Machine Learning Engineer jobs grew almost 10 fold since 2012, and Data Scientist jobs grew 6.5 times. However, finding qualified people to fill such jobs remains difficult.
Results from a survey include: the average data scientist is a male, with median experience on the job is 2 years. He uses R, Python, and SQL. Read for more details.
70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.
When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.
Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.
"A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing."
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.
5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.
While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.
My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.
In this post you will find a list of common the data fallacies that lead to incorrect conclusions and poor decision-making using data. Here you will find great resources and information so that you can always be reminded of these fallacies when you’re working with data.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles
How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?
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