Applied Data Science Course (online)

On successful completion of this course, students will be able to:

1. Understand and describe the essential concepts of AI and Machine Learning
2. Identify and implement appropriate Machine Learning techniques to build and validate the predictive models
3. Clean the datasets and prepare them for machine Learning projects
4. Understand and implement the supervised and un-supervised machine learning algorithms
5. Practically design and run the Machine Learning projects in Python
6. Practice all the techniques they learned through a final project applied to real-world datasets.

1. Introduction to Data Science: Concepts and Applications
2. Fundamentals of Machine Learning
3. Machine Learning pipeline
4. Data Preparation Techniques
5. Feature Engineering Techniques
6. How to manage a data science project
7. How to design a machine learning project
8. Train and Test Datasets
9. Data preprocessing, Cleaning, and visualization
10. Supervised and unsupervised algorithms
11. Building and validating the Regression models
12. Building and validating the Decision Trees
13. Building Clustering and Classification Models
14. Tuning the machine learning models
15. Final Project: Applying Machine Learning techniques to  real datasets and building the predictive models