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Machine Learning

    Deep Learning (with TensorFlow, Keras and PyTorch)

    This course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, Keras, and PyTorch — the leading Deep Learning libraries. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across all of the contemporary families, including:

    • Convolutional Networks for machine vision
    • Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis
    • Generative Adversarial Networks for producing jaw-dropping synthetic data
    • Reinforcement Learning for complex sequential decision-making
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    Data Science with Python: Machine Learning

    This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

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    Data Science with R: Machine Learning

    This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.

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    Data Science Bootcamp Deposit

    Deposit for 12-week immersive data science bootcamp. In this program students will learn beginner and intermediate levels of Data Science with R, Python, Spark and Hadoop as well as widely used industry tools such as Selenium, Caret, Tensorflow, MongoDB, AWS, and more. A deposit of $5,000 is required within 7 days of acceptance to secure your spot. After making your deposit, the remaining tuition is due on the first day of the Bootcamp.

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