SQL Tutorial Analytics and Data Sciences Machine Learning Prerequisites (Numpy) Pandas for Machine Learning Project - Churn Emails Inbox with Python Getting Started with Git Foundations of Python Project - Building Cat vs Non-Cat Image Classifier using NumPy and ANN Convolutional Neural Networks Training Deep Neural Networks Custom Models and Training with TensorFlow Loading and Preprocessing Data with TensorFlow Project - How to Build a Neural Network for Image Classification with TensorFlow What's Next? Project - Image Classification with Pre-trained Keras models Project - Mask R-CNN with OpenCV for Object Detection Git Version Control (Basic to Advanced) Lab Overview Project - Sentiment Analysis in Hive Support Vector Machines Training Models Decision Trees Ensemble Learning and XGBoost Dimensionality Reduction The underpinnings of ML Challenges in Machine Learning Project Unsupervised Machine Learning Generalized AI and Real Life Large Projects Project - Forecast Bike Rentals Design & Constructs Big Data Introduction Apache ZooKeeper MapReduce Programming Pig & Pig Latin Hive NoSQL HBase Sqoop Flume Oozie Scala Apache Spark Basics Writing Spark Applications DataFrames, Spark SQL, R Machine Learning With Spark Yarn MapReduce Basics Apache Spark - Key Value RDD Spark Streaming Spark On Cluster Adv Spark Programming Loading and Saving Data Java Essentials Spark Project - Log Parsing Graph Processing With Spark Project - Hive - Visualization with Tableau Project - Hive - NYSE Project - Predicting Titanic Passenger Survival using Machine Learning and Python Introduction to ML Artificial Neural Networks - MCQs Natural Language Processing - MCQs Reinforcement Learning Reinforcement Learning - MCQs End-to-End ML Project - California Housing Natural Language Processing Course Overview Classification Artificial Neural Networks Processing Sequences with RNNs and CNNs Autoencoders and GANs Session Recordings - Machine Learning and AI Getting Started with Numpy Getting Started with Pandas ML Prerequisites - Matplotlib End to End Project - Classification (Studio Recorded) Introduction to AI and OpenVINO™ Toolkit Setting up the environment for AI application development with OpenVINO™ Toolkit Expedite development of high-performance deep learning inference applications with Open Model Zoo Optimization and Quantization of Models for better performance Inference Engine & integration with Deep Learning applications Streamline AI Application Development with Deep Learning Workbench Advanced Labs Speed up the solution development journey with Intel® Edge Software Hub