# Machine Learning. Price and Time Prediction (Part 5/5)

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Price and Time Prediction (Part 5/5) 0.0 (0 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

What you’ll learn

• What is machine learning?
• Key ML Terminology
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Learning
• Jupyter Notebooks for Data Science
• Introduction to Kaggle for Beginners in Machine Learning
• Supervised learning: predicting an output
• Predict the price of a house
• Prediction of time and cost for small training dataset
• K-means supervised Machine Learning algorithm
• Understanding K-means Clustering in Machine Learning
• Overview of Machine Learning Algorithms
• Getting started with Machine Learning in MS Excel
• A Kaggle Walkthrough – Cleaning Data
• Beginner’s Guide to Jupyter Notebooks
• Train, Validation Sets in Machine Learning
• Splitting data into Training & Validation
• Determined the cost and time of construction work for project X
• Evaluation Metrics for Machine Learning Model
• Linear Regression for Machine Learning
• How our algorithm works visually
• Creating and Visualizing Decision Trees
• Stages of the Machine Learning Modeling Cycle
• Learning Phase of Machine Learning
• Inference from Model
• Machine Learning Deployment Pipeline
• Find Open Datasets
• Data visualization and analysis in Kaggle
• Average postcode price on a San Francisco map
• Total cost of all building permits for the postal code
• Average “estimated cost” by type of housing
• Build a Predictive Model
• Training and Validation Sets: Splitting Data
• Determining the “estimated cost” by parameters
• Predict the “estimated cost” for arbitrary parameters

Description

This course is intended to be an initiation to learn #BigData and #MachineLearning & #AI with #Python programming for absolute beginners that have no background in programming.

In this course, we will step by step, using the example of real data, we will go through the main processes related to the topic

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