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Last updated 9/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 302.71 MB | Duration: 0h 40m
A practical Hands-on Data Science Project on Graduate Admission Prediction Using Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 302.71 MB | Duration: 0h 40m
A practical Hands-on Data Science Project on Graduate Admission Prediction Using Machine Learning
What you'll learn
Using AI and Machine Learning to Predict Chance of Admit into Universities
Building, Training, Testing and Evaluating Machine learning Models
Learn to create heatmaps, correlation tables, scatter plots and distplot using Seaborn library
A-Z step by step guide into importing libraries, importing and exploring datasets, building a Machine learning model, training, testing and evaluating it.
Learn to work with Linear Regression Machine Learning Algorithm to create Machine Learning Models with approx 96 percent accuracy.
Importing, Exploring and Analyzing datasets and finding correlation between its variables
Requirements
Very basic knowledge of python and its libraries
Description
Have you ever desired to build, train and test a Machine Learning Model in a real-world application?Would you like to learn how to predict Chance of Admission into Graduate School using Machine Learning?If the answer to any of the question is "YES", then you will love this project.This is a Practical Hands-on Machine Learning Guided Project with a real-world application of Machine learning. You learn by Practice. No unnecessary lectures. No unnecessary details. Clear, Concise, to the point Course.By the end of this course, you will be confident in building. training and testing any Linear Regression Machine Learning Model and Implementing them in real-world scenarios. No prior Experience in Machine learning required. We will guide you from first to last: Every Single Step.Reviews: "Gosh, this was fast! I was so absorbed with the learning I did not even realise I went through it no-stop! Really, interesting, the material is well done and presented in a concise but very clear manner. Surely the course is short but is lacking of nothing, I am in ore and full of praise! Well deserved 5 stars!""This Course is very great to learn Machine learning & Data Science."Enrol Now and let's build a Machine Learning Model together in under 1 hour. We will build a Machine Learning Model and we will feed the data of thousands of students and their GRE Score, TOEFL Score, CGPA, SOP. LOR, University rating and Research to the Model and train it in order to predict the Chance of Admit to Graduate School. In the end, we will test the model and evaluate its performance.When you complete the project, you will be proud of yourself on what you have learned and achieved.You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures. Learn the most important aspect of Data Science :Importing all the necessary LibrariesImporting and Exploring DatasetsBuilding a Linear Regression Machine Learning ModelTraining, Testing and Evaluating the modelWe will build a Machine Learning model to predict Graduate Admissions. In this hands-on project, we will complete the following tasks:Task 1: Brief theoretical information about Libraries, Dataset, Linear Regression Algorithm and Google Colab EnvironmentTask 2: Importing all the necessary LibrariesTask 3: Importing the Graduate Admission dataset to the Colab EnvironmentTask 4: Data Cleaning: Removing unnecessary columnsTask 5: Exploratory Data Analysis using graphs: Correlation & feature selectionTask 6: Splitting the Dataset into Training and Testing setsTask 7: Building and Training Linear Regression ModelTask 8: Performance evaluation & Testing the modelMake a leap into Data science with this Hands-on guided project and showcase Machine Learning skills on your resume.So, grab a coffee, turn on your laptop, click on the "Enrol Now" button and start learning right now.
Overview
Section 1: Introduction
Lecture 1 Project Overview and Introduction to the Dataset
Lecture 2 Introduction to Libraries, Linear Regression Algorithm and Colab Platform
Section 2: Importing Libraries and Dataset
Lecture 3 Importing all the necessary Libraries and Dataset into the Colab environment
Lecture 4 Cleaning the Data
Section 3: Exploratory Data Analysis (EDA)
Lecture 5 Exploring the data using Seaborn and Pandas Libraries
Section 4: Building and Training Machine Learning Model
Lecture 6 Build and Train Machine Learning Model
Section 5: Model Testing and Evaluation
Lecture 7 Testing & Evaluating the Performance of the Model
Anyone who wants to build a Machine learning Model and evaluate its prediction,Anyone interested in Data science
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