Introduction to AI and its applied fields
Introduction to Data and Statistics
- Data Distributions
- Descriptive Statistics
- Mean, Median, Mode
- Std. Deviation, Variance and Covariance
- Inferential Statistics
- Correlation
- Hypothesis Testing
Introduction to Python
- Basic Syntax
- Arithmetic operations
- List
- String
- Tuple
- Dictionary
- Function
Object oriented Python (OOP)
- Class and Object
- Inheritance
- Method Overriding
Regular Expression (RE)
Introduction to Advanced Python
- Numpy
- Pandas
- Matplotlib and Seaborn
Project on Visualization
PySQL
Introduction to Machine Learning (ML)
- Supervised
- Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Apply Regression on Dataset
- Classification
- Logistic Regression
- K-nearest neighbor (KNN)
- Decision Tree
- Support Vector Machine (SVM)
- Random Forest (RF)
- Apply Classification on Dataset
- Unsupervised
- Cluster Analysis
- Dimensionality Reduction
Introduction to Artificial Neural Network (ANN)
- Perceptron
- Simple Neural Network
Backpropagation
Convolutional Neural Network (CNN)
- Pooling
- Padding and Strides
Natural Language Processing (NLP)
- Text Vectorization
- Stop words
- Stemming
- Lemmatization
- TF-IDF
Final Project on AI
