Data Science certification

Are you interested in learning data science and machine learning with Python. If so, this course is for you. Designed for students and professionals who want to acquire practical knowledge and skills in data science and machine learning using Python, this course covers various topics that are essential for building a strong foundation in data analysis, visualisation, and machine learning.

The course covers various essential topics such as an overview of data science and machine learning concepts and terminology. Students will follow a crash course on Python Programming for a strong foundation for Data Science. They will learn about data analysis using Numpy and pandas, and data visualization using Matplotlib and seaborn.

Students will also learn about data preprocessing, cleaning, encoding, scaling, and splitting for machine learning. The course covers a range of machine learning techniques, including supervised, unsupervised, and reinforcement learning, and various models such as linear regression, logistics regression, naives bayes, k-nearest neighbours, decision trees and random forests, support vector machines, and k-means clustering. In addition, students will get hands-on training with scikit-learn to train, evaluate, tune, and validate models.

They will also learn about natural language processing techniques, including pre-processing, sentence segmentation, tokenization, POS tagging, stop word removal, lemmatization, and frequency analysis, and visualizing dependencies in NLP data. The final week of the course involves working on a final project and taking certification exams.