mlessentials/lab_overview.md

Data Science

Description

Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms

Key Features

• Gain a full understanding of the model production and deployment process
• Build your first machine learning model in just five minutes and get a hands-on machine learning experience
• Understand how to deal with common challenges in data science projects

What You Will Learn

• Explore the key differences between supervised learning and unsupervised learning
• Manipulate and analyze data using scikit-learn and pandas libraries
• Understand key concepts such as regression, classification, and clustering
• Discover advanced techniques to improve the accuracy of your model
• Understand how to speed up the process of adding new features
• Simplify your machine learning workflow for production

Labs

Labs for this course are available at endpoints shared below. Update <host-ip> with the lab environment DNS.

1. Introduction to Data Science in Python

    * http://<host-ip>/lab/workspaces/lab1_Introduction
   

2. Regression

    * http://<host-ip>/lab/workspaces/lab2_Regression
   

3. Binary Classification

    * http://<host-ip>/lab/workspaces/lab3_Classification
   

4. Multiclass Classification with RandomForest

    * http://<host-ip>/lab/workspaces/lab4_RandomForest
   

5. Performing Your First Cluster Analysis

    * http://<host-ip>/lab/workspaces/lab5_Analysis
   

6. How to Assess Performance

    * http://<host-ip>/lab/workspaces/lab6_Performance
   

7. The Generalization of Machine Learning Models

    * http://<host-ip>/lab/workspaces/lab7_Models
   

8. Hyperparameter Tuning

    * http://<host-ip>/lab/workspaces/lab8_Hyperparameter
   

9. Interpreting a Machine Learning Model

    * http://<host-ip>/lab/workspaces/lab9_ML
   

10. Analyzing a Dataset

    * http://<host-ip>/lab/workspaces/lab10_Dataset
    

11. Data Preparation

    * http://<host-ip>/lab/workspaces/lab11_Data
    

12. Feature Engineering

    * http://<host-ip>/lab/workspaces/lab12_Feature
    

13. Imbalanced Datasets

    * http://<host-ip>/lab/workspaces/lab13_Imbalanced
    

14. Dimensionality Reduction

    * http://<host-ip>/lab/workspaces/lab14_Dimensionality
    

15. Ensemble Learning

    * http://<host-ip>/lab/workspaces/lab15_Ensemble
    

About

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.

The course begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.

Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the course demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.

By the end of this course, you’ll have the skills to start working on data science projects confidently. By the end of this course, you’ll have the skills to start working on data science projects confidently.