of recorded content for self-paced learning
Course FAQ
Do I need prior knowledge of AI?
Basic knowledge of Python is recommended, but we cover AI fundamentals from scratch.
Will I receive a certificate?
Yes, you will receive a Brilliant Brains Valley Deep Learning Certification upon completion.
Does this course prepare me for deep learning jobs?
Yes, this course covers practical skills needed for AI & deep learning roles with real-world projects.
Will I get practical experience?
Yes, you will work on real-world datasets, deep learning models, and AI applications.
What career support is available?
Resume-building, interview prep, job placement assistance, and industry networking.
৳ 400.00
- Students: 0
- Lessons: 40
- Durations: 4
- Category: Technical Learning
