Deep Learning Prerequisites: Logistic Regression in Python

120,00  10,00 

Deep Learning Prerequisites: Logistic Regression in Python. Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python



The name of this course is Deep Learning Prerequisites: Logistic Regression in Python. The knowledge you will get with this indescribable online course is astonishing. Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Lazy Programmer Inc., one of the very best experts in this field.

Description of this course: Deep Learning Prerequisites: Logistic Regression in Python

Course Description This course is a lead-in to deep learning and neural networks – it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone’s emotions just based on a picture! If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you. This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: logistic_regression_class Make sure you always “git pull” so you have the latest version! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: calculuslinear algebraprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV file TIPS (for getting through the course): Watch it at 2x.Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. USEFUL COURSE ORDERING: (The Numpy Stack in Python)Linear Regression in PythonLogistic Regression in Python(Supervised Machine Learning in Python)(Bayesian Machine Learning in Python: A/B Testing)Deep Learning in PythonPractical Deep Learning in Theano and TensorFlow(Supervised Machine Learning in Python 2: Ensemble Methods)Convolutional Neural Networks in Python(Easy NLP)(Cluster Analysis and Unsupervised Machine Learning)Unsupervised Deep Learning(Hidden Markov Models)Recurrent Neural Networks in PythonNatural Language Processing with Deep Learning in Python

Requirements of this course: Deep Learning Prerequisites: Logistic Regression in Python

What are the requirements? You should know how to take a derivative You should know some basic Python coding Install numpy and matplotlib

What will you learn in this course: Deep Learning Prerequisites: Logistic Regression in Python?

What am I going to get from this course? program logistic regression from scratch in Python describe how logistic regression is useful in data science derive the error and update rule for logistic regression understand how logistic regression works as an analogy for the biological neuron use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition understand why regularization is used in machine learning

Target audience of this course: Deep Learning Prerequisites: Logistic Regression in Python

Who is the target audience? Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are interested in pursuing statistics and coding in Python instead of R People who know some machine learning but want to be able to relate it to fabricado intelligence People who are interested in bridging the gap between computational neuroscience and machine learning

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