Supervised Machine Learning: Complete Masterclass [2023]

Machine Learning, artificial intelligence, supervised machine learning, simple linear regression, and KNN model

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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