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Machine Learning [*] Beginner to intermediate [*]
Learn with Prem Radheshyam Mahajan
11 modules
Lifetime access
Dive into the world of machine learning with this comprehensive course including basic concepts, algorithms, and hands-on projects.
Overview
- This machine learning course covers basic concepts, algorithms, and hands-on projects to provide a comprehensive understanding of machine learning. Learn the fundamental principles, key algorithms, and practical applications of machine learning in real-world scenarios.
- Machine Learning is a comprehensive course that covers the fundamental concepts, algorithms, and applications of machine learning. You will delve into the world of artificial intelligence and learn how to build and train machine learning models to make predictions and decisions based on data.
Key Highlights
Foundational machine learning concepts
Key algorithms and techniques taught
Hands-on projects to apply learning
What you will learn
Understand Basic Concepts
Gain a solid foundation in machine learning concepts such as supervised learning, unsupervised learning, and deep learning.
Learn Key Algorithms
Explore essential machine learning algorithms like decision trees, neural networks, and support vector machines.
Implement Hands-on Projects
Apply your knowledge through hands-on projects that cover real-world machine learning applications.
Modules
Overview To Machine Learning
9 attachments • 27.73 mins
What is Machine Learning [ Theory For Knowledge Growing and Reading ].
What is Machine Learning.
Machine Learning Tool's and Platform
Machine Learning Platform's.
Why Machine Learning ?
Normal Details About Machine Learning.
Basic Part of Machine Learning.
Basic Quiz Machine Learning.
Basic Quiz Machine Learning
How To Download Machine Learning Tool's / Platform.
3 attachments
Jupiter Notebook
Google Colab Notebook
Anaconda Machine
Basic's Machine Learning [ Part 1st ].
10 attachments • 19.38 mins
Library's Use In Machine Learning.
How To Import Libraries in ML
Dataset Location's
Datasets Location's
Find Missing value and set Them
Include File in Machine Learning [ Online and Offline ] Mode.
How To Include File In Machine Learning Platform
Find, Delete, Update , Check Missing Value's
How to Change, Delete, Update Null Value In data [ ML ].
What and Why missing value's ?
Data Selection and Conversion.
6 attachments • 2 mins
Type's of Data Conversion.
Nominal and Ordinal Data
Data Conversion Technique's
One Hot Encoding
Label Encoding
Ordinal Encoding
Train and Test Data Creation and solve Some Problem.
1 attachment
Data Include and Split It Into Train and Test Part's.
How data Create Problem In Machine Learning ?
6 attachments • 1 mins
Technique To Solve This Problem.
Data Scaling [ Grow Modal Performance ]
Data Normalization and Technique
Max absolute Scaling.
Data Normalization
Data Standadization
Plotting Graph's and Data Distribution's.
13 attachments
Why Use Graph Plotting in ML ?
Use Of Graph Plotting.
Types Of Graph's.
Bar Graph
Histogram Graph.
Scatter Graph.
Area Plot Graph.
Pie chart / Graph.
Different - Different Data Distribution's
Barnoulie Data Distribution
Uniform Data Distribution
Binomial Data Distribution.
Poisson data Distribution
Part 1: Knowledge Testing [ Assignment and Quiz ].
3 attachments • 15 mins
Basic Quiz Machine Learning Part: 1st
Include Iris Data Set and Processing data [ Cleaning, Scaling, Graph Plotting ] Task.
Theory Base Assignment On Part:- 1st
Basic's To Intermediate Machine Learning [ Part 2nd ].
5 attachments
Main types of Machine Learning algorithm's. [ Theory with Example ].
Supervised Learning
Un-supervised Learning.
Reinforcement Learning.
Theory Basic To Intermediate Part-2 Test.
Supervised Learning:- Classification Algorithm.
23 attachments
What Is Classification ?
Types of classification in Supervised approach learning.
Logistic Regression Algorithm.
Logistic Regression
1) Binomial , 2) Multinomial, 3) Ordinal
Logistic Regression [ Practicle ].
Logistic Regression in long Way Use.
Logistic Regression In short Use.
K-nearest neighbors Algorithm.
Theory [ KNN Algo].
Practicle [ KNN Algo ]. Long Way Use.
Practicle [ KNN Algo ]. Short Way Use.
Decision Tree Algorithm.
Decision Tree Theory
Decision Tree Practicle. [ 'Cart' Use ].
Decision Tree Practicle. [ 'id3' Use ].
Random Forest Algorithm.
Random Forest Theory.
Random Forest Practicle.
Support Vector Machine Algorithm.
Theory and Practicle [SVC].
Naive Bayes Algorithm.
Theory and Practicle [Naive Bayes].
Evaluating Model's On Classification's.
8 attachments • 15 mins
Confusion Metrix.
Model Accuracy.
Precision.
Recall.
F2 Score.
Evaluating Classification Model Practicle's.
Supervised Learning
Find Dataset On Your Learning In Above Course [ Various Data Websites ]. and Perform Algo [ Logistic Regression , Decision Tree , SVM ].
FAQs
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Yes, our platform is designed to be accessible on various devices, including computers, laptops, tablets, and smartphones. You can access the course materials anytime, anywhere, as long as you have an internet connection.
How can I access the course materials?
Once you enrol in a course, you will gain access to a dedicated online learning platform. All course materials, including video lessons, lecture notes, and supplementary resources, can be accessed conveniently through the platform at any time.
Can I interact with the instructor during the course?
Absolutely! we are committed to providing an engaging and interactive learning experience. You will have opportunities to interact with them through our community. Take full advantage to enhance your understanding and gain insights directly from the expert.
About the creator
Learn with Prem Radheshyam Mahajan
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Machine Learning [*] Beginner to intermediate [*]
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