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

• Build a Predictive Model

⚐ Topics covered in this course:

Lecture 2. What is machine learning? Key ML Terminology.

• What is machine learning?

• Key ML Terminology

• Supervised Machine Learning

• Unsupervised Machine Learning

• Reinforcement Learning

Lecture 3. Practice. Predict the price of houses. Dataset 1. Beginner's Guide to Jupyter Notebooks for Data Science.

• Jupyter Notebooks for Data Science

• Introduction to Kaggle for Beginners in Machine Learning

• Supervised learning: predicting an output

• Predict the price of a house

Lecture 4.How does machine learning work? Prediction of construction time and cost. Example of how predictions work.

• 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

Lecture 5. Practice. Prediction of price and construction time. Data loading and preparation (Part 1/2).

• 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

Lecture 6. Practice. Prediction of price and construction time. Evaluation Metrics. Linear Regression(Part 2/2).

• 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

Lecture 7. Workflow of a Machine Learning project. Stages of the Machine Learning Modeling Cycle.

• Stages of the Machine Learning Modeling Cycle

• Learning Phase of Machine Learning

• Inference from Model

• Machine Learning Deployment Pipeline

Lecture 8. Practice. San Francisco - explore Building Permits Data. Data loading and preparation to Analyzing (Part 1/2).

• Find Open Datasets

• Loading large Datasets into Kaggle

• 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

Lecture 9. Practice. San Francisco - explore Building Permits Data. Cost Prediction. Way to build a Predictive Model (Part 2/2).

• Build a Predictive Model

• Training and Validation Sets: Splitting Data

• Determining the "estimated cost" by parameters

• Predict the "estimated cost" for arbitrary parameters

• Evaluation Metrics for Machine Learning Model

• Linear regression Predictive Models

The course is best-suited for learners who are interested in Big Data and Machine Learning (using Python) or for learners who already have Python programming skills but want to practice with a hands-on, real-world data project can also benefit from this course.