Worked on six distinct modules, each designed to equip me with valuable skills and knowledge in the field of data science and machine learning.
In Module 1, I delved into Python programming, covering fundamental topics such as data structures, control statements, functions, and object-oriented programming. This foundation enabled me to effectively manipulate data and build robust applications. Additionally, I worked on web API integration and database management, vital components of modern data-driven solutions. The module concluded with advanced techniques like list comprehension, lambda functions, and problem-solving strategies, setting the stage for more intricate challenges in subsequent modules.
Module 2 further honed my data manipulation skills with a focus on Python for data analysis. Worked on data analytics frameworks, explored libraries like NumPy and Pandas, and learned to handle missing data effectively. Visualization techniques using Matplotlib, Seaborn, and Plotly became second nature, and I put my skills to the test through hands-on case studies.
In module 3, I worked on statistics, covering topics such as normal distribution, hypothesis testing, and statistical testing—an essential toolkit for informed decision-making in data analysis.
Module 4 was the heart of the project, as i worked on machine learning concepts. I honed my skills in supervised and unsupervised learning projects, delved into various algorithms, and learned the art of model optimization and hyperparameter tuning.
Furthermore, in Module 5 I worked on MLOps projects, focusing on model management and orchestration, ensuring my ability to deploy and maintain machine learning solutions.
Finally, Module 6 presented a series of real-world case studies that allowed me to apply my knowledge and skills to practical scenarios, from regression tasks to sentiment analysis, web application development, and cloud deployment.