Mini Diploma in Artificial Intelligence and Machine Learning

Mini Diploma in Artificial Intelligence and Machine Learning

Duration: 1 Month
Mode: Online Self-Study
Target Audience: Beginners interested in AI and ML concepts
Outcomes:

• Understanding of core AI/ML concepts.
• Ability to implement basic ML models using Python.
• Insights into AI ethics and real-world applications.

Course Structure and Weekly Breakdown

Week 1: Foundations of AI and ML

Objective: Understand the basics of AI, ML, and their significance.

1. Introduction to Artificial Intelligence (AI):

   • Definition and history of AI.
   • Applications of AI in industries.
   • Key differences: AI, ML, Deep Learning, and Data Science.

2. Introduction to Machine Learning (ML):

   • Definition and types of ML: Supervised, Unsupervised, Reinforcement Learning.
   • The ML workflow: Data Collection, Preprocessing, Training, Testing, and Deployment.

3. Key Algorithms Overview:

   • Linear Regression, Logistic Regression, k-Nearest Neighbors (k-NN).

4. Python Basics for AI/ML:

   • Setting up Python for ML (Anaconda, Jupyter Notebook).
   • Libraries: NumPy, Pandas, Matplotlib.

Assignments:

• Watch introductory videos on AI/ML.
• Complete a quiz on the history and applications of AI.
• Practice Python coding basics.

Week 2: Machine Learning Workflow and Algorithms

Objective: Dive into the ML process and explore key algorithms.

1. Data Preprocessing and Visualization:

   • Handling missing data.
   • Scaling and normalizing data.
   • Visualizing data using Matplotlib and Seaborn.

2. Supervised Learning Algorithms:

   • Linear Regression and Logistic Regression in detail.
   • Hands-on coding with datasets.

3. Unsupervised Learning Algorithms:

   • Clustering: k-Means Clustering.
   • Dimensionality Reduction: Principal Component Analysis (PCA).

4. Model Evaluation:

   • Metrics: Accuracy, Precision, Recall, F1 Score.
   • Cross-validation.

Assignments:

• Preprocess and visualize a dataset.
• Implement and evaluate a simple Linear Regression model.

Week 3: Practical Implementation and Tools

Objective: Develop hands-on skills in implementing ML models.

1. Building ML Models in Python:

   • Working with Scikit-learn.
   • Training and testing models.

2. Real-World Applications:

   • Sentiment Analysis using Natural Language Processing (NLP).
   • Predictive analysis (e.g., predicting house prices).

3. Introduction to Neural Networks:

   • Basics of Deep Learning and Neural Networks.
   • Using TensorFlow/Keras for beginners.

4. Mini Project Preparation:

   • Define a problem statement.
   • Collect and preprocess data.

Assignments:

• Build and train an ML model on a given dataset.
• Start working on the mini project.

Week 4: Ethical AI, Trends, and Project Completion

Objective: Explore AI ethics and present a complete project.

1. AI Ethics and Societal Impact:

   • Bias in AI.
   • Ethical frameworks for AI usage.
   • Responsible AI development.

2. Emerging Trends in AI:

   • Generative AI (e.g., ChatGPT).
   • Autonomous systems.

3. Final Project Submission and Feedback:

   • Complete and document the mini project.
   • Submit project reports and receive feedback.

Assignments:

• Write an essay on AI ethics.
• Complete and present the mini project.

Mini Project Ideas

1. Predicting house prices using Linear Regression.
2. Customer segmentation using k-Means Clustering.
3. Sentiment analysis on social media data.

Evaluation Criteria

1. Weekly quizzes (20%).
2. Assignments and coding tasks (30%).
3. Final project submission and presentation (50%).

Recommended Tools and Resources

• Platforms: Google Colab, Jupyter Notebook.
• Datasets: Kaggle, UCI Machine Learning Repository.
• Books/References:
  • “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron.
  • Online tutorials (e.g., Coursera, YouTube channels like “StatQuest”).

Syllabus Overview – English Version

Week 1: Foundations of AI and ML

• Introduction to Artificial Intelligence (AI): History, applications, and types.
• Basics of Machine Learning (ML): Types of learning and the ML workflow.
• Overview of key ML algorithms: Linear Regression, Logistic Regression, k-NN.
• Python setup for AI/ML (Anaconda, Jupyter Notebook), basics of NumPy, Pandas.

Week 2: ML Workflow and Algorithms

• Data preprocessing, handling missing values, data visualization (Matplotlib, Seaborn).
• Supervised Learning Algorithms: Linear Regression and Logistic Regression with coding.
• Unsupervised Learning: Clustering (k-Means), Dimensionality Reduction (PCA).
• Model evaluation: Accuracy, Precision, Recall, F1 Score, Cross-validation.

Week 3: Practical Implementation and Tools

• Hands-on with Scikit-learn: Training and testing models.
• Real-world applications: Sentiment analysis (NLP), predictive analysis (house prices).
• Introduction to Neural Networks using TensorFlow/Keras.
• Mini Project Preparation.

Week 4: AI Ethics, Trends, and Final Project

• Ethics in AI: Bias, fairness, responsible AI.
• Emerging AI trends: Generative AI (ChatGPT), autonomous systems.
• Final project completion and presentation.

විෂය නිර්දේශය – Sinhala Version

1 වන සතිය: AI සහ ML මූලිකතා

• Artificial Intelligence (AI) පිළිබඳ හැදින්වීම: ඉතිහාසය, යෙදුම්, සහ වර්ග.
• Machine Learning (ML) මූලිකතා: ඉගෙනුම් වර්ග, සහ ML ක්‍රියා ප්‍රවාහය.
• ප්‍රධාන ML අලගෝරිදම් වල සාරාංශය: රේඛීය ප්‍රතිසන්ධානය (Linear Regression), ලොජිස්ටික් ප්‍රතිසන්ධානය (Logistic Regression), k-NN.
• Python සැකසුම: Anaconda, Jupyter Notebook. NumPy සහ Pandas මූලිකාංග.

2 වන සතිය: ML ක්‍රියා ප්‍රවාහය සහ අලගෝරිදම්

• දත්ත ප්‍රාථමික සැකසීම, අස්වැන්න පිරවීම, දත්ත දැක්ම (Matplotlib, Seaborn).
• පරීක්ෂණාත්මක ඉගෙනුම: රේඛීය ප්‍රතිසන්ධානය සහ ලොජිස්ටික් ප්‍රතිසන්ධානය කේතන සමඟ.
• අපරීක්ෂණාත්මක ඉගෙනුම: Clustering (k-Means), Dimensionality Reduction (PCA).
• ආදර්ශ මිනුම්: නිවැරදිභාවය, නිරවද්‍යතාව, කැඳවීම, F1 ලකුණු, Cross-validation.

3 වන සතිය: ප්‍රායෝගික ක්‍රියාකාරකම් සහ මෙවලම්

• Scikit-learn සමඟ අත්හදා බලන්න: ආදර්ශ පුහුණු කිරීම සහ පරීක්ෂා කිරීම.
• සත්‍ය ලෝක යෙදුම්: සංවේදන විශ්ලේෂණය (NLP), අනාවැකි විශ්ලේෂණය (ගෘහ මිල).
• Neural Networks පිළිබඳ හැඳින්වීම: TensorFlow/Keras භාවිතයෙන්.
• Mini Project සූදානම් කිරීම.

4 වන සතිය: AI නීතිමය, ප්‍රවණතා, සහ අවසාන ව්‍යාපෘතිය

• AI නීතිමය: වාසනාව, සාධාරණභාවය, වගකිවයුතු AI.
• නවතම AI ප්‍රවණතා: Generative AI (ChatGPT), ස්වයංක්‍රීය පද්ධති.
• අවසාන ව්‍යාපෘතිය සම්පූර්ණ කිරීම සහ ඉදිරිපත් කිරීම.

Assignments and Project Ideas

Assignments:

• Practice Python basics and ML concepts weekly.
• Preprocess, visualize datasets, and implement small models.

Project Ideas:

• Predict house prices (Linear Regression).
• Sentiment analysis on product reviews (NLP).
• Customer segmentation (k-Means Clustering).