Supervised Machine Learning In Python by EDUCBA Bridging the Gap

Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.78 GB | Duration: 8h 22m

A practical course about supervised machine learning using Python programming language

What you’ll learn
Python Basics
Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
Machine learning Concept and Different types of Machine Learning
Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more

Requirements
Python porgramming language and Data pre-processing techniques

Description
In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language. Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.In the realm of cutting-edge technology, machine learning stands at the forefront, revolutionizing industries and transforming the way we interact with the world. From personalized recommendations to autonomous vehicles, machine learning empowers computers to learn from vast amounts of data and make intelligent decisions. If you’ve ever been captivated by the idea of building intelligent systems, understanding the prerequisites for machine learning is your essential first step.Embarking on a journey into machine learning requires a solid foundation in several key areas. As with any endeavor, building upon a sturdy groundwork paves the way for success. Let us unveil the prerequisites that will equip you with the skills to unravel the mysteries of machine learning and harness its potential to shape the future.Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many moreMachine learning Concept and Different types of Machine LearningMachine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..Feature engineeringPython Basics

Overview
Section 1: Supervised Machine Learning in Python

Lecture 1 Introduction to Machine Learning

Lecture 2 Advantages and Disadvantages of Machine Learning

Lecture 3 NumPy Introduction

Lecture 4 Features and Installation

Lecture 5 NumPy Array Creation

Lecture 6 NumPy Array Attributes

Lecture 7 NumPy Array Operations

Lecture 8 NumPy Array Operations Continue

Lecture 9 NumPy Array Unary Operations

Lecture 10 Numpy Array Splicing

Lecture 11 NumPy Array Shpe

Lecture 12 Stacking Together Different Arrays

Lecture 13 Splitting one Array into Several Smaller ones

Lecture 14 Copies and Views

Lecture 15 NumPy Array Indexing

Lecture 16 NumPy Array Indexing Continue

Lecture 17 NumPy Array Boolean

Lecture 18 Introduction to Matlplotlib

Lecture 19 Understanding Various Functions of Pyplot

Lecture 20 Multiple Figures and Subplots

Lecture 21 Intro to Pandas

Lecture 22 Intro to Pandas Continue

Lecture 23 Data Structure in Pandas

Lecture 24 Data Structure in Pandas Continue

Lecture 25 Pandas Column Select

Lecture 26 Remove Operations

Lecture 27 Pandas Arithmetic Operations

Lecture 28 Pandas Arithmetic Operations Continue

Lecture 29 Introduction to Scikit Learn

Lecture 30 Supervised

Lecture 31 Unsupervised Learning

Lecture 32 Load Data Set

Lecture 33 Scikit Example Digits

Lecture 34 Digits Dataset Using Matplotlib

Lecture 35 Understading Metrics of Predicted Digits Dataset

Lecture 36 Persisting Models

Lecture 37 K-NN Algorithm with Example

Lecture 38 Cross Validation

Lecture 39 Cross Validation Techniques

Lecture 40 K-Means Clustering Example

Lecture 41 Agglomeration

Lecture 42 PCA Pipeline

Lecture 43 Face Recognition

Lecture 44 Face Recognition Output

Lecture 45 Right Estimator

Lecture 46 Text Data Example

Lecture 47 Extracting Features

Lecture 48 Occurrences to Frequencies

Lecture 49 Classifier Training

Lecture 50 Performance Analysis on the Test Set

Lecture 51 Parameter Tuning

Lecture 52 Language Identifcation

Lecture 53 Movie Review Screen Stream

Lecture 54 Movie Review Screen Stream Continue

Python developers, Data Scientists, Computer engineers, Researchers Students

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