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Machine Learning & Deep Learning Essentials with Spark and TensorFlow

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This course is intended for data scientists and software engineers, and assumes attendees have little or no previous experience with Machine Learning. This course explores popular machine learning algorithms from the ground up.

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Unlimited Duration

Last Updated

March 3, 2021

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Posted by

ashar hafeez

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 will 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
        • Understanding Machine Learning 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
        • Softmax output 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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