In this course you will go through completed Client Projects by MFH.

1. Client Projects
2. Client Projects Architectures
3. Project Documentation / Runbooks
4. Step by Step Lab procedures to simulate client projects.

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Live Sessions will be conducted on Microsoft Teams.

Artificial Intelligence (AI) Curriculum

 1. Foundations of AI
History and Evolution of AI
Definitions and Philosophical Underpinnings
Ethics in AI

2. Mathematics for AI
Linear Algebra
Probability and Statistics
Calculus
Optimization Techniques

 3. Programming and Software
Python Programming
Data Structures and Algorithms
Software Engineering Principles
Version Control (e.g., Git)

 4.Machine Learning
Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
Neural Networks
Unsupervised Learning
Clustering (K-means, Hierarchical)
Dimensionality Reduction (PCA, t-SNE)
Reinforcement Learning
Markov Decision Processes (MDPs)
Q-Learning
Deep Reinforcement Learning

5. Deep Learning
Neural Network Architectures
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
Generative Adversarial Networks (GANs)
Transformers
Training Deep Networks
Backpropagation
Gradient Descent
Hyperparameter Tuning
Regularization Techniques (Dropout, Batch Normalization)

6. Natural Language Processing (NLP)
Text Preprocessing
Word Embeddings (Word2Vec, GloVe, BERT)
Sequence Models
Machine Translation
Sentiment Analysis

7. Computer Vision
Image Preprocessing
Object Detection
Image Classification
Image Segmentation

8. AI Frameworks and Tools
TensorFlow
PyTorch
Keras
Scikit-Learn

9. Data Science and Big Data
Data Wrangling and Preprocessing
Exploratory Data Analysis
Data Visualization
Big Data Technologies (Hadoop, Spark)

10.Specialized Topics
Robotics
AI in Healthcare
AI in Finance
AI in Autonomous Systems

11. AI Ethics and Policy
Bias and Fairness in AI
Privacy Issues
AI and the Law
Societal Impacts of AI

12. Practical Applications and Projects
Real-world Case Studies
AI Competitions (e.g., Kaggle)
Capstone Projects

By covering these topics, you’ll gain a comprehensive understanding of AI and be equipped to work on a wide range of AI problems and applications.