mbexhrs3/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Summary:

I am an Atmospheric Scientist with strong mathematical, statistical and programming background. I have 5+ years of working experience in modelling, data analysis and visualization. I am passionate about machine learning and deep learning. I primarily use Python programming language including NumPy, Xarray, Pandas, Scikit-Learn, Matplotlib, NLP, and TensorFlow. However, I am proficient in other languages (e.g. Octave, Matlab, R, SQL, JavaScript and Fortran) as well. I have an excellent understanding of AWS cloud computing system. I strongly believe that learning is a never ending process which always motivates me to be a enthusiastic learner. I enjoy taking challenges, in particular, solving real-world problems that bring a real-change in our communities.

Experience:

As a Research Scientist at the BC ministry of health, I am working on the impacts of climate change on health. I am also teaching Atmospheric and Oceanic Sciences at the School of Environment and Sustainability, Royal Roads University, Victoria, Canada. Recently, I worked on a project “Fight hunger through machine learning-based crop classification” in Uganda where I led the exploratory data analysis (EDA) working group mainly working on extractions, transformations and visualization of data. Previously, I worked as a Research Associate at the Tyndall Center for Climate Change Research, The University of Manchester, UK. At the Tyndall Center, I worked on various projects from grant proposal writing to research and coordination. In particular, I led a project MITAC which was developing a contrail-climate model to quantify the impacts of aviation-induced pollution on climate. Prior to joining the Tyndall Center, I worked as a Climate Modelling Scientist (2013-2018) in the Natural Sciences and Engineering Research Council (NSERC) funded projects at the Canadian Center for Climate Modelling and Analysis (CCCma), the University of Victoria, Canada, where I worked on numerical modelling and statistical analysis of climate, air pollution and vegetation including publications of the results in peer-reviewed journal articles. During my PhD research at Manchester, I led the Himalayan Aerosol Cloud Interaction (HACI) field experiment which was conducted to monitor air quality in the foot-hills of the Nepal Himalayas. The HACI field experiment was complemented by a high-resolution modelling study to model the impacts of air pollution on regional climate. I am actively involving reviewing academic papers from ERL, Climate Policy and MDPI journals.

Credentials:

I have received PhD degree in Atmospheric Science from the University of Manchester, UK, and MSc degree in Environmental Science from the UNESCO-IHE, the Netherlands. The degree programs were fully-funded by the Dorothy-Hodgkin Post-graduate Scholarship Program and the Netherlands Fellowship Program, respectively. I have obtained bachelor’s degree in Civil Engineering from Institute of Engineering, Kathmandu, Nepal. Recently, I have successfully completed a 11-week online Machine Learning course offered by the Stanford University and 4-week Natural Language Processing (NLP) course.

Certifications

• Machine Learning - Stanford University (Online via Coursera)
• Natural Language Processing with Classification and Vector Spaces (DeepLearning.AI)

Other Links

• Google Scholar
• LinkedIn
• ResearchGate
• Twitter