Machine Learning, Data Science and Deep Learning with Python
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
What you’ll learn
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Build artificial neural networks with Tensorflow and Keras
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Classify images, data, and sentiments using deep learning
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Make predictions using linear regression, polynomial regression, and multivariate regression
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Data Visualization with MatPlotLib and Seaborn
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Implement machine learning at massive scale with Apache Spark’s MLLib
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Understand reinforcement learning – and how to build a Pac-Man bot
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Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
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Use train/test and K-Fold cross validation to choose and tune your models
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Build a movie recommender system using item-based and user-based collaborative filtering
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Clean your input data to remove outliers
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Design and evaluate A/B tests using T-Tests and P-Values
Requirements
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You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
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Some prior coding or scripting experience is required.
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At least high school level math skills will be required.
Who this course is for:
- Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
- Technologists curious about how deep learning really works
- Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.
- If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.
