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Machine Learning Project: Heart Attack Prediction Analysis

Data Science & Machine Learning - Boost your Machine Learning, statistics skills with real heart attack analysis project


Oak Academy

Summary

Price
£16 inc VAT
Study method
Online, On Demand What's this?
Duration
7.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free

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Overview

Machine Learning, python, statistics, data science, machine learning python, python data science, machine learning a-z, data scientist, r, python for data science

Hello there,

Welcome to the “ Machine Learning Project: Heart Attack Prediction Analysiscourse.

Machine Learning & Data Science - Boost your Machine Learning skills with a real hands-on heart attack prediction project

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that.

A machine learning course teaches you the techniques and concepts behind the predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.

Do you know data science needs will create 11.5 million job openings by 2026?


Do you know the average salary is $100.000 for data science careers!

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

  • If you want to learn one of the employer’s most requested skills?

  • If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

  • If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you " Machine Learning with Real Hearth Attack Prediction Project " a straight-forward course for the Python Programming Language and Machine Learning.

In the course, you will have a down-to-earth way explanation of the project. With this course, you will carry out a data science project from start to finish. I made it simple and easy with a real-life example.

We will open the door of the Data Science and Machine Learning world and will move deeper. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn.

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn

Dive in now into; Machine Learning Project: Heart Attack Prediction Analysis

Data Science & Machine Learning - Boost your Machine Learning, and statistics skills with a real heart attack analysis project

See you in the course!

Curriculum

10
sections
62
lectures
7h 28m
total
    • 2: First Step to the Project 15:16
    • 3: FAQ about Machine Learning, Data Science 02:00
    • 4: Notebook Design to be Used in the Project 14:16
    • 5: Project Link File - Hearth Attack Prediction Project, Machine Learning 01:00
    • 6: Examining the Project Topic 10:01
    • 7: Recognizing Variables In Dataset 17:02
    • 8: quiz 01:00
    • 9: Required Python Libraries 08:40
    • 10: Loading The Dataset 01:48
    • 11: Initial analysis on the dataset 12:22
    • 12: quiz 01:00
    • 13: Examining Missing Values 10:05
    • 14: Examining Unique Values 09:11
    • 15: Separating variables (Numeric or Categorical 03:12
    • 16: Examining Statistics of Variables 18:12
    • 17: quiz 01:00
    • 18: Numeric Variables (Analysis with Distplot): Lesson 1 14:29
    • 19: Numeric Variables (Analysis with Distplot): Lesson 2 03:57
    • 20: Categoric Variables (Analysis with Pie Chart): Lesson 1 13:55
    • 21: Categoric Variables (Analysis with Pie Chart): Lesson 2 15:40
    • 22: Examining the Missing Data According to the Analysis Result 10:09
    • 23: quiz 02:00
    • 24: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1 08:33
    • 25: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2 07:31
    • 26: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1 03:58
    • 27: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2 12:57
    • 28: Examining Numeric Variables Among Themselves Lesson 1 04:56
    • 29: Examining Numeric Variables Among Themselves Lesson 2 06:55
    • 30: Feature Scaling with the Robust Scaler Method 09:00
    • 31: Creating a New DataFrame with the Melt() Function 09:00
    • 32: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1 06:26
    • 33: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2 11:10
    • 34: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1 07:19
    • 35: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2 07:45
    • 36: Relationships between variables (Analysis with Heatmap): Lesson 1 06:05
    • 37: Relationships between variables (Analysis with Heatmap): Lesson 2 12:32
    • 38: quiz 02:00
    • 39: Dropping Columns with Low Correlation 03:47
    • 40: Visualizing Outliers 08:31
    • 41: Dealing with Outliers – Trtbps Variable: Lesson 1 09:58
    • 42: Dealing with Outliers – Trtbps Variable: Lesson 2 10:53
    • 43: Dealing with Outliers – Thalach Variable 08:22
    • 44: Dealing with Outliers – Oldpeak Variable 07:50
    • 45: Determining Distributions of Numeric Variables 05:02
    • 46: Transformation Operations on Unsymmetrical Data 04:56
    • 47: Applying One Hot Encoding Method to Categorical Variables 05:24
    • 48: