Artificial Intelligence Basic to Advanced Course
Course Name - Artificial Intelligence Basic to Advanced Course
This comprehensive AI course covers fundamental to advanced topics. You’ll start with the basics of AI, its history, types, and ethical considerations.
Course Overview
This comprehensive AI course covers fundamental to advanced topics. You’ll start with the basics of AI, its history, types, and ethical considerations. The course delves into machine learning and data preparation, exploring supervised and unsupervised learning algorithms, model evaluation, and preprocessing techniques. You’ll learn neural networks, deep learning fundamentals, and advanced AI topics like NLP, reinforcement learning, GANs, and AI in robotics. The course culminates in a capstone project, providing hands-on experience with real-world AI applications.
What You'll Learn?
You’ll gain a solid understanding of AI fundamentals, machine learning algorithms, data preparation, and model evaluation. You’ll explore neural networks, deep learning techniques, NLP, reinforcement learning, GANs, and AI in robotics. Practical exercises and a capstone project provide hands-on experience with real-world AI applications.
Duration:
110 Hours
Requirements:
Nothing
Pre-requisite:
No pre-requisite
Batch Details
Batch Start Date
Batch Timing
Batch End Date
Batch Days
Curriculum
1. What is AI?
- Definition and History of AI
- Types of AI (Narrow AI vs. General AI)
- Applications and Impact of AI
2. AI vs. Machine Learning vs. Deep Learning:
- Differences and Relationships
- Overview of Machine Learning and Deep Learning
3. Ethics in AI:
- Ethical Considerations
- Bias and Fairness in AI
- Privacy and Security Issues
1. Introduction to Machine Learning
- Overview and Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Key Concepts (Features, Labels, Training, Testing)
2. Supervised Learning Algorithms:
- Regression (Linear Regression, Polynomial Regression)
- Classification (Logistic Regression, Decision Trees, k-Nearest Neighbors)
- Model Evaluation (Confusion Matrix, Accuracy, Precision, Recall, F1 Score)
3. Unsupervised Learning Algorithms:
- Clustering (K-means, Hierarchical Clustering)
- Dimensionality Reduction (Principal Component Analysis)
4. Model Selection and Validation:
- Cross-Validation Techniques
- Hyperparameter Tuning
1. Data Collection and Integration:
- Sources of Data
- Data Acquisition Techniques
2. Data Cleaning and Transformation:
- Handling Missing Values
- Data Normalization and Standardization
- Feature Engineering
3. Exploratory Data Analysis:
- Data Visualization Techniques
- Summary Statistics and Correlations
1. Basics of Neural Networks:
- Neurons and Layers
- Activation Functions (Sigmoid, ReLU, Tanh)
2. Feedforward Neural Networks:
- Structure and Training
- Backpropagation Algorithm
3. Advanced Neural Network Concepts:
- Overfitting and Regularization (Dropout, L2 Regularization)
- Optimization Techniques (Gradient Descent, Adam)
1. Introduction to Deep Learning:
- Overview and Applications
- Comparison with Traditional Machine Learning
2. Convolutional Neural Networks (CNNs):
- Architecture and Components (Convolutional Layers, Pooling Layers)
- Applications in Image Processing
3. Recurrent Neural Networks (RNNs):
- Basics of RNNs
- Long Short-Term Memory (LSTM) Networks
- Applications in Sequence Data
4. Transfer Learning:
- Concept of Transfer Learning
Pre-trained Models and Fine-Tuning
1. Natural Language Processing (NLP):
- Text Processing Techniques (Tokenization, Lemmatization)
- Text Representation (Bag of Words, TF-IDF, Word Embeddings)
- NLP Models (Sentiment Analysis, Named Entity Recognition)
2. Reinforcement Learning:
- Basics of Reinforcement Learning
- Key Concepts (Agent, Environment, Reward, Policy)
- Q-Learning and Deep Q-Networks (DQN)
3. Generative Adversarial Networks (GANs):
- Introduction to GANs
- Architecture (Generator, Discriminator)
- Applications and Examples
4. AI in Robotics:
- Basics of Robotics
- Integration of AI in Robotics
- Examples and Applications
1. AI Project Lifecycle:
- Problem Definition and Goal Setting
- Data Collection and Preparation
- Model Building, Evaluation, and Deployment
2. AI Tools and Libraries:
- Overview of Libraries (TensorFlow, Keras, PyTorch, scikit-learn)
- Practical Hands-on with Tools
3. Case Studies and Applications:
- Real-World AI Applications (Healthcare, Finance, Autonomous Vehicles)
- Analysis of Successful AI Projects
1. Project Planning:
- Defining Project Objectives and Scope
- Data Collection and Preparation
2. Implementation:
- Model Selection and Training
- Evaluation and Optimization
3. Presentation and Reporting:
- Documenting Findings
- Creating a Final Report and Presentation
This syllabus is designed to provide a structured learning path from basic concepts to advanced applications in AI, incorporating practical exercises and real-world projects.
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