Supervised Machine Learning Tutorial for Beginners
In this Machine Learning course, you'll learn about Supervised Machine Learning. Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.
Machine Learning with Python - Supervised and Unsupervised Learning
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Machine Learning Types - Supervised, Unsupervised & Reinforcement | Machine Learning
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In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher.
Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, spam filtering, etc.
In this comprehensive course, you will learn the fundamental concepts and techniques used in Machine Learning. We will cover a range of topics from data preprocessing to model evaluation and selection, with hands-on exercises and projects to help you build and solidify your understanding of the concepts.
The course is designed for beginners, but it will also be valuable for those who have some experience in programming and data analysis. You will be guided through the basics of Python programming and the most commonly used libraries for data manipulation and visualization, such as Pandas and Matplotlib.
Once you have mastered the basics, we will delve into the core concepts of Machine Learning, including supervised and unsupervised learning, decision trees, random forests, clustering, neural networks, and deep learning. You will learn how to preprocess data, train and evaluate models, and optimize them for better performance.
In addition to the theory, you will also have hands-on practice using real-world datasets and implementing Machine Learning algorithms with Python. By the end of the course, you will be able to apply Machine Learning techniques to solve a wide range of problems and use cases, and have the skills to further your studies in this exciting and rapidly growing field.
Whether you are a student, a researcher, or a professional looking to expand your skillset, this course will provide you with a strong foundation in Machine Learning and equip you with the knowledge and tools to succeed in the field. So, join us now and start your journey toward becoming a Machine Learning expert!
What you will learn:
Machine Learning
Artificial Intelligence
Supervised Machine Learning
Supervised ML Model
What is Regression?
Simple LR
Multi-LR
Polynomial Regression
Model Development
Data Preprocessing
Regression Coding
Scikit Programming
Collection of Data
Splitting of Data
Poly-Scatter Plot
KNN-Model for SML
Decision Tree
Data Visualization for SML
Support Vector mechanics.
scatter Plots
Matplotlib Glitches
Colors in Scattering
Plot Vs Scatter Plot
Bar Plotting
Multiple Bar Plot
Stacked and Sub Plots
Histogram Plot
Data Set
Data Distribution
Are there any course requirements or prerequisites?
PC, Laptop, or Smartphone
Who this course is for:
Those who wants to learn supervised machine learning algorithms in detailed.
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