Artificial Intelligence Course

– Basic Level: 4-6 weeks

– Intermediate Level: 6-8 weeks

– Advanced Level: 8-12 weeks

 

Basic Level

 

1. Introduction to AI

– Definition and History of AI

– AI vs. Machine Learning vs. Deep Learning

– Applications and Examples of AI

 

2. Mathematical Foundations

– Linear Algebra (Vectors, Matrices)

– Calculus (Derivatives, Integrals)

– Probability and Statistics (Distributions, Hypothesis Testing)

 

3. Programming Basics

– Introduction to Python

– Libraries: NumPy, Pandas, Matplotlib

 

4. Data Handling

– Data Collection and Cleaning

– Exploratory Data Analysis (EDA)

– Basic Data Visualization

 

5. Fundamental Machine Learning Concepts

– Supervised vs. Unsupervised Learning

– Basic Algorithms: Linear Regression, Logistic Regression

– Model Evaluation: Accuracy, Precision, Recall, F1 Score

 

Intermediate Level

 

1. Advanced Machine Learning Algorithms

– Decision Trees, Random Forests, Gradient Boosting

– Support Vector Machines (SVM)

– k-Nearest Neighbors (k-NN)

 

2. Feature Engineering

– Feature Selection and Extraction

– Dimensionality Reduction (PCA, t-SNE)

 

3. Introduction to Neural Networks

– Perceptrons and Multilayer Perceptrons (MLP)

– Activation Functions, Loss Functions

– Backpropagation and Optimization

 

4. Model Evaluation and Tuning

– Cross-Validation

– Hyperparameter Tuning

– Model Selection and Ensemble Methods

 

5. Introduction to Deep Learning

– Fundamentals of Deep Learning

– Neural Network Architectures: CNNs, RNNs

 

Advanced Level

 

1. Advanced Neural Networks

– Convolutional Neural Networks (CNNs)

– Recurrent Neural Networks (RNNs), LSTMs, GRUs

– Generative Adversarial Networks (GANs)

 

2. Natural Language Processing (NLP)

– Text Preprocessing and Tokenization

– Word Embeddings (Word2Vec, GloVe)

– Transformers and Attention Mechanisms

 

3. Reinforcement Learning

– Basic Concepts and Algorithms (Q-Learning, Policy Gradients)

– Applications and Case Studies

 

4. AI Ethics and Fairness

– Bias in AI

– Ethical Considerations and Implications

– Fairness and Transparency in AI Systems

 

5. Deployment and Production

– Model Deployment Techniques

– Scaling and Optimization

– Monitoring and Maintenance

 

Basic Requirements

 

1. Basic Knowledge: Familiarity with high school mathematics and basic programming concepts.
2. Programming Skills: Proficiency in Python is highly recommended, as it is commonly used in AI and machine learning.
3. Mathematical Skills: Understanding of basic algebra and statistics.