AI Academy

Classification Analysis

Instructor: Di Wu Duration: 4 weeks to complete at 10 hours a week
Objective 1 Understand the concept and significance of classification as a supervised learning method.
Objective 2 Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
Objective 3 Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.
Bayesian Statistics
Data Science
Classification And Regression Tree (CART)
Machine Learning
Supervised Learning
Probability & Statistics
Machine Learning Algorithms
Feature Engineering
Predictive Modeling
Data Mining
Data analysis

Regression Analysis

Instructor: Di Wu Duration: 4 weeks to complete at 10 hours a week
Objective 1 Understand the principles and significance of regression analysis in supervised learning.
Objective 2 Implement cross-validation methods to assess model performance and optimize hyperparameters.
Objective 3 Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.
Regression Analysis
Data analysis
Statistical Analysis
Predictive Modeling
Exploratory Data Analysis
Machine Learning Methods
Feature Engineering
Supervised Learning
Statistical Modeling
Scikit Learn (Machine Learning Library)

Clustering Analysis

Instructor: Di Wu Duration: 4 weeks to complete at 10 hours a week
Objective 1 Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.
Objective 2 Apply clustering techniques to diverse datasets for pattern discovery and data exploration.
Objective 3 Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.
Exploratory Data Analysis
Applied Machine Learning
Data analysis
Statistical Machine Learning
Machine Learning Algorithms
Scikit Learn (Machine Learning Library)
Unsupervised Learning
Machine Learning
Dimensionality Reduction
Machine Learning Methods

Association Rules Analysis

Instructor: Di Wu Duration: 22 hours to complete 3 weeks at 7 hours a week
Objective 1 Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection
Objective 2 Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
Objective 3 Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.
Data Mining
Machine Learning Algorithms
Data Processing
Machine Learning Methods
Algorithms
Anomaly Detection
Unsupervised Learning
Data analysis
Exploratory Data Analysis
Feature Engineering
Applied Machine Learning

Data Analysis with Python Project

Instructor: Di Wu Duration: 2 weeks to complete at 10 hours a week
Objective 1 Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
Objective 2 Apply various classification and regression algorithms and implement cross-validation and ensemble techniques to enhance the performance of models.
Objective 3 Apply various clustering, dimension reduction association rule mining, and outlier detection algorithms for unsupervised learning models.
Unsupervised Learning
Analytics
Statistical Analysis
Supervised Learning
Machine Learning
Regression Analysis
Predictive Modeling
Dimensionality Reduction
Data analysis
Data Mining
Exploratory Data Analysis
Statistical Methods
Scikit Learn (Machine Learning Library)
Classification And Regression Tree (CART)
Anomaly Detection
Machine Learning Algorithms