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Machine learning algorithms can be black boxes--inputs go in, outputs come out, and what happens in the middle is anybody's guess.  But understanding how a model arrives at an answer is critical for interpreting the model, and for knowing if it's doing something reasonable (one could even say... trustworthy).  We'll talk about a new algorithm called LIME that seeks to make any model more understandable and interpretable.

Relevant Links:
http://arxiv.org/abs/1602.04938
https://github.com/marcotcr/lime/tree/master/lime

Model Interpretation (and Trust Issues)

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

In this session of the Super Data Science Podcast, I chat with business strategy expert Brendan Hogan. You will learn how he uses Data to alter strategy of businesses, you will learn about the three pillars of research and how to build a successful career from your strengths.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/4

SDS 004 : Data and Strategy, Three Pillars of Research and Building your Career with Brendan Hogan

In this session of the Super Data Science Podcast, I chat with Up and Rising Tableau Expert Megan Putney. You will know about how Megan started with Tableau through online learning, learn about know about Ad Hoc Analytics and gain some powerful Tableau insights.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/12

SDS 012 : Online Learning, Tableau Insights and Ad Hoc Analytics with Megan Putney

In this episode of the SuperDataScience Podcast, I chat with Aspiring Data Scientist Nicholas Cepeda. You will be able to discover the many different pathways in Data Science, learn R from a SQL background and know about SAS Enterprise Miner. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/41

SDS 041: An Inspiring Journey from a Totally Different Background to Data Science

In this episode of the SuperDataScience Podcast, I chat with Kaggle Grandmaster Eu Jin Lok. You will be able to know how to forecast year-ahead hospital stays using an ensemble of algorithms, discover feature extraction in a credit scoring model, discuss his great competition experiences and be able to get ideas on how to be involved in a Kaggle competition.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/45

SDS 045: Exclusive Interview with the Legendary in Kaggle Competitions Eu Jin Lok

In this episode of the SuperDataScience Podcast, I chat with the Deep Learning Expert Hadelin de Ponteves. You will be able to have a look at the 6 supervised and unsupervised deep learning models, learn the idea to the question: why deep learning instead of machine learning, and discover TensorFlow and PyTorch as the two major tools in Deep Learning.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/47

SDS 047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised

The year: 1998.  The size of the web: 150 million pages.  The problem: information retrieval.  How do you find the "best" web pages to return in response to a query?  A graduate student named Larry Page had an idea for how it could be done better and created a search engine as a research project.  That search engine was called Google.

Page Rank

Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution.  It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE.  And boy oh boy can it be tough to explain.  But we're trying our hardest in this episode!

KL Divergence

Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes.  Today we have a roundup of a few successful efforts to create robust adversarial examples, including what it means for an adversarial example to be robust and what this might mean for machine learning in the future.

Adversarial Examples

In this episode of the SuperDataScience Podcast, I chat with the Chief Data Officer at Ziff, Ben Taylor. You will discuss about the future of AI & what the world of future could look like, learn about the different approaches to Deep Learning revolution, and also get to know the consequences of the oncoming AI developments.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/85

SDS 085: The AI Revolution – What the Future Will Look Like

In this episode of the SuperDataScience Podcast, I chat with one of the key people behind the Python package scikit-learn, Andreas Mueller. You will learn about gradient boosting algorithms, XGBoost, LightGBM and HistGradientBoosting. You will hear Andreas's approach to solving problems, what machine learning algorithms he prefers to apply to a given data science challenge, in which order and why. You will also hear about problems with Kaggle competitions. You will find out the four key questions that Andreas recommends to ask when you have a data challenge in front of you. You will learn about his 95% rule to creating models, and creating success in business enterprises with the help of machine learning. And, finally, you will also learn about the Data Science Institute at Columbia University.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/283

SDS 283: Getting The Most Out of Data With Gradient Boosting

How does your inner voice compare to your passions?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/290

SDS 290: The Passion Paradox

In this episode of the SuperDataScience Podcast, I chat with founder and CEO at Daisy Intelligence, Gary Saarenvirta. You'll learn about dangerous implicit assumptions, the power of theory and theory versus data. You'll also learn about two types of decisions,  the spacial interaction model, traffic flow model, the concept of dividing the world in two and what humans should be doing, and what artificial intelligence should be doing. You will hear about the difference between artificial intelligence that leverages just data versus artificial intelligence that leverages theory and data, and what advantages that creates.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/291

SDS 291: Changing the World With Theory & Data

How can you find a way to balance your energy through recharging in the way that works best for you?  

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/292

SDS 292: Introverts and Extroverts

In this episode of the SuperDataScience Podcast, I chat with Data Scientist, Peyman Hesami. You will find out what reinforcement learning is and how it works on an intuitive level. You will hear about the differences between reinforcement learning versus classification, or other supervised learning methods, and how it's used for personalization specifically. You will learn about six distinct advantages of reinforcement learning, what role reinforcement learning is going to play in the future of machine learning and why. Also, you will find out how and why Peyman made a career transition to work for a startup, how he's using reinforcement learning, and what is the biggest mistake he has made with reinforcement learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/293

SDS 293: True Personalization Through Reinforcement Learning

In this episode of the SuperDataScience Podcast, I chat with my friend and business partner, Hadelin de Ponteves. You will hear what new exciting things are happening in Hadelin's life now. You will hear some preview of his upcoming presentation at DataScienceGO 2019, which will cover NLP, especially the BERT model, which raised a whole new level in NLP. You will also learn about reinforcement learning and Hadelin's new course on Twin-Delayed DDPG.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/295

SDS 295: A Deep Conversation About Tech & Life

I sat down with Morgan Mendis whom I met at DSGO this year. He is one of the most advanced data scientists I’ve met and he’s been using his skills and experience to give back to his community. We discuss his career, his dreams, his ideology, and his hunt for a VP of Data Science at his former company.

In this episode you will hear:
• Catch up since DSGO 2019 [8:04]
• VP of Data Science at Inspire [12:00]
• Morgan’s career dreams [22:04]
• Morgan’s experience [30:50]
• Tools & solutions [1:01:33]
• How you can get involved [1:12:45]

Additional materials: www.superdatascience.com/321

SDS 321: The Life of One Advanced Data Scientist

I chatted with top Upwork freelancer Wesley Engers who has worked over 150 jobs in data science. He’s worked in a variety of industries and shared a few of his most interesting jobs and offered advice for those considering diving into freelance data science work.

In this episode you will learn:
• Wesley on Upwork [9:11] 
• Wesley’s background [16:20]
• How Wesley onboards a client [26:32]
• Good clients vs. bad clients [31:12]
• Tools [37:23]
• Wesley’s best projects [45:09]
• Freelance vs. full-time work [59:26]
• Tips about getting into Upwork [1:06:48]

Additional materials: www.superdatascience.com/323

SDS 323: Data Science as a Freelance Career

In today’s FiveMinuteFriday episode, I wanted to experience explaining support vector regression without the use of any visual aids.

Additional materials: www.superdatascience.com/370

SDS 370: What is Support Vector Regression (SVR)?

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

SDS 283: Getting The Most Out of Data With Gradient Boosting

Super Data Science: ML & AI Podcast with Jon Krohn