Data Science & Machine Learning

This course provides a comprehensive introduction to data science and machine learning using Python. Students will learn the foundational concepts of data science, including data collection, cleaning, and preparation, as well as key Python libraries like Pandas, NumPy, and Matplotlib. The course emphasizes hands-on experience, where students can explore real-world datasets and engage in practical exercises to understand data wrangling, preprocessing, and exploratory data analysis (EDA). These essential skills lay the groundwork for advanced topics like machine learning and deep learning.

As the course progresses, students will delve into machine learning techniques, including supervised and unsupervised learning algorithms. They will gain proficiency in building predictive models using the Scikit-learn library and learn to evaluate and fine-tune model performance. Additionally, students will explore time series analysis and deep learning, including neural networks, to understand how these technologies are transforming industries. By the end of the course, students will have developed a strong foundation in data science and machine learning, preparing them to apply these skills in real-world data-driven projects and pursue further career opportunities in the field.Learners will explore practical considerations for machine learning with Python, including data preprocessing, cleansing, and evaluation. They will learn about key machine learning concepts and the use of core Python data science tools. The course aims to provide a strong foundational understanding of data science and machine learning concepts, preparing students for further exploration and application in these fields.