Effective Prediction with Machine Learning

Effective Prediction with Machine Learning

A one-stop solution to quickly program fast Machine Learning algorithms with NumPy and scikit-learn.

Bestseller
Created By: Julian Avila
15.87 9.52

About This Course

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.


This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models. Finally, you'll explore dimensionality reduction with various parameters.

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Build predictive models in minutes by using scikit-learn
  • Understand the differences and relationships between Classification and Regression
  • Use distance metrics to predict in Clustering
  • Find points with similar characteristics with Nearest Neighbors
  • Use automation and cross-validation to find the best model and focus on it for a data product

Are There Any Course Requirements Or Prerequisites?

A sound knowledge of Anaconda and its libraries such as NumPy and Sckit-learn is required.

Who Are Your Target Students?


This course is ideal for the Machine Learning beginner with some basic Python experience.

Course Content

  • 19 lectures
  • 01:31:59
  • The Course Overview
    00:03:32
  • NumPy Basics
    00:07:30
  • Loading and Viewing the Iris Dataset
    00:04:28
  • Viewing the Iris Dataset with Pandas
    00:02:24
  • Plotting with NumPy and Matplotlib
    00:04:34
  • SVM Classification
    00:04:40
  • Cross-Validation Using Various Algorithms
    00:08:34
  • Classification versus Regression
    00:08:08
  • Creating Sample Data for Toy Analysis
    00:02:44
  • Scaling Data to the Standard Normal Distribution
    00:04:03
  • Working with Categorical Variables
    00:06:01
  • Creating Binary Features and Imputing Missing Values
    00:05:30
  • A Linear Model in the Presence of Outliers
    00:07:42
  • Using Gaussian Processes for Regression
    00:04:32
  • Using SGD for Regression
    00:03:34
  • Reducing Dimensionality with PCA
    00:05:29
  • Using Decomposition to Classify with Dictionary Learning
    00:02:31
  • Dimensionality Reduction with Manifolds
    00:02:40
  • Testing Methods to Reduce Dimensionality with Pipelines
    00:03:23
Image

Packt Publication

  • 4.48 (6)
  • 13 Reviews
  • 6 Students
  • 935 Courses