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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.

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March 5, 2021

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Certification

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
      • Statistics Primer 00:00:00
      • Covariance, Correlation, Covariance Matrix 00:00:00
      • Errors, Residuals 00:00:00
      • Overfitting / Underfitting 00:00:00
      • Cross-validation, bootstrapping 00:00:00
      • Confusion Matrix 00:00:00
      • ROC curve, Area Under Curve (AUC) 00:00:00
      • Lab: Basic stats 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
      • Simple Linear Regression 00:00:00
      • Multiple Linear Regression 00:00:00
      • Running LR 00:00:00
      • Evaluating LR model performance 00:00:00
      • Lab 00:00:00
      • Use case: House price estimates 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
      • SVM concepts and theory 00:00:00
      • SVM with kernel 00:00:00
      • Lab: Use case: Customer churn data 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 00:00:00
      • Lab 00:00:00
      • Use case: spam filtering 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
      • Understanding PCA concepts 00:00:00
      • PCA applications 00:00:00
      • Running a PCA algorithm 00:00:00
      • Evaluating results 00:00:00
      • Lab: Use case: analyzing retail 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
      • Best practices for scaling and optimizing Apache Spark 00:00:00
      • Memory caching 00:00:00
      • Testing and validation 00:00:00
        • Supervised versus Unsupervised Learning 00:00:00
        • Regression 00:00:00
        • Classification 00:00:00
        • Clustering 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 Tensors 00:00:00
        • Tensorflow Execution Model 00:00:00
        • Lab: Learning about Tensors 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
        • Hidden Layers as a solution to XOR problem 00:00:00
        • Distributed Training with Tensorflow 00:00:00
        • Vanishing Gradient Problem and ReLU 00:00:00
        • Loss Functions 00:00:00
        • Lab: Feedforward Neural Network Classifier 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
        • Introducing CNNs 00:00:00
        • CNNs in Tensorflow 00:00:00
        • Lab : CNN apps 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
        • Introducing RNNs 00:00:00
        • RNNs in Tensorflow 00:00:00
        • Lab: RNN 00:00:00
        • Summarize features and advantages of Tensorflow 00:00:00
        • Summarize Deep Learning and How Tensorflow can help 00:00:00
        • Next steps 00:00:00

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