• Class and Course

    Hands on Machine Learning for Geoscientists and Engineers

    Learn and apply fundamental machine learning concepts with Petro-technical applications. In this course you will be exposed to upstream projects and develop the required skills to deploy data scientist approaches in end-to-end machine learning pipelines. To be eligible for this course, you do not need to have any prior knowledge of programming or data science. At the end of this course, you will be able to judge if you can take data-oriented approaches for your geoscience and engineering problems. In addition, you will learn techniques to make sure your data is clean and prepared for modeling. You will also learn how to perform exploratory data analysis. Finally, you will practice the steps to design, run and validate your modeling pipeline.

    -           What Is Machine Learning?
    -           Common Libraries Used
    -           Virtual Environment
    -           Overview of Machine Learning Pipelines
    -           SME (Subject Matter Expertise)
    -           Feature Engineering

    Python crash course
    -           Variables
    -           List/Dictionaries
    -           Loops
    -           Conditional statements
    -           Functions

    -           Filtering
    -           Indexing
    -           Slicing

    -           Data Cleaning
    -           Data Manipulation
    -           Exploratory Data Analysis
    -           Built in Plots

    Overview of Machine Learning
    -           Intuition of Fundamental ML Algorithms
    -           Types of Learning
    -           Bias-Variance Trade Off
    -           Shallow or Deep
    -           Learning Curves
    -           How to Select Features

    -           When do We Use Clustering?
    -           How to Evaluate Clusters

    Feature reduction and feature generation

    Regression problems
    -           Error Definition
    -           Model Validation
    -           K-Fold
    -           Cross Validation

    -           Classification Metrics
    -           Confusion matrix
    -           Grid Search
    -           Randomized Grid Search

    Advance topics:
    -           Multi-index DataFrame for multi-variable time series
    -           Pipeline
    -           Multi Output Models
    -           Staking and Voting

    -           Volumetric Calculations with NumPy
    -           Formation Evaluation and EDA with Pandas
    -           Type Well Analysis with Pandas (Optional)
    -           Facies Analysis and Clustering (Optional)
    -           Log Prediction
    -           Production Prediction

    Upcoming Classes

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