Supervised Machine Learning: Complete Masterclass [2023]
Machine Learning, artificial intelligence, supervised machine learning, simple linear regression, and KNN model
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 Preprocession
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
Why take this course?
< Step-by-step explanation of more than 7 hours of video lessons on Supervised Machine Learning: Complete Masterclass [2023]>
<Instant reply to your questions asked during lessons>
<Weekly live talks on Supervised Machine Learning: Complete Masterclass [2023]. You can raise your questions in a live session as well>
<Helping materials like notes, examples, and exercises>
<Solution of quizzes and assignments>
Welcome to the Machine Learning course!
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
Allah Ditta is your lead instructor – a Ph.D. and lecturer making a living from teaching Supervised Machine Learning, and data science.
You'll get premium support and feedback to help you become more confident with data science!
We can't wait to see you on the course!
Enroll now, and we'll help you improve your data science skills!
AD Chauhdry
Tayyab Rashid