What you'll learn
Theory and practicals of Regression
Theory and practicals of Classification
Theory and practicals of Clustering
Exploratory Data Analysis techniques

Description
This course has been put together by a team of experienced teaching professionals and industry experts in machine learning.

We aim to offer software engineers and those with some coding experience an introduction to the main concepts of machine learning.

We take a very practical approach, mixing theory videos and practical videos, with all code and jupyter notebooks used throughout the course being available for download. We begin with Regression, then Exploratory Data Analysis, before moving on to Classification and Clustering.

Not only will you learn how to build models, you'll also learn the correct ways to evaluate your data, identify problems and validate the correctness of your models.

At the end of this course you will be able to:

Analyse a new set of data using Exploratory Data Analysis

Generate summary statistics and visualisations

Identify outliers and be able to handle missing data

Be able to use: jupyter, pandas, seaborn, matDescriptionlib, scipy, imblearn

Build Linear Regression models - Ordinary Least Squares

Build Non-Linear Regression models - SVM, Decision Trees, Random Forest

Build Classification models - K-Nearest Neighbour, Approximate KNN, Naive Bayes

Build Clustering models - K-means, Gaussian Mixture Models, Agglomerative Clustering, DBSCAN

Data resampling techniques, dummy classifiers & k-fold validation, Pipelines

Data encoding techniques - One-hot Encoding, Target Encoding, Binary Encoding

This course includes:

Over 11 hours of video content

17 downloadable resources

17 practical assignments in jupyter notebooks

Reference Materials & further reading

Who this course is for:
Coders who are looking to learn or brush up on some practical Machine Learning skills
Developers who are interested in Machine Learning
Developers who are interested in Data Science

Screenshots

Citación :

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