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  • Building Machine Learning Models
  • ML Pipeline Design
  • Working with Imbalanced Datasets
  • Model Bias and Fairness Checking
  • Ability to Interpret ML Models

Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works?

In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle.

 

In this course we  will be introducing to you some extra things that is not covered in most machine learning courses - such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etc

We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.

We will learn about model interpretation and explanation. Certain ML models when used in production tend to be bias, hence in this course we will explore how to detect model fairness and bias.

 

By the end of the course you will have a comprehensive overview of extra concepts and tools in the entire machine learning project life cycle and things to consider when performing  a data science project.

This course is unscripted,fun and exciting but at the same time we dive deep into some extra aspects of the machine learning life cycle.

 

Specifically you will learn

  • Pipelines and their advantages.

  • How to build ML Pipelines with Scikit-Learn

  • How to build Spark NLP Pipelines

  • How to work with and fix Imbalanced Datasets

  • Model Fairness and Bias Detection

  • How to interpret and explain your Black Box Models using Lime,Eli5,etc

  • Incremental/Online Machine Learning Frameworks

  • Best practices in data science project

  • Model Deployment

  • Alternative ML Libraries eg TuriCreate,etc

  • how to track your ML experiments and more

  • etc

NB: This course will not cover CI/CD ML Pipelines

Join us as we explore the world of machine learning in python - the Extras

  • Computer/Laptop
  • Understanding of Python
  • Basic understanding of Machine Learning Concepts
  • Willingness to Learn
  • Python Developers and ML Enthusiasts
  • Beginner Python Developers curious about Machine Learning
  • Everyone
View More...
  • Section 1 : Introduction 6 Lectures 00:22:59

    • Lecture 1 :
    • Lecture 2 :
    • Components of A Machine Learning Pipeline
    • Lecture 3 :
    • Tools of the Craft
    • Lecture 4 :
    • Course Guide
    • Lecture 5 :
    • Setting Up Workspace
    • Lecture 6 :
    • Course Materials
  • Section 2 : Module 02 - Understanding and Working with ML Pipelines 11 Lectures 00:32:53

    • Lecture 1 :
    • What are Pipelines?
    • Lecture 2 :
    • Types of Pipelines
    • Lecture 3 :
    • Usefulness of Pipelines
    • Lecture 4 :
    • Intro to Scikit-Learn ML Pipelines
    • Lecture 5 :
    • Shortcomings of Scikit-Learn ML Pipelines
    • Lecture 6 :
    • Building A Machine Learning Model - Non Pipeline Approach
    • Lecture 7 :
    • Building A Machine Learning Model using Pipelines
    • Lecture 8 :
    • Using Grid Search with ML Pipelines
    • Lecture 9 :
    • Combining Multiple Machine Learning Models
    • Lecture 10 :
    • Visualizing Scikit-Learn Pipelines
    • Lecture 11 :
    • Voting Classifier
  • Section 3 : Module 02 - Natural Language Processing Pipelines 7 Lectures 00:31:21

    • Lecture 1 :
    • NLP Pipelines - Introduction
    • Lecture 2 :
    • NLP Pipelines with Scikit-Learn - Text Classifier Model
    • Lecture 3 :
    • NLP Pipelines - Text Classifier Pipeline
    • Lecture 4 :
    • NLP Pipelines - Saving the Text Classifier Pipeline
    • Lecture 5 :
    • NLP Pipelines - Multi-Label Text Classification with Pipelines
    • Lecture 6 :
    • NLP-Pipelines - How to Make Multi-Label Datasets For ML
    • Lecture 7 :
    • NLP-Project - Multi-Label Text Classification - Full Length
  • Section 4 : Module 02 - PySpark Pipelines 3 Lectures 00:05:39

    • Lecture 1 :
    • PySpark Pipelines - Introduction
    • Lecture 2 :
    • PySpark Pipelines - Building Features & Models
    • Lecture 3 :
    • PySpark Pipelines - Making Predictions
  • Section 5 : Module 03 - Working with Imbalanaced Datasets 10 Lectures 00:51:39

    • Lecture 1 :
    • Imbalanced Dataset - Introduction
    • Lecture 2 :
    • Imbalanced Dataset - How to Make A Dataset Imbalanced
    • Lecture 3 :
    • Imbalanced Dataset - How to Detect Imbalanced Datasets
    • Lecture 4 :
    • Imbalanced Dataset - Building Baseline For ML Model
    • Lecture 5 :
    • Imbalanced Dataset - Fixing Imbalanced Dataset : Changing ML Algorithms
    • Lecture 6 :
    • Imbalanced Dataset - Oversampling Technique
    • Lecture 7 :
    • Imbalanced Dataset - Fixing Imbalanced Dataset using SMOTE
    • Lecture 8 :
    • Imbalanced Dataset - Fixing Imbalanced Dataset using TomekLinks
    • Lecture 9 :
    • Imbalanced Dataset - Combining Oversampling and Undersampling Techniques
    • Lecture 10 :
    • Imbalanced Dataset - Fixing Imbalanced Dataset using Undersampling Techniques
  • Section 6 : Module 04 - Interpreting & Evaluating Machine Learning Models 5 Lectures 00:30:23

    • Lecture 1 :
    • Model Evaluation
    • Lecture 2 :
    • Model Interpretation & Explanation - Introduction
    • Lecture 3 :
    • Model Interpretation - Working with Eli5
    • Lecture 4 :
    • Model Interpretation - Working with Lime
    • Lecture 5 :
    • Model Interpretation - Visualizing Tree Based ML Models
  • Section 7 : Module 05 - Model Fairness and Bias Detection 3 Lectures 00:03:42

    • Lecture 1 :
    • Model Fairness & Bias Detection - Full Length & Indepth
    • Lecture 2 :
    • Model Fairness & Bias Detection - DecisionTreeClassifier
    • Lecture 3 :
    • Model Drift and Data Drift Detection
  • Section 8 : Online Machine Learning 2 Lectures 00:00:01

    • Lecture 1 :
    • Online Machine Learning - River Crash Course
    • Lecture 2 :
    • Online Machine Learning - Creme Crash Course
  • Section 9 : Extra Machine Learning Tools 6 Lectures 00:15:47

    • Lecture 1 :
    • Turi Create - Crash Course
    • Lecture 2 :
    • Machine Learning with Turi Create
    • Lecture 3 :
    • Data Logging & Monitoring For ML Projects
    • Lecture 4 :
    • Visualizing Deep Learning Models
    • Lecture 5 :
    • Snorkel Python Tutorial - Labeling Unlabeled Dataset
    • Lecture 6 :
    • Text Classification with Turi Create
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