Practical Artificial Intelligence (AI) with H2O in Python
What you'll learn
- Be Able To Use the Python/Anaconda Environment For Practical Data Science
- Learn the Important Concepts Associated With Supervised and Unsupervised Learning
- Implement Supervised Learning on Real Life Data With the Powerful H2O Package in Python
- Implement Unsupervised Learning on Real Life Data With the Powerful H2O Package in Python
- Implement Artificial Neural Networks (ANN) on Real Life Data With the Powerful H2O Package in Python
- Implement Deep Neural Networks (DNN) on Real Life Data With the Powerful H2O Package in Python
- Be Able To Operate & Install Software On A Computer
- Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
- Prior Exposure To What Neural Networks Are & What They Can Be Used For
- Be Able to Install Packages in Python
YOUR COMPLETE GUIDE TO H2O: POWERFUL PYTHON PACKAGE FOR MACHINE LEARNING, & DEEP LEARNING IN PYTHON
This course covers the main aspects of the H2O package for data science in Python. If you take this course, you can do away with taking other courses or buying books on Python-based data science as you will have the keys to a very powerful Python supported data science framework.
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 machine learning, neural networks and deep learning via a powerful framework, H2O in Python, you can give your company a competitive edge and boost your career to the next level!
LEARN FROM AN EXPERT DATA SCIENTIST:
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I finished a PhD at Cambridge University, UK, where I specialized in data science models.
I have +5 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 practical neural networks and deep learning.
Unlike other Python instructors, I dig deep into the data science features of Python and give you a one-of-a-kind grounding in data science...
You will go all the way from carrying out data reading & cleaning to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using Python.
Among other things:
You will be introduced to powerful Python-based deep learning packages such as H2O.
You will be introduced to important concepts of machine learning without the jargon.
You will learn how to implement both supervised and unsupervised algorithms using the H2O framework
Identify the most important variables.
Implement both Artificial Neural Networks (ANN) and Deep Neural Networks (DNNs) with the H2O framework
Work with real data within the framework
NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:
You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real-life.
After taking this course, you’ll easily use the data science package H2O to implement novel deep learning techniques in Python. You will get your hands dirty with real-life data, including real-life imagery data which you will learn to pre-process and model
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will also work with real data and you will have access to all the code and data used in the course.
JOIN MY COURSE NOW!
Who this course is for:
- People Wanting To Master The Python/Anaconda Environment For Data Science
- Students Wishing to Master a Powerful Data Science Framework, H2O For Machine Learning in Python
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).