# Machine Learning. Price and Time Prediction

👉  Machine Learning. Price and Time Prediction | Udemy NED

Machine Learning. Price and Time Prediction Applying Machine Learning and Artificial intelligence to Construction. Price and Time Forecasting.
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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
Requirements
• You need only the installed Windows System
• You do not need any special programming knowledge or theoretical knowledge of Python
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 "Big data and machine learning".
Since the material turned out to be voluminous, I divided the course into five parts.

In this fifth part:

⇉ We will examine in detail the basic types, terms and algorithms of machine learning. We go through the basic concepts of machine learning that beginners need. We will consider in more detail such algorithms as K-means supervised Machine Learning, Linear Regression and other algorithms for Machine Learning.

⇉ In practical lessons we will predict the time and cost of construction for the new project X, based on the data that we collected on previous projects. And in another lesson we will predict the cost of building project X and construction time by the parameters that we will set for the new project x

⇉ Then we take open source data for the San Francisco city. We will clear this raw data and display the data in the form of a charts and maps. We will collect various interesting insights from this public information. Then we will prepare the data to create a machine learning model and try to predict some parameters from this data.

⚐  You will be guided through the basics of using:

• Machine Learning Algorithms

• Jupyter Notebooks for Data Science

• K-means Machine Learning algorithm

• Machine Learning Modeling Cycle

• Linear Regression

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