Designing and Implementing a Microsoft Azure Al Solution

What Will You Learn?

• Week 1: Introduction to AI
• Session 1: (4 hours)
• • Welcome and course overview
• • History and evolution of AI
• • Key concepts and terminology
• • Current trends and applications of AI
• Session 2: (4 hours)
• • Introduction to AI ethics
• • AI in various industries (healthcare, finance, etc.)
• • Basic AI project planning
• Assignments:
• • Read an introductory AI article
• • Write a brief on potential AI applications in a chosen industry
• Week 2: Python for AI
• Session 3: (4 hours)
• • Introduction to Python programming
• • Python basics: variables, data types, control structures
• Session 4: (4 hours)
• • Functions, modules, and libraries
• • Introduction to Jupyter Notebooks
• • Hands-on coding exercises
• Assignments:
• • Complete Python exercises
• • Install necessary Python libraries (NumPy, pandas, etc.)
• Week 3: Data Handling and Preprocessing
• Session 5: (4 hours)
• • Introduction to data science
• • Types of data: structured vs. unstructured
• • Data collection and cleaning
• Session 6: (4 hours)
• • Exploratory Data Analysis (EDA)
• • Data visualization techniques
• • Hands-on data preprocessing exercises
• Assignments:
• • Perform EDA on a provided dataset
• • Visualize data using matplotlib or seaborn
• Week 4: Machine Learning Basics
• Session 7: (4 hours)
• • Introduction to machine learning
• • Supervised vs. unsupervised learning
• • Common algorithms (e.g., linear regression, k-NN)
• Session 8: (4 hours)
• • Training and testing models
• • Model evaluation metrics
• • Hands-on machine learning exercises
• Assignments:
• • Implement a simple supervised learning model
• • Evaluate model performance using given metrics
• Week 5: Advanced Machine Learning
• Session 9: (4 hours)
• • Decision trees and random forests
• • Support Vector Machines (SVM)
• • Introduction to ensemble methods
• Session 10: (4 hours)
• • Model tuning and hyperparameter optimization
• • Cross-validation techniques
• • Hands-on advanced machine learning exercises
• Assignments:
• • Optimize a machine learning model using cross-validation
• • Apply ensemble methods to improve model performance
• Week 6: Neural Networks and Deep Learning
• Session 11: (4 hours)
• • Introduction to neural networks
• • Basics of deep learning
• • Understanding backpropagation
• Session 12: (4 hours)
• • Convolutional Neural Networks (CNNs)
• • Recurrent Neural Networks (RNNs)
• • Hands-on deep learning exercises
• Assignments:
• • Build a simple neural network using TensorFlow/Keras
• • Explore CNNs or RNNs on a dataset
• Week 7: Natural Language Processing (NLP)
• Session 13: (4 hours)
• • Introduction to NLP
• • Text preprocessing techniques
• • Basic NLP tasks: tokenization, stemming, lemmatization
• Session 14: (4 hours)
• • Advanced NLP: sentiment analysis, topic modeling
• • Hands-on NLP exercises
• • Introduction to NLP libraries (NLTK, spaCy)
• Assignments:
• • Perform text preprocessing on a sample dataset
• • Implement a basic NLP task
• Week 8: Master Class
• Session 15 & 16: (8 hours)
• • Master class by a renowned guest lecturer in the field of AI
• • Topics may include advanced AI concepts, industry trends, research insights, and
• practical applications
• • Q&A session with the guest lecturer
• Week 9: Certification Week
• Session 17: (4 hours)
• • Overview of AI900 Certification
• • Key topics and areas of focus for AI900
• • Study tips and strategies
• Session 18: (4 hours)
• • Mock tests and review sessions
• • Final preparations for the AI900 exam
• • Certification ceremony and course conclusion
• Resources and Tools
• • Programming Language: Python
• • IDEs: Jupyter Notebooks, PyCharm
• • Libraries: NumPy, pandas, matplotlib, scikit-learn, TensorFlow/Keras, NLTK,
• spaCy
• • Additional Tools: GitHub for version control, Kaggle for datasets