This course is centered around data cleaning and preprocessing techniques in the field of data science. Students will learn various methodologies to handle missing data, outliers, and inconsistencies within datasets, as well as techniques for data transformation and normalization. The course emphasizes the importance of data quality and prepares students to effectively preprocess datasets to ensure accurate and reliable analysis results in their data science projects.

This course delves into supervised learning techniques, specifically focusing on regression analysis in the context of machine learning. Students will learn about various regression models and algorithms, exploring how to predict and model the relationships between variables in datasets. The course emphasizes the practical application of regression analysis for making continuous predictions and estimates, enabling students to understand and implement predictive modeling in real-world scenarios.

This course delves into supervised learning techniques, focusing specifically on classification methods in the context of machine learning. Students will learn about various classification algorithms and models used to categorize data points into predefined classes or categories. The course emphasizes the practical application of classification techniques for pattern recognition, prediction, and decision-making tasks, enabling students to develop a comprehensive understanding of how to implement and evaluate classification models for diverse applications in data analysis and prediction.

This course centers on the principles and techniques of dimensionality reduction and feature extraction in the context of data science and machine learning. Students will learn various methods to reduce the number of random variables under consideration, aiming to obtain a smaller set of features that are more manageable, interpretable, and efficient for analysis. The course emphasizes the practical applications of dimensionality reduction and feature extraction for simplifying complex datasets and improving the performance of machine learning models.

The Machine Learning Project Part 1 course is a comprehensive module that focuses on the initial stages of implementing a machine learning project. It covers data collection, preprocessing, and exploratory data analysis, providing students with hands-on experience in preparing datasets for analysis. Additionally, the course emphasizes the selection and implementation of appropriate machine learning algorithms to establish a foundation for building predictive models in subsequent project stages.

The Machine Learning Project Part 2 course is a continuation of the comprehensive module, focusing on advanced stages of implementing a machine learning project. It emphasizes the application of various modeling techniques, model evaluation, and tuning to improve model performance. Additionally, the course covers the interpretation and communication of results, enabling students to gain a deeper understanding of the intricacies of machine learning applications and their practical implications.

This course provides an introduction to the fundamental concepts and applications of Artificial Intelligence (AI). It covers various topics such as machine learning, natural language processing, and computer vision, giving students a foundational understanding of AI’s capabilities and its potential impact on diverse fields. The course emphasizes the basic principles and approaches used in AI systems, enabling students to grasp the fundamental concepts that underpin the development and implementation of AI technologies.