Data-Science and AI
- 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) 9.1. 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) 9.1.3.5. Random Forest (RF)
- Apply Classification on Dataset
- Unsupervised
- Cluster Analysis
- Dimensionality Reduction
- Introduction to Artificial Neural Network (ANN) 10.1. Perceptron
- Simple Neural Network
- Backpropagation
- Convolutional Neural Network (CNN) 12.1. Image Data
- Pooling
- Padding and Strides
- Natural Language Processing (NLP)
- Text Vectorization
- Stop words
- Stemming
- Lemmatization
- TF-IDF
- Final Project on AI
