This introduction to intermediate Machine Learning course will give you practical working knowledge to apply essential analytics and decision making determinations to scientific, finance and investments scenarios. The course will be delivered online. The course delivery makes assumed knowledge of python programming for data management, learners without prior exposure can enroll in our preparatory, Data analysis with Python and SQL course. The structure of the current course is as follows:
Tools for Data Science
The introductory module introduces the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools. The module also gives an outline of languages commonly used by data scientists such as Python, R, and SQL. Learners will demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features. Finally learners will be able to create and manage source code for data science using Git repositories and GitHub.
Statistics for Data Science with Python
Learners will receive practical training to write Python code to undertake various statistical tests including a T test, an ANOVA, and regression analysis. Students will also interpret the results of the statistical analysis after conducting hypothesis testing. Further, learners will be supported to calculate descriptive statistics and visualization by writing Python code after which they will then create a capstone project demonstrating their understanding of different statistical test using Python. Exciting application areas are designed to make learning more fun and concepts easier to grasp.
Machine learning with Python
This course introduces machine learning concepts of supervised and unsupervised learning. An outline of clustering and classification supervised and unsupervised algorithms is given. Specific focus will be given to regression, decision trees, K-Nearest neighbors, Logistic regression, SVM and K-Means clustering algorithms. Learners will engage in intensive practical exercises.
Final machine learning Project
The final project in Machine learning will be crucial to make learners industry ready. Under this practical exercise, learners will use machine learning modules to make predictions on stock price movements, financial inclusion situations of individuals, and household resilience in development programs. In the final project, learners will be provided with an overview of the problems and the tools required to complete the course.