Artificial Intelligence at Machine Learning
( 25 Module )

module #1
Introduction to Artificial Intelligence
Overview of AI, his history, and applications

module #2
Machine Learning Fundamentals
Introduction to machine learning, type, and supervised/unsupervised learning

module #3
Math and Statistics for ML
Linear algebra, calculus, probability, at statistics para sa machine learning

module #4
Python for Machine Learning
Introduction to Python, NumPy, Pandas, at pagmamanipula ng data

module #5
Data Preprocessing
Data paglilinis, pag-scale ng feature, at pagpili ng feature

module #6
Supervised Learning
Regression, classification, at model evaluation metrics

module #7
Linear Regression
Simple and multiple linear regression, cost function, at gradient descent

module #8
Logistic Regression
Binary classification, logistic function, at decision boundaries

module #9
Decision Trees
Introduction to decision trees, entropy, at information gain

module #10
Random Forests
Ensemble learning, bagging, at random na kagubatan

module #11
Support Vector Machines
Maximum-margin classification, soft margin, at kernel trick

module #12
Unsupervised Learning
Clustering, dimensionality reduction, at anomaly detection

module #13
K-Means Clustering
K-means algorithm, centroid initialization, at convergence

module #14
Principal Component Analysis
PCA, feature extraction, at dimensionality reduction

module #15
Deep Learning Fundamentals
Introduction to neural network, perceptron, at multilayer perceptron

module #16
Convolutional Neural Networks
CNNs, convolutional layers, at image classification

module #17
Recurrent Neural Networks
RNNs, LSTM, at sequence modelling

module #18
Natural Pagproseso ng Wika
Pag-preprocess ng teksto, tokenization, at pag-embed ng salita

module #19
Paglipat ng Pag-aaral
Mga pre-trained na modelo, fine-tuning, at model zoo

module #20
Pagsusuri at Pagpili ng Modelo
Pagpili ng modelo, hyperparameter tuning, at cross-validation

module #21
Handling Imbalanced Datasets
Class imbalance, oversampling, at undersampling techniques

module #22
Model Deployment
Model deployment, API integration, at Docker containerization

module #23
AI Ethics and Fairness
Bias detection, fairness metrics, at etikal na pagsasaalang-alang

module #24
Mga Espesyal na Paksa sa AI
Generative models, attention mechanisms, at explainable AI

module #25
Pagtatapos ng Kurso at Konklusyon
Pagpaplano ng mga susunod na hakbang sa karera ng Artificial Intelligence at Machine Learning

Handa nang malaman, ibahagi, at makipagkumpetensya?