Here is the translated outline of the “Machine Learning and Big Data” course:
- Unit One: Overview of Machine Learning and Big Data
- What is Machine Learning?
- What is Big Data?
- The relationship between Machine Learning and Big Data
- Applications of Machine Learning and Big Data
- Unit Two: Data Processing and Analysis
- Data Cleaning
- Data Transformation
- Data Exploration
- Feature Engineering
- Unit Three: Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- Unit Four: Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Association Rule Learning
- Unit Five: Deep Learning and Neural Networks
- Fundamentals of Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Application of Deep Learning in Big Data
- Unit Six: Evaluating Machine Learning Models
- Confusion Matrix
- ROC Curve
- Overfitting and Underfitting
- Cross-validation
- Unit Seven: Big Data Tools and Techniques
- Introduction to Hadoop
- Introduction to Spark
- NoSQL Databases
- Application of Cloud Computing in Big Data
- Unit Eight: Case Studies and Future Trends
- Analysis of practical applications of Machine Learning and Big Data
- Challenges in Machine Learning and Big Data
- Future trends in Machine Learning and Big Data