With so many Python based Data Science & Machine Learning courses around, why should you take this course?
As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers MAIN ASPECTS of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge - and boost your career to the next level.
THIS IS MY PROMISE TO YOU
COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED MACHINE LEARNING
But first things first. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing simple deep learning based models using Python.
About the Instructor
Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler
Frequently Asked Questions
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