Machine Learning for Software Engineers

[LeeAndro]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 5.96 GB | Duration: 11h 30m

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

What you'll learn

Theory and practicals of Regression

Theory and practicals of Classification

Theory and practicals of Clustering

Exploratory Data Analysis techniques

Description

We aim to offer software eeers 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 . We b 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, matplotlib, 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 able 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







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