Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.
Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.
To receive the certificate for this course, you’ll need to submit one of the projects for the course. After successful evaluation by the course advisor, you’ll receive the certification.
Introduction to programming
- Python programming
- Standard library functions
- Variables and strings
- Functions, control flows and loops
- Structured data
- Research methods and visualization of data
- Concentration trends
- Variability and standardization
- Normal distribution and sampling distribution
- Statistical tests: hypothesis test, T test, ANOVA, chi-square test. Regression and correlation.
- Differential equations
- Vectors: Learning the basic operation of the vector and writing a library to implement the basic operation of the vector.
- Intersection point: Learning the intersection of geometric and algebraic expression and how to solve real-world problems.
- Writing algorithms to calculate a set of straight or flat intersections.
- Data analysis process: Learn how to use data to answer questions.
- NumPy and Pandas operations for one-dimensional data.
- NumPy and Pandas operations for two-dimensional data.
- Data modelling: Understand the basic types of data and learn how to handle data sets
Evaluation and verification
- Learn how to use the accuracy rate or recall rate and other indicators to test and measure to improve performance.