Learn Machine Learning #1
Video tutorial goes over Machine Learning, Supervised Learning, Unsupervised Learning, Modeling, Classification, Regression. (ML #1)
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Modeled after the Stanford University CS221/229 A.I. principles course, the Learn ML video lecture series is aimed at developing viewer’s computer science skills in the expansive fields of Machine Learning and Deep Learning. Viewers will cover both theory and implementation extensively through a variety of ML/DL tutorials and seminars. The first section is aimed at developing the skills to work with Machine Learning algorithms and analyzing them mathematically. We will cover regression and classification, sci-kit-learn, machine learning libraries, k-means clustering, Bayes ML, k-nearest neighbors, and decision trees. Later, we will jump into Deep Learning and begin with discussing Artificial Neural Networks, Perceptron, and the math behind large networks. We will cover Convolutional Neural Networks, Deep Learning Frameworks (Tensorflow, PyTorch, and Keras), LSTMs, RNNs, Reinforcement Learning/Transfer Learning, and more. Prerequisites include basic knowledge of probability, linear algebra (feature vectors, weights, matrices), and programming ability (python is preferred).
