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MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO WRITE THE PERFECT DATA SCIENTIST RESUME Write down your most impressive experiences and then list preliminary bullet points underneath. Then, fill in all the other sections such as your contact info, education background, skills, etc. Now you have a quick “version 0.1” of your resume. It’s not ready to be submitted, but at least it has all the pieces. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 21 MACHINE LEARNING INTERVIEW QUESTIONS AND ANSWERS 21 Machine Learning Interview Questions and Answers. If you want to land a job in data science, you’ll need to pass a rigorous and competitive interview process. In fact, most top companies will have at least 3 rounds of interviews. During the process, you’ll be tested for a variety of skills, including: Your technical andprogramming skills.
5 GENIUS PYTHON DEEP LEARNING LIBRARIES Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARESEE MORE ON ELITEDATASCIENCE.COM LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCES Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO WRITE THE PERFECT DATA SCIENTIST RESUME Write down your most impressive experiences and then list preliminary bullet points underneath. Then, fill in all the other sections such as your contact info, education background, skills, etc. Now you have a quick “version 0.1” of your resume. It’s not ready to be submitted, but at least it has all the pieces. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 21 MACHINE LEARNING INTERVIEW QUESTIONS AND ANSWERS 21 Machine Learning Interview Questions and Answers. If you want to land a job in data science, you’ll need to pass a rigorous and competitive interview process. In fact, most top companies will have at least 3 rounds of interviews. During the process, you’ll be tested for a variety of skills, including: Your technical andprogramming skills.
5 GENIUS PYTHON DEEP LEARNING LIBRARIES Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARESEE MORE ON ELITEDATASCIENCE.COM HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. HOW TO LEARN MACHINE LEARNING, THE SELF-STARTER WAY 6. Tie each algorithm to value. For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist. 7. Don't believe the hype. Machine learning is not what the movies portray as artificial intelligence. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TO Cross-validation. Cross-validation is a powerful preventative measure against overfitting. The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model. In standard k-fold cross-validation, we partition the data into k subsets, called folds. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by FEATURE ENGINEERING FOR MACHINE LEARNING Feature engineering can substantially boost machine learning model performance. It's how data scientists can leverage domain knowledge. But where do you start? This guide takes you step-by-step through creating new input features, tightening up your dataset, and HOW TO LEARN PYTHON FOR DATA SCIENCE (UPDATED) Step 3: End-to-End Projects. By now, you'll have a basic understanding of programming and a working knowledge of essential libraries. This actually covers most of the Python you'll need to get started with data science. At this point, some students will feel a bit overwhelmed. That's OK, and it's perfectly normal. THE 5 LEVELS OF MACHINE LEARNING ITERATION Everything is easier and faster on the second try, and this is the best way to see your progress. #5. Cycle between theory, practice, and projects. We believe the most effective way to learn machine learning is by cycling between theory, targeted practice, and larger projects. HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. 8 FUN MACHINE LEARNING PROJECTS FOR BEGINNERS There are plenty of fun machine learning projects for beginners. For example, you could try. Sports betting. Predict box scores given the data available at the time right before each new game. Talent scouting. Use college statistics to predict which players would have the best professional careers. General managing. 5 TASTY PYTHON WEB SCRAPING LIBRARIES For example, if you wanted to find all the links in the web page we pulled down earlier, it’s only a few lines: Python. 1. 2. 3. from bs4 import BeautifulSoup. soup = BeautifulSoup(contents, 'html.parser') soup.find_all('a') This charming simplicity has made it one of the most beloved Python LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCES Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO WRITE THE PERFECT DATA SCIENTIST RESUME Write down your most impressive experiences and then list preliminary bullet points underneath. Then, fill in all the other sections such as your contact info, education background, skills, etc. Now you have a quick “version 0.1” of your resume. It’s not ready to be submitted, but at least it has all the pieces. LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCES Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO WRITE THE PERFECT DATA SCIENTIST RESUME Write down your most impressive experiences and then list preliminary bullet points underneath. Then, fill in all the other sections such as your contact info, education background, skills, etc. Now you have a quick “version 0.1” of your resume. It’s not ready to be submitted, but at least it has all the pieces. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while DATA CLEANING STEPS AND TECHNIQUES The steps and techniques for data cleaning will vary from dataset to dataset. As a result, it's impossible for a single guide to cover everything you might run into. However, this guide provides a reliable starting framework that can be used every time.We cover common steps such as fixing structural errors, handling missing data, and filteringobservations.
MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM PYTHON CHEAT SHEET FOR DATA SCIENCESEE MORE ON ELITEDATASCIENCE.COM OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARESEE MORE ON ELITEDATASCIENCE.COM HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. HOW TO LEARN STATISTICS FOR DATA SCIENCE, THE SELF-STARTER WAY Here are the 3 steps to learning the statistics and probability required for data science: 1. Core Statistics Concepts. Descriptive statistics, distributions, hypothesis testing, and regression. 2. Bayesian Thinking. Conditional probability, priors, posteriors, and maximum likelihood. 3. FEATURE ENGINEERING FOR MACHINE LEARNING Feature engineering can substantially boost machine learning model performance. It's how data scientists can leverage domain knowledge. But where do you start? This guide takes you step-by-step through creating new input features, tightening up your dataset, and OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TO Cross-validation. Cross-validation is a powerful preventative measure against overfitting. The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model. In standard k-fold cross-validation, we partition the data into k subsets, called folds. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. 8 FUN MACHINE LEARNING PROJECTS FOR BEGINNERS There are plenty of fun machine learning projects for beginners. For example, you could try. Sports betting. Predict box scores given the data available at the time right before each new game. Talent scouting. Use college statistics to predict which players would have the best professional careers. General managing. 15 WAYS MACHINE LEARNING WILL IMPACT YOUR EVERYDAY LIFE Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Here are 15 fun, exciting, and mind-boggling ways machine learning will impact your everyday life. 5 TASTY PYTHON WEB SCRAPING LIBRARIES For example, if you wanted to find all the links in the web page we pulled down earlier, it’s only a few lines: Python. 1. 2. 3. from bs4 import BeautifulSoup. soup = BeautifulSoup(contents, 'html.parser') soup.find_all('a') This charming simplicity has made it one of the most beloved Python LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCES Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 5 GENIUS PYTHON DEEP LEARNING LIBRARIES Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSES Dimensionality Reduction Algorithms: Strengths and Weaknesses. Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we’ll cover methods for Dimensionality Reduction, further broken into Feature Selection and Feature Extraction. In general, these tasks are rarely performed in THE 5 LEVELS OF MACHINE LEARNING ITERATION Everything is easier and faster on the second try, and this is the best way to see your progress. #5. Cycle between theory, practice, and projects. We believe the most effective way to learn machine learning is by cycling between theory, targeted practice, and larger projects. LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCES Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 5 GENIUS PYTHON DEEP LEARNING LIBRARIES Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSES Dimensionality Reduction Algorithms: Strengths and Weaknesses. Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we’ll cover methods for Dimensionality Reduction, further broken into Feature Selection and Feature Extraction. In general, these tasks are rarely performed in THE 5 LEVELS OF MACHINE LEARNING ITERATION Everything is easier and faster on the second try, and this is the best way to see your progress. #5. Cycle between theory, practice, and projects. We believe the most effective way to learn machine learning is by cycling between theory, targeted practice, and larger projects. MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MACHINE LEARNING, THE SELF-STARTER WAY 6. Tie each algorithm to value. For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist. 7. Don't believe the hype. Machine learning is not what the movies portray as artificial intelligence. THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by HOW TO LEARN PYTHON FOR DATA SCIENCE (UPDATED) Step 3: End-to-End Projects. By now, you'll have a basic understanding of programming and a working knowledge of essential libraries. This actually covers most of the Python you'll need to get started with data science. At this point, some students will feel a bit overwhelmed. That's OK, and it's perfectly normal. PYTHON CHEAT SHEET FOR DATA SCIENCE Python Cheat Sheet for Data Science. Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python. Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python. Scikit-Learn comes with many machine learningmodels that
