- Introduction to Machine Learning
- Understanding Linear Regression through graphs
- Demand vs Price Problem to understand Linear Regression
- Introduction to Optimizers
- The Gradient Descent Algorithm
- Downloading the dataset and importing Libraries
- Reading the dataset
- Cleaning the data
- The Model
- Visualising the data
- Overfitting and Underfitting
- Concluding Remarks
What you'll learn
- Machine Learning and Linear Regression: Gain insights into the world of Machine Learning, with a focus on Linear Regression.
- Fundamentals of Machine Learning: Grasp the essential concepts and principles that underpin machine learning algorithms.
- Starting with Data Science in Python: Learn how to initiate your journey into Data Science using Python as a versatile tool.
- Regression Mathematics: Dive into the mathematical foundations of regression analysis, a key technique in predictive modeling
Description
Greetings, everyone! We're excited to announce that our "Machine Learning Absolute Fundamentals for Linear Regression" course is now open to all students. This course is specifically designed for novice Python developers who are eager to embark on their journey into the world of machine learning. In this instructional module, we will dive into the practical application of a linear regression model, harnessing the power of the Python scikit-learn library, to predict the total number of COVID-19 positive cases within a specific Indian state.
By the end of this course, you will have the knowledge and skills to:
Gain a fundamental understanding of what machine learning is, demystifying its core concepts and principles.
Define what a dataset entails and comprehend its significance in the context of machine learning.
Explore the pivotal functions and roles of machine learning in various domains and applications.
Attain a comprehensive grasp of the concept of linear regression, a foundational machine learning technique for predictive modeling.
Elaborate on the cost function and delve into the concept of the line of greatest fit, often measured by the Mean Squared Error (MSE).
Learn how to effectively manipulate and preprocess your dataset using the versatile pandas library functions, ensuring that it's ready for machine learning.
Master the art of partitioning your data into training and testing subsets, a critical step in model evaluation.
Harness the power of Scikit-Learn to create a robust linear regression model and efficiently train it on your dataset.
Evaluate the performance of your model and make data-driven predictions, enabling you to foresee future COVID-19 positive cases with confidence.
Develop your data visualization skills using Matplotlib, allowing you to communicate your findings effectively through compelling graphical representations.
Diving deeper into the realm of linear regression, we find that this technique leverages linear predictor functions to model relationships within data. The essence of linear regression lies in the estimation of unknown parameters from the available dataset. These models, aptly named linear models, offer valuable insights into the conditional mean of the response variable. Typically, this conditional mean is viewed as an affine function of the explanatory variables, commonly referred to as predictors. Occasionally, in specific applications, other quantiles such as the conditional median are employed.
Other Courses
Building a Facebook Chatbot in Chatfuel
A Comprehensive Guide to Monetizing Messenger for Brands & Businesses
Why the Law of Attraction Fails You and What to Do about It!
Learn how to use the Law of Attraction successfully to manifest wealth, love, health and happiness!
Introduction to Front-End Development
How to build web application if you never did it before
Welcome to Weigh Down - The Answer to Permanent Weight Loss
Orientation to the "Weigh Down Basics" Weight Loss Seminar by Gwen Shamblin
Expert Advice For Parents
Breaking the pacifier habit, eating healthy, learning to share toys with friends and first day at preschool
About the instructors
- 4.25 Calificación
- 224408 Estudiantes
- 34 Cursos
Sujithkumar MA
Engineer | Course Instructor
Languages - C, C++, Python, Verilog, System Verilog
Hardware - Digital Logic Design, Computer Architecture, VLSI Design, Analog Electronics, Signal Processing, Embedded Systems
Software - Data Structures & Algorithms, Operating Systems, Database Management Systems, Computer Networks, Machine Learning, Deep Learning.
Tools - Xilinx Vivado, Matlab, Multisim, Altium, Arduino IDE, TinkerCAD, Tanner EDA, Cadence Virtuoso
Boards - Arduino, 8051, TIVA, Raspberry Pi, NodeMCU
Areas of Interest - Artificial Intelligence, Digital Design, Software Engineering, Algorithms
Student feedback
Course Rating
Reviews
The author is very sincere and involved. Very good attempt to make LR easily understood. GD explanation is well-done. Inclusion of one or more case studies would go a long way, perhaps. But not needed. It is all in here.
Great explanation and very interesting!
Pace of the course could be better. Overall quite insightful