ratings
Apache Spark, a significant component in the Hadoop Ecosystem, is a cluster computing engine used in Big Data. Building on top of the Hadoop YARN and HDFS ecosystem, offers order-of-magnitude faster processing for many in-memory computing tasks compared to Map/Reduce. It can be programmed in Java, Scala, Python, and R - the favorite languages of Data Scientists - along with SQL-based front ends.
Unlimited Duration
March 5, 2021
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.In this course you learn about:
· Learn popular machine learning algorithms, their applicability, and limitations
· Practice the application of these methods in the Spark machine learning environment
· Learn practical use cases and limitations of algorithms
· Will explore not just the related APIs, but will also learn the theory behind them
· Work with real world datasets from Uber, Netflix, Walmart, Prosper, etc.
Course Curriculum
-
-
- Machine Learning landscape 00:00:00
- Machine Learning applications 00:00:00
- Understanding ML algorithms & models 00:00:00
- Spark ML Overview 00:00:00
- Introduction to Jupyter notebooks 00:00:00
- Lab: Working with Jupyter + Python + Spark 00:00:00
- Lab: Spark ML utilities 00:00:00
- Preparing data for ML 00:00:00
- Extracting features, enhancing data 00:00:00
- Data cleanup 00:00:00
- Visualizing Data 00:00:00
- Lab: data cleanup 00:00:00
- Lab: visualizing data 00:00:00
- Understanding Logistic Regression 00:00:00
- Calculating Logistic Regression 00:00:00
- Evaluating model performance 00:00:00
- Lab: Use case: credit card application, college admissions 00:00:00
- Theory behind trees 00:00:00
- Classification and Regression Trees (CART) 00:00:00
- Random Forest concepts 00:00:00
- Labs: Use case: predicting loan defaults, estimating election contributions 00:00:00
- Theory behind K-Means 00:00:00
- Running K-Means algorithm 00:00:00
- Estimating the performance 00:00:00
- Lab: Use case: grouping cars data, grouping shopping data 00:00:00
- Recommender systems overview 00:00:00
- Collaborative Filtering concepts 00:00:00
- Lab: Use case: movie recommendations, music recommendations 00:00:00
- Tensorflow intro 00:00:00
- Tensorflow Features 00:00:00
- Tensorflow Versions 00:00:00
- GPU and TPU scalability 00:00:00
- Lab: Setting up and Running Tensorflow 00:00:00
- Introducing Perceptrons 00:00:00
- Linear Separability and Xor Problem 00:00:00
- Activation Functions 00:00:00
- Backpropagation, loss functions, and Gradient Descent 00:00:00
- Lab: Single-Layer Perceptron in Tensorflow 00:00:00
- Using high level tensorflow 00:00:00
- Developing a model with tf.learn 00:00:00
- Lab: Developing a tf.learn model 00:00:00
- What is Keras? 00:00:00
- Using Keras with a Tensorflow Backend 00:00:00
- Lab: Example with a Keras 00:00:00
- Introducing RNNs 00:00:00
- RNNs in Tensorflow 00:00:00
- Lab: RNN 00:00:00
Course Reviews

Students