Supervised Machine Learning In Python by EDUCBA Bridging the Gap

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Published 1/2024
MP4 | Video: h264, 1920x1080 | 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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