8 FUN MACHINE LEARNING PROJECTS FOR BEGINNERS There are plenty of fun machine learning projects for beginners. For example, you could try. Sports betting. Predict box scores given the data available at the time right before each new game. Talent scouting. Use college statistics to predict which players would have the best professional careers. General managing. THE ULTIMATE PYTHON SEABORN TUTORIAL: GOTTA CATCH 'EM ALL 1. First, understand the basics and paradigms of the library. Each library approaches data visualization differently, so it's important to understand how Seaborn "thinks about" the problem. 2. Then, fire up a dataset for practice. Learning in context is the best way to master a new skill quickly. 3. 15 WAYS MACHINE LEARNING WILL IMPACT YOUR EVERYDAY LIFE Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Here are 15 fun, exciting, and mind-boggling ways machine learning will impact your everyday life. OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARE Advantages of Open Source. Python and R are programming languages with rich open source ecosystems. For example, Python has Scikit Learn, a powerful general-purpose framework that can run classification, regression, clustering, and other tasks out of the box. Python also has specialized packages for deep learning and NLP, such as TensorFlow, Theano, and Keras. LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCESELITE DATA CORPELITE DATASERVICES
Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 5 GENIUS PYTHON DEEP LEARNING LIBRARIESPYTHON DEEP LEARNING FRAMEWORKPYTHON DEEP LEARNING TUTORIALPYTHON DEEP LEARNING EXAMPLEDEEP LEARNING LIBRARY PYTHONDEEP LEARNING PYTHON PDFDEEP LEARNING LIBRARY Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSESDIMENSIONALITY REDUCTION PYTHONDATA DIMENSIONALITY REDUCTIONDIMENSION REDUCTION IN MACHINE LEARNINGLDA DIMENSIONALITYREDUCTION
Dimensionality Reduction Algorithms: Strengths and Weaknesses. Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we’ll cover methods for Dimensionality Reduction, further broken into Feature Selection and Feature Extraction. In general, these tasks are rarely performed in THE 5 LEVELS OF MACHINE LEARNING ITERATION Everything is easier and faster on the second try, and this is the best way to see your progress. #5. Cycle between theory, practice, and projects. We believe the most effective way to learn machine learning is by cycling between theory, targeted practice, and larger projects. LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCESELITE DATA CORPELITE DATASERVICES
Step 6: Launch your high-growth career in DS. There’s always more to learn when it comes to data science. But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. I was looking for the link between the concepts and the real steps of a data science job. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY You don’t need to read a whole textbook, but you’ll want to learn the key concepts first. Here are the 3 steps to learning the math required for data science and machine learning: 1. Linear Algebra for Data Science. Matrix algebra and eigenvalues. 2. Calculus for Data Science. Derivatives and gradients. 3. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while HOW TO HANDLE IMBALANCED CLASSES IN MACHINE LEARNING Next, we'll look at the first technique for handling imbalanced classes: up-sampling the minority class. 1. Up-sample Minority Class. Up-sampling is the process of randomly duplicating observations from the minority class in order to reinforce its signal. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM 5 GENIUS PYTHON DEEP LEARNING LIBRARIESPYTHON DEEP LEARNING FRAMEWORKPYTHON DEEP LEARNING TUTORIALPYTHON DEEP LEARNING EXAMPLEDEEP LEARNING LIBRARY PYTHONDEEP LEARNING PYTHON PDFDEEP LEARNING LIBRARY Keras is a heavyweight wrapper for both Theano and Tensorflow. It’s minimalistic, modular, and awesome for rapid experimentation. This is our favorite Python library for deep learning and the best place to start for beginners. MXNet is another high-level library similar to Keras. It offers bindings for multiple languages and support for DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSESDIMENSIONALITY REDUCTION PYTHONDATA DIMENSIONALITY REDUCTIONDIMENSION REDUCTION IN MACHINE LEARNINGLDA DIMENSIONALITYREDUCTION
Dimensionality Reduction Algorithms: Strengths and Weaknesses. Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we’ll cover methods for Dimensionality Reduction, further broken into Feature Selection and Feature Extraction. In general, these tasks are rarely performed in THE 5 LEVELS OF MACHINE LEARNING ITERATION Everything is easier and faster on the second try, and this is the best way to see your progress. #5. Cycle between theory, practice, and projects. We believe the most effective way to learn machine learning is by cycling between theory, targeted practice, and larger projects. MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MACHINE LEARNING, THE SELF-STARTER WAY 6. Tie each algorithm to value. For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist. 7. Don't believe the hype. Machine learning is not what the movies portray as artificial intelligence. THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by HOW TO LEARN PYTHON FOR DATA SCIENCE (UPDATED) Step 3: End-to-End Projects. By now, you'll have a basic understanding of programming and a working knowledge of essential libraries. This actually covers most of the Python you'll need to get started with data science. At this point, some students will feel a bit overwhelmed. That's OK, and it's perfectly normal. PYTHON CHEAT SHEET FOR DATA SCIENCE Python Cheat Sheet for Data Science. Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python. Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python. Scikit-Learn comes with many machine learningmodels that
8 FUN MACHINE LEARNING PROJECTS FOR BEGINNERS There are plenty of fun machine learning projects for beginners. For example, you could try. Sports betting. Predict box scores given the data available at the time right before each new game. Talent scouting. Use college statistics to predict which players would have the best professional careers. General managing. THE ULTIMATE PYTHON SEABORN TUTORIAL: GOTTA CATCH 'EM ALL 1. First, understand the basics and paradigms of the library. Each library approaches data visualization differently, so it's important to understand how Seaborn "thinks about" the problem. 2. Then, fire up a dataset for practice. Learning in context is the best way to master a new skill quickly. 3. 15 WAYS MACHINE LEARNING WILL IMPACT YOUR EVERYDAY LIFE Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Here are 15 fun, exciting, and mind-boggling ways machine learning will impact your everyday life. OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARE Advantages of Open Source. Python and R are programming languages with rich open source ecosystems. For example, Python has Scikit Learn, a powerful general-purpose framework that can run classification, regression, clustering, and other tasks out of the box. Python also has specialized packages for deep learning and NLP, such as TensorFlow, Theano, and Keras. LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCESELITE DATA CORPELITE DATASERVICES
EliteDataScience.com helps busy people streamline the path to becoming a data scientist. You'll discover how to shorten the learning curve, future-proof your career, and land a high-paying job in data science. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY Don't worry too much about the nuances of neural networks for now. It's ok if you're just following instructions and writing code. We'll cover machine learning in depth in another guide, as this is for targeted math practice. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSESDIMENSIONALITY REDUCTION PYTHONDATA DIMENSIONALITY REDUCTIONDIMENSION REDUCTION IN MACHINE LEARNINGLDA DIMENSIONALITYREDUCTION
A Quora user has provided an excellent analogy for the Curse of Dimensionality, which we'll borrow here:. Let's say you have a straight line 100 yards long and you dropped a penny somewhere on it. It wouldn't be too hard to find. You walk along the line and it takestwo minutes.
THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while 5 GENIUS PYTHON DEEP LEARNING LIBRARIESPYTHON DEEP LEARNING FRAMEWORKPYTHON DEEP LEARNING TUTORIALPYTHON DEEP LEARNING EXAMPLEDEEP LEARNING LIBRARY PYTHONDEEP LEARNING PYTHON PDFDEEP LEARNING LIBRARY Deep learning is an exciting subfield at the cutting edge of machine learning and artificial intelligence. Deep learning has led to major breakthroughs in exciting subjects just such computer vision, audio processing, and even self-driving cars. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM LAUNCH YOUR CAREER IN DATA SCIENCEGUIDESCONCEPT EXPLAINERSCODE TUTORIALSCAREER HELPTOOLS & RESOURCESELITE DATA CORPELITE DATASERVICES
EliteDataScience.com helps busy people streamline the path to becoming a data scientist. You'll discover how to shorten the learning curve, future-proof your career, and land a high-paying job in data science. 65 FREE DATA SCIENCE RESOURCES FOR BEGINNERS In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.. To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to businessanalytics.
MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MATH FOR DATA SCIENCE, THE SELF-STARTER WAY Don't worry too much about the nuances of neural networks for now. It's ok if you're just following instructions and writing code. We'll cover machine learning in depth in another guide, as this is for targeted math practice. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by DIMENSIONALITY REDUCTION ALGORITHMS: STRENGTHS AND WEAKNESSESDIMENSIONALITY REDUCTION PYTHONDATA DIMENSIONALITY REDUCTIONDIMENSION REDUCTION IN MACHINE LEARNINGLDA DIMENSIONALITYREDUCTION
A Quora user has provided an excellent analogy for the Curse of Dimensionality, which we'll borrow here:. Let's say you have a straight line 100 yards long and you dropped a penny somewhere on it. It wouldn't be too hard to find. You walk along the line and it takestwo minutes.
THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. 9 MISTAKES TO AVOID WHEN STARTING YOUR CAREER IN DATA SCIENCE If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes. If you’re not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation. We’ve broken them into three categories: Mistakes while 5 GENIUS PYTHON DEEP LEARNING LIBRARIESPYTHON DEEP LEARNING FRAMEWORKPYTHON DEEP LEARNING TUTORIALPYTHON DEEP LEARNING EXAMPLEDEEP LEARNING LIBRARY PYTHONDEEP LEARNING PYTHON PDFDEEP LEARNING LIBRARY Deep learning is an exciting subfield at the cutting edge of machine learning and artificial intelligence. Deep learning has led to major breakthroughs in exciting subjects just such computer vision, audio processing, and even self-driving cars. OVERFITTING IN MACHINE LEARNING: WHAT IT IS AND HOW TOSEE MORE ON ELITEDATASCIENCE.COM MODEL TRAINING WITH MACHINE LEARNING Since we've already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we'll discuss, and these have become widely-accepted best practices in the field.. Again, this mini-course is meant to be a gentle introduction to data science and machine learning, so we won't get into the nitty gritty yet. HOW TO LEARN MACHINE LEARNING, THE SELF-STARTER WAY Now, some people may be wondering: "If I don't plan to perform original research, why would I need to learn the theory when I can just use existing ML packages?" This is a reasonable question! However, learning the fundamentals is important for anyone who plans to apply machine learning in their work. Here are 5 super practical reasons for learning ML theory. THE 5 LEVELS OF MACHINE LEARNING ITERATION But we do hope to frame this simple concept in a way that might be new to you. Our goal is to walk through a tour of several essential concepts in ML, but to do so from a different perspective than the common approach taught in textbooks.. You see, most books focus on the sequential process for machine learning: load data, then preprocess it, then fit models, then make predictions, etc. MODERN MACHINE LEARNING ALGORITHMS: STRENGTHS AND WEAKNESSES 2.1. (Regularized) Logistic Regression. Logistic regression is the classification counterpart to linear regression. Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.. The models themselves are still "linear," so they work well when your classes are linearly separable (i.e. they can be separated by HOW TO LEARN PYTHON FOR DATA SCIENCE (UPDATED) In this guide, we’ll cover how to learn Python for data science, including our favorite curriculum for self-study. You see, data science is about problem solving, exploration, and PYTHON CHEAT SHEET FOR DATA SCIENCE This cheat sheet summarizes some of the most common functionality from Pandas and Scikit-Learn, two of the most useful Python libraries fordata science.
8 FUN MACHINE LEARNING PROJECTS FOR BEGINNERS In this guide, we’ll be walking through 8 fun machine learning projects for beginners. Projects are some of the best investments ofyour time.
15 WAYS MACHINE LEARNING WILL IMPACT YOUR EVERYDAY LIFE Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Here are 15 fun, exciting, and mind-boggling ways machine learning will impact your everyday life. OPEN SOURCE VS COMMERCIAL MACHINE LEARNING SOFTWARE Advantages of Open Source. Python and R are programming languages with rich open source ecosystems. For example, Python has Scikit Learn, a powerful general-purpose framework that can run classification, regression, clustering, and other tasks out of the box. Python also has specialized packages for deep learning and NLP, such as TensorFlow, Theano, and Keras. THE ULTIMATE PYTHON SEABORN TUTORIAL: GOTTA CATCH 'EM ALL In this step-by-step Seaborn tutorial, you’ll learn how to use one of Python’s most convenient libraries for data visualization. For those who’ve tinkered with Matplotlib before, you may have wondered, “why does it take me 10 lines of code just to make a decent-looking histogram?” Well, if you’re looking for a simpler way to plot attractive charts, then * Data Science Primer* Articles
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You'll discover how to shorten the learning curve, future-proof your career, and land a high-paying job in data science. Download the DS Career Guide FREE: DATA SCIENCE CAREER GUIDE How to Learn Data Science & Machine Learning, Land a High-Paying Job, and Future-Proof Your CareerSend Me The Guide!
No spam. No nonsense. LEARN DATA SCIENCE IN HALF THE TIME EliteDataScience.com helps you shorten the path to data science. We help you skip the non-essential and laser-focus on the skills and tools that will move the needle in your career. WE HELP TIME-CONSTRAINED PROFESSIONALS You might already see the rising role of data in today's economy. You might already be determined to learn DS and future-proof yourcareer...
Yet it feels like there's a looming mountain of topics you're "supposed" to learn for data science. And with only 24 hours in a day, where do you even begin? SIMPLIFY THE PATH TO DATA SCIENCE EliteDataScience.com helps you connect the dots between DS and real-world business value. You'll learn only the most relevant, commercially-viable tools and best practices. We skip the non-essential theory and math, and instead take you through the most direct path to applying DS and ML at a professionallevel.
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STEP 1: START BY UNDERSTANDING THE BIG PICTURE First, learn how all of the moving pieces fit together… Knowing the inputs, outputs, and core steps is the essential to driving real-world value with DS and ML.*
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STEP 6: LAUNCH YOUR HIGH-GROWTH CAREER IN DS There’s always more to learn when it comes to data science… But by following these streamlined steps, you’ll be more than qualified to get your foot in the door. Jumpstart your data science journey today! Enter your email to receive our FREE 4-PART CRASH COURSE on data science and applied machine learning. Enter your name here...Enter your email address here... __Send me the free crash course!*
> I WAS LOOKING FOR THE LINK BETWEEN THE CONCEPTS AND THE REAL STEPS > OF A DATA SCIENCE JOB. The way you approach the models/techniques is > a far better way than long and exhaustive statistical explanations. > I feel motivated to keep learning more detailed and complex models. > This is the "easy and gentle" way we can learn and apply data > science in our day to day problems. RAPHAEL PAIM Financial Analyst*
> ML is generally presented as though advanced math is required. Yet, > I knew who did not have an advanced math > background. This course is helpful for those with IT/analyst > background who are not looking to invest several years in math > courses, but would like to engage in ML. THE REALLY VALUABLE SKILL > IN THE NEAR FUTURE WILL ACTUALLY BE UNDERSTANDING THE PROCESS AND> THE RESULTS.
JESSICA STAHL University Professor*
> This is at least my 5th pass at learning this material (Thinkful > bootcamp, Andrew Ng Course, Data Camp, and a multitude of books). > WALKING THROUGH THE FULL PROCESS WAS SO VALUABLE. A LOT OF OTHER > PROGRAMS JUST FOCUS ON THE ALGORITHM IMPLEMENTATION. It's easy when > you are starting with the ABT. Now I'm confident to take on > predictive analytics projects at the office that no one has had the > expertise to tackle before. BEN WILSON BI Analyst* 1
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