AI & Deep Learning
Course Overview
The AI & Deep Learning course by CourseDeal is designed to equip learners with the knowledge and practical skills needed to build intelligent systems and neural network-based solutions. You’ll start with core artificial intelligence concepts, including machine learning, neural networks, and deep learning architectures. The course emphasizes hands-on experience using Python and popular deep learning frameworks to develop AI models for image recognition, natural language processing, and predictive analytics. Learners will gain expertise in training, optimizing, and deploying neural networks for real-world applications. By the end of this course, participants will be able to implement AI solutions, understand advanced neural architectures, and pursue careers in AI engineering, data science, or research-driven AI projects.
Key Highlights
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Learn core AI concepts and deep learning architectures
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Hands-on experience with neural networks for real-world applications
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Train and deploy AI models for image, text, and sequence data
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Explore cutting-edge frameworks and optimization techniques
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Prepare for careers in AI, deep learning, and neural network engineering
Tools & Technologies Covered
- Python
- TensorFlow
- Keras
- PyTorch
- NumPy
- Pandas
- Matplotlib
- Jupyter Notebook
- OpenCV
- NLP libraries
Curriculum
- 5 Sections
- 0 Lessons
- 32 Hours
- Module 1: Introduction to Artificial IntelligenceThis module introduces artificial intelligence, its history, applications, and impact across industries. You’ll learn the difference between AI, machine learning, and deep learning, as well as the types of AI — narrow, general, and superintelligent. The module also covers problem-solving techniques, search algorithms, and decision-making processes. Through practical exercises, learners gain foundational knowledge of AI concepts and begin exploring how AI systems are structured and implemented in real-world scenarios.0
- Module 2: Fundamentals of Neural NetworksIn this module, you’ll dive into neural network basics, including perceptrons, activation functions, forward and backward propagation, and loss functions. You’ll learn how to construct multi-layer perceptrons and train them using gradient descent. Practical exercises involve implementing neural networks in Python with TensorFlow or Keras. By the end of this module, learners will understand how neural networks process information, recognize patterns, and form the basis for advanced deep learning architectures.0
- Module 3: Deep Learning ArchitecturesThis module focuses on advanced deep learning architectures such as Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) for sequential data, and Long Short-Term Memory (LSTM) networks. You’ll learn how to implement, train, and optimize these models for applications like image classification, object detection, and time-series forecasting. Hands-on exercises provide practical experience in designing complex networks for real-world problems.0
- Module 4: AI in Natural Language Processing (NLP)This module introduces NLP techniques and their applications in AI systems. You’ll explore text processing, tokenization, word embeddings, and sequence modeling using RNNs and Transformers. Practical exercises involve sentiment analysis, chatbot development, and text classification using Python libraries. By completing this module, learners will be able to develop AI models that understand and process human language efficiently.0
- Module 5: Optimization, Regularization, and Model EvaluationThis module teaches techniques to optimize and evaluate deep learning models. Topics include learning rate tuning, regularization methods like dropout, batch normalization, early stopping, and evaluation metrics such as accuracy, precision, recall, and F1-score. Learners practice improving model performance, preventing overfitting, and interpreting results for reliable AI applications. These skills are critical for building robust and scalable AI solutions.0
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