Artificial Intelligence Mastery: Build and Deploy Scalable AI Models with Deep Learning and Python
Course Overview This comprehensive course is designed to help you master the concepts of Artificial Intelligence (AI) and Deep Learning (DL) using Python. You will learn how to build and deploy scalable AI models, and gain hands-on experience with real-world applications. Upon completion, you will receive a certificate issued by The Art of Service.
Course Curriculum Module 1: Introduction to Artificial Intelligence
- What is Artificial Intelligence?
- History of AI
- Types of AI: Narrow, General, and Superintelligence
- AI Applications: Computer Vision, Natural Language Processing, and Robotics
Module 2: Python for AI
- Introduction to Python
- Python Basics: Variables, Data Types, Loops, and Functions
- Python Libraries for AI: NumPy, Pandas, and Matplotlib
- Hands-on Project: Building a Simple AI Model using Python
Module 3: Deep Learning Fundamentals
- What is Deep Learning?
- Types of Neural Networks: Feedforward, Recurrent, and Convolutional
- Activation Functions: Sigmoid, ReLU, and Softmax
- Backpropagation and Optimization Algorithms
Module 4: Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Convolutional Layers: Filters, Stride, and Padding
- Pooling Layers: Max Pooling and Average Pooling
- Hands-on Project: Building a CNN for Image Classification
Module 5: Recurrent Neural Networks (RNNs)
- Introduction to RNNs
- Simple RNNs: Architecture and Training
- Long Short-Term Memory (LSTM) Networks
- Hands-on Project: Building an RNN for Time Series Forecasting
Module 6: Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing: Tokenization, Stopwords, and Stemming
- Word Embeddings: Word2Vec and GloVe
- Hands-on Project: Building an NLP Model for Sentiment Analysis
Module 7: Transfer Learning and Fine-Tuning
- Introduction to Transfer Learning
- Pre-trained Models: VGG16, ResNet50, and InceptionV3
- Fine-Tuning: Adapting Pre-trained Models to Your Dataset
- Hands-on Project: Fine-Tuning a Pre-trained Model for Image Classification
Module 8: Deploying AI Models
- Introduction to Model Deployment
- Model Serving: TensorFlow Serving and AWS SageMaker
- Containerization: Docker and Kubernetes
- Hands-on Project: Deploying an AI Model using TensorFlow Serving
Module 9: Real-World Applications of AI
- Computer Vision: Object Detection, Segmentation, and Generation
- NLP: Chatbots, Sentiment Analysis, and Text Summarization
- Robotics: Autonomous Vehicles and Human-Robot Interaction
- Hands-on Project: Building a Real-World AI Application
Module 10: Final Project and Course Wrap-Up
- Final Project: Building a Scalable AI Model
- Course Wrap-Up: Review and Q&A
- Certificate of Completion: Issued by The Art of Service
Course Features - Interactive and Engaging: Quizzes, Games, and Hands-on Projects
- Comprehensive: Covers AI, DL, and Python Fundamentals
- Personalized: Tailored to Your Learning Style and Goals
- Up-to-date: Latest AI and DL Techniques and Tools
- Practical: Real-World Applications and Case Studies
- Expert Instructors: Industry Practitioners with Real-World Experience
- Certification: Receive a Certificate upon Completion
- Flexible Learning: Self-Paced and Mobile-Accessible
- Community-Driven: Discussion Forums and Live Support
- Actionable Insights: Hands-on Projects and Real-World Examples
- Bite-Sized Lessons: Easy to Digest and Retain
- Lifetime Access: Learn at Your Own Pace
- Gamification: Track Your Progress and Compete with Peers
Enroll Now and Start Your AI Mastery Journey!
Module 1: Introduction to Artificial Intelligence
- What is Artificial Intelligence?
- History of AI
- Types of AI: Narrow, General, and Superintelligence
- AI Applications: Computer Vision, Natural Language Processing, and Robotics
Module 2: Python for AI
- Introduction to Python
- Python Basics: Variables, Data Types, Loops, and Functions
- Python Libraries for AI: NumPy, Pandas, and Matplotlib
- Hands-on Project: Building a Simple AI Model using Python
Module 3: Deep Learning Fundamentals
- What is Deep Learning?
- Types of Neural Networks: Feedforward, Recurrent, and Convolutional
- Activation Functions: Sigmoid, ReLU, and Softmax
- Backpropagation and Optimization Algorithms
Module 4: Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Convolutional Layers: Filters, Stride, and Padding
- Pooling Layers: Max Pooling and Average Pooling
- Hands-on Project: Building a CNN for Image Classification
Module 5: Recurrent Neural Networks (RNNs)
- Introduction to RNNs
- Simple RNNs: Architecture and Training
- Long Short-Term Memory (LSTM) Networks
- Hands-on Project: Building an RNN for Time Series Forecasting
Module 6: Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing: Tokenization, Stopwords, and Stemming
- Word Embeddings: Word2Vec and GloVe
- Hands-on Project: Building an NLP Model for Sentiment Analysis
Module 7: Transfer Learning and Fine-Tuning
- Introduction to Transfer Learning
- Pre-trained Models: VGG16, ResNet50, and InceptionV3
- Fine-Tuning: Adapting Pre-trained Models to Your Dataset
- Hands-on Project: Fine-Tuning a Pre-trained Model for Image Classification
Module 8: Deploying AI Models
- Introduction to Model Deployment
- Model Serving: TensorFlow Serving and AWS SageMaker
- Containerization: Docker and Kubernetes
- Hands-on Project: Deploying an AI Model using TensorFlow Serving
Module 9: Real-World Applications of AI
- Computer Vision: Object Detection, Segmentation, and Generation
- NLP: Chatbots, Sentiment Analysis, and Text Summarization
- Robotics: Autonomous Vehicles and Human-Robot Interaction
- Hands-on Project: Building a Real-World AI Application
Module 10: Final Project and Course Wrap-Up
- Final Project: Building a Scalable AI Model
- Course Wrap-Up: Review and Q&A
- Certificate of Completion: Issued by The Art of Service