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AI/ML Fundamentals
AIML
5. Reference
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN
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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN
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1.1 The Concept of Artificial Intelligence
1.2 Turing's test
1.3 Areas of Artificial Intelligence
1.4 Regulation of Artificial Intelligence, Legal and Ethical Challenges
1.5 Narrow, General, and Superintelligence
Intro Exercise: Jupyter Exercise Notebooks
Intro Exercise: Google Colab platform
Intro Exercise: NumPy, Matplotlib and Pandas libraries
Exercise 1.1: Application of Artificial Intelligence - Using AI Programs
Exercise 1.2: Application of Artificial Intelligence - Simulating the Turing Test
Exercise 1.3: Application of Artificial Intelligence - Identifying AI-Based Systems
Exercise 1.4: Application of Artificial Intelligence - Exploring Superintelligence
2.1 The Relationship between Artificial Intelligence and Machine Learning
2.2 Data-driven programming
2.3 Basic concepts of machine learning
2.4 A machine learning process
2.5 Types of machine learning
2.6 Data in Machine Learning
2.7 Exploratory Data Analysis (EDA)
2.8 Creating a Representation of a Dataset
2.9 Training, validation and testing sets
Exercise 2.A: Popular datasets MNIST
Exercise 2.B: Popular datasets - ImageNet
Exercise 2.C: Popular datasets - COCO
Exercise 3: Exploratory analysis of a data set
Exercise 4: Training, validation and testing sets
3.1 Linear Regression
3.2 Gradient Descent
3.3 Polynomial regression
3.4 Multiple linear regression
3.5 Classification, types of classification, and matrix of confusion
3.6 Logistic regression
3.7 Decision tree
3.8 K-Nearest Neighbor (kNN) Algorithm
3.9 Hyperparameters
3.10 Generalization, under-adaptation, and over-adaptation
3.11 Validation, cross-validation
3.12 Regularization
Exercise 5.1: Linear regression
Exercise 5.2: Gradient Descent
Exercise 6: Logistic Regression
Exercise 7: Decision tree
Exercise 8: The k-nearest neighbor algorithm
Exercise 9: Validation
4.1 Neural networks
4.2 Training of neural networks
4.3 Convolutional Neural Networks (CNN)
4.4 Recurrent neural networks
4.5 K-Means algorithm
Exercise 10: Training neural networks
Exercise 11: VGG-16 Network and Image Classification Task
Exercise 12: K-Means algorithm
GitHub repostitory
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS EN
Curriculum: Fundamentals of Artificial Intelligence and Machine Learning
https://scikit-learn.org/
https://petlja.org/sr-Latn-RS/kurs/11203/0
Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow -Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-Aurélien-Géron-O’Reilly-Media-2019
machine-learning-and-artificial-intelligence-1st-ed-2020-978-3-030-26621-9-978-3-030-26622-6 compress
Explorations in Artificial Intelligence and Machine Learning
Machine Learning For Absolute Beginners
Artificial Intelligence A Modern Approach 3rd
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Hands-On AI Projects for the Classroom
Book
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS SR
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Curriculum: Fundamentals of Artificial Intelligence and Machine Learning