Machine Learning and AI Bootcamp includes fundamental concepts of machine learning and AI systems, as well as neural networks and deep learning. You will learn the key concepts, the intuition, and the tools that you will need to implement intelligent systems capable of learning from data using production ready open source implementations of modern algorithms. Expect to use Scikit-learn, Tensorflow and Keras running in a Jupyter notebook.

This course is offered through Cloud Genius.

• High school level math, including probability and linear algebra

Fundamentals of Machine Language (ML) and Artificial Intelligence (AI)

• What is Machine Learning
• What problems does it try to solve?
• What are the main categories and fundamental concepts of Machine Learning systems?
• The main steps in a typical Machine Learning project
• Learning by fitting a model to data
• Optimizing a cost function
• Handling, cleaning, and preparing data
• Selecting and engineering features
• Selecting a model and tuning hyperparameters using cross-validation
• The main challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff)
• Reducing the dimensionality of the training data to fight the curse of dimensionality
• The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests and Ensemble methods

Neural Networks & Deep Learning

• What are neural nets? What are they good for?
• Building and training neural nets using TensorFlow
• The most important neural net architectures: feedforward neural nets convolutional nets, recurrent nets, long short-term memory (LSTM) nets and autoencoders
• Techniques for training deep neural nets
• Scaling neural networks for huge datasets
• Reinforcement learning