Myself Shridhar Mankar a Engineer l YouTuber l Educational Blogger l Educator l Podcaster. My Aim- To Make Engineering Students Life EASY. Website – 5 Minutes Engineering English YouTube Channel – Instagram – A small donation would mean the world to me and will help me to make AWESOME videos for you. • UPI ID : 5minutesengineering@apl Playlists : • 5 Minutes Engineering Podcast : • Aptitude : • Machine Learning : • Computer Graphics : • C Language Tutorial for Beginners : • R Tutorial for Beginners : • Python Tutorial for Beginners : • Embedded and Real Time Operating Systems (ERTOS) : • Shridhar Live Talks : • Welcome to 5 Minutes Engineering : • Human Computer Interaction (HCI) : • Computer Organization and Architecture : • Deep Learning : • Genetic Algorithm : • Cloud Computing : • Information and Cyber Security : • Soft Computing and Optimization Algorithms : • Compiler Design :–SachxUTOiQ7XHw • Operating System : • Hadoop : • CUDA : • Discrete Mathematics : • Theory of Computation (TOC) : • Data Analytics : • Software Modeling and Design : • Internet Of Things (IOT) : • Database Management Systems (DBMS) : • Computer Network (CN) : • Software Engineering and Project Management : • Design and Analysis of Algorithm : [More]
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: This Linear Regression Analysis video will help you understand the basics of linear regression algorithm. You will learn how Simple Linear Regression works with solved examples, look at the applications of Linear Regression and Multiple Linear Regression model. In the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. Dataset Link – Below topics are covered in this Linear Regression Analysis Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Usecase – Profit estimation of companies What is Linear Regression Analysis? Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors: Which variables, in particular, are significant predictors of the outcome variable? How significant is the regression line in terms of making predictions with the highest possible accuracy? Subscribe to our channel for more Machine Learning Tutorials: For a more detailed understanding of Linear Regression Analysis, do visit: You will find in-depth content on Machine Learning. Browse further to discover similar resources on related topics, made available to you as a learning path. Enjoy top-quality learning for FREE. Machine Learning Articles: To gain in-depth [More]
** Machine Learning Training with Python: ** This Linear Regression Algorithm video is designed in a way that you learn about the algorithm in depth. This video is designed in a way that in the first part you will learn about the algorithm from scratch with its mathematical implementation, then you will drill down to the coding part and implement linear regression using python. Below are the topics covered in this tutorial: 1. What is Regression? 2. Regression Use-case 3. Types of Regression – Linear vs Logistic Regression 4. What is Linear Regression? 5. Finding best-fit regression line using Least Square Method 6. Checking goodness of fit using R squared Method 7. Implementation of Linear Regression Algorithm using Python (from scratch) 8. Implementation of Linear Regression Algorithm using Python (scikit lib) Check out our playlist for more videos: PG in Artificial Intelligence and Machine Learning with NIT Warangal : Post Graduate Certification in Data Science with IIT Guwahati – (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Subscribe to our channel to get video updates. Hit the subscribe button above. #LinearRegressionAlgorithm #LinearRegressionAlgorithmUsingPython #LeastSquareMethod #RsquareMethod How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will [More]
In this video, make sure you define the X’s like so. I flipped the last two lines by mistake: X = np.array(df.drop([‘label’],1)) X = preprocessing.scale(X) X_lately = X[-forecast_out:] X = X[:-forecast_out:] To forecast out, we need some data. We decided that we’re forecasting out 10% of the data, thus we will want to, or at least *can* generate forecasts for each of the final 10% of the dataset. So when can we do this? When would we identify that data? We could call it now, but consider the data we’re trying to forecast is not scaled like the training data was. Okay, so then what? Do we just do preprocessing.scale() against the last 10%? The scale method scales based on all of the known data that is fed into it. Ideally, you would scale both the training, testing, AND forecast/predicting data all together. Is this always possible or reasonable? No. If you can do it, you should, however. In our case, right now, we can do it. Our data is small enough and the processing time is low enough, so we’ll preprocess and scale the data all at once. In many cases, you wont be able to do this. Imagine if you were using gigabytes of data to train a classifier. It may take days to train your classifier, you wouldn’t want to be doing this every…single…time you wanted to make a prediction. Thus, you may need to either NOT scale anything, or you may scale the data separately. As [More]
In this tutorial, you will learn how to create a Machine Learning Linear Regression Model using Python. You will be analyzing a house price predication dataset for finding out the price of a house on different parameters. You will do Exploratory Data Analysis, split the training and testing data, Model Evaluation and Predictions. Blog and Dataset –
To begin, what is regression in terms of us using it with machine learning? The goal is to take continuous data, find the equation that best fits the data, and be able forecast out a specific value. With simple linear regression, you are just simply doing this by creating a best fit line. From here, we can use the equation of that line to forecast out into the future, where the ‘date’ is the x-axis, what the price will be. A popular use with regression is to predict stock prices. This is done because we are considering the fluidity of price over time, and attempting to forecast the next fluid price in the future using a continuous dataset. Regression is a form of supervised machine learning, which is where the scientist teaches the machine by showing it features and then showing it was the correct answer is, over and over, to teach the machine. Once the machine is taught, the scientist will usually “test” the machine on some unseen data, where the scientist still knows what the correct answer is, but the machine doesn’t. The machine’s answers are compared to the known answers, and the machine’s accuracy can be measured. If the accuracy is high enough, the scientist may consider actually employing the algorithm in the real world.