For more information contact us      Phone: +1-425-748-5018   Fax: +1-425-644-2185   TrainingUSA@selagroup.com Coming courses Register/More info More courses from Big Data  Deep Learning for Software Developers (of Machine Learning) Machine Learning Fundamentals Elastic Stack Workshop Using solr search engine Big Data for Decision Makers Machine Learning for Software Developers Big Data with Hadoop and Spark Testing the Internet of Things (IoT) – An Exploratory Workshop

MLFund - Version: 1 Machine Learning Fundamentals 4 days course Description In this 4-day course you will learn how to use several machine learning algorithms. We will start with simple linear regression and work our way towards deep neural networks. we will also learn how to handle our data and create efficient data sets that will help our machine learning process be faster and better. Intended audience This course is intended for Software engineers as well as decision makers in the organization. Prerequisites Knowledge and experience in Python Objectives Understand the purpose of each machine learning algorithm. Use Several Mahcine learning algorithms. Avoid common pitfalls Create efficient Data Sets Topics Module 01 - What is Machine Learning? What is machine learning good for Where is machine learning used Terminology Module 02 – Regression Linear Regression Calculating and reducing Loss Intro to Pandas Intro to TensorFlow Module 03 – Generalization Overfitting and how to avoid it Creating your data sets Module 04 - Feature Engineering Mapping Numerical and categorical values Multi and One hot encoding Scaling Binning Data Verification Module 05 - Non Linear Features Feature Crosses Module 06 – Regularization Measuring Complexity L2 regulatization Lambda Module 07 - Logistic Regression Understanding Logistic Regression Logistic Regression Loss Regularization Module 08 – Classification Threasholding Expanding the true false notion Accuracy, Precision and Recall Module 09 – Sparsity Handling huge sparse vectors L1 regularization Module 10 - Neural Nets Non Linear Problems Hidden Layers Activation Functions Common Failures Regularization MultiClass Neural Networks SoftMax Module 11 - Embeddings Collaborative Filtering Reducing Dimensions Wotd2Vec

MLFund Course
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