AI & ML

The AI & Machine Learning program is designed to provide a deep understanding of artificial intelligence and machine learning principles. This course covers everything from fundamental concepts and programming foundations to advanced techniques and real-world applications.

Whether you’re a beginner or looking to enhance your existing skills, this program offers a comprehensive learning experience, equipping you with the tools and knowledge needed to excel in the rapidly evolving field of AI and ML.

Course Content:

• Module 1: Overview of AI and Machine Learning:
• This module introduces the basic concepts of AI and ML, a brief history of the field, and some fundamental distinctions. It explains the basic concepts and takes a closer look at how the various industries apply AI in real-life settings. Building on that knowledge will be the foundation for more advanced topics.
• Module 2: Programming Fundamentals of AI & ML
• The course also covers the essential Python programming required for AI and ML, including libraries such as NumPy for numerical operations, Pandas for data manipulation, Matplotlib for visualization, and Scikit-learn for algorithm implementation. It also covers the most important data structure and algorithms.
• Module 3: Data Handling and Preprocessing
• Understand how to manage and pre-process data: different data types, cleaning, and feature transformation to prepare the data for more accurate models. Learn different methods of data visualization to facilitate interpretation of results.
• Module 4: Statistics and Probability for Machine Learning
• Have strong fundamentals of concepts related to statistics and probability, including topics on mean, variance, and standard deviation, and probability distributions. Understand hypothesis testing and how it applies to model validation. Explore correlation and regression for relationships among data.
• Module 5: Supervised Learning Techniques
• Study the concepts of supervised learning techniques: simple linear regression, logistic regression, decision trees, and ensemble methods such as random forests and gradient boosting. Explain model performance metrics to get an understanding of model evaluation metrics that include accuracy and cross-validation in order to be sure of the model’s performance.
• Module 6: Unsupervised Learning Techniques
• Review unsupervised learning techniques, clustering algorithms, which include K-Means, Hierarchical Clustering, DBSCAN, dimensionality reduction techniques such as PCA, LDA, and t-SNE. Get insights into how association rule learning can be done through algorithms like Apriori in finding patterns in data.
• Module 7: Advanced Machine Learning Algorithms
• These are more evolved algorithms, such as the Support Vector Machines (SVM), ensemble methods that include bagging, boosting, and stacking. It also touches base on time series analysis and forecasting techniques to handle sequential data for predictive model enhancement.
• Module 8: Deep Learning and Neural Networks
• Study the basics of deep learning: learn neural network architecture, layers, and activation functions. Understand how CNNs work on Image data and RNNs on sequential data. Hands-on with Packages: TensorFlow and PyTorch.
• Module 9: Natural Language Processing (NLP)
• Understand major NLP techniques such as text preprocessing, tokenization, and lemmatization. Learn about sentimental analysis and text and topic modelling. Advanced NLP includes deep learning models, including the Transformer and BERT for solving sophisticated text tasks.
• Module 10: AI & ML Model Deployment
• Study AI and ML model deployment on the cloud and on-premise, and MLOps best practices for Continuous Integration/Continuous Deployment supported by Docker and Kubernetes. Also, how to monitor the model and maintain it so that the model stays useful for a longer period of time.
• Module 11: Ethics and Privacy in AI & ML
• Survey ethical considerations in AI: bias, equity, and transparency. Understand privacy concerns and data security, regulatory standards. Emphasize the responsible use of AI in practice in order to help with questions about how to apply AI in an ethical manner.
• Module 12: Capstone Project
• Apply your knowledge in an integrative project, starting from problem definition to model deployment. Hands-on experience will prove your ability in solving real-world problems with AI/ML techniques.