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Certifications

• Art and Science of Machine Learning
  Essential skills of ML intuition, good judgment and experimentation to finely tune and optimize ML models for the best performance with the many knobs and levers or ‘hyperparameters’ involved in training a model.

• Natural Language Processing
  This course covered a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. It helped recognize NLP tasks in day-to-day work, propose approaches, and judge what techniques are likely to work well.

• Python for Data Science and AI
  This was a kickstart to learning Python for data science, as well as programming in general. It helped understand Basics of Python, Data Structures and Programming Fundamentals and Intelligent Data Analytics and Visualization.

• Machine Learning with Python
  This course dived into the basics of machine learning, where it applies to the real world, a general overview of Supervised vs Unsupervised learning, model evaluation, and Machine Learning algorithms. It ends with implementations of predicting economic trends, predicting customer churn and recommendation engines and see how it affects society.

• Applied Machine Learning in Python
  This course will introduced the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Starting on how machine learning is different than descriptive statistics. Supervised approaches for creating predictive models will be described, predictive modelling methods while understanding process issues related to data generalizability, building ensembles, and practical limitations of predictive models. Identify and apply for a particular ML algorithm to a dataset in need, engineer features to meet that need, and write python code to carry out analyses.

• Guided Tour of Machine Learning in Finance
  This aimed at providing an introductory and broad overview of the field of ML with the focus on applications on Finance, to obtain a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. Supervised Machine Learning methods were used in the capstone project to predict bank closures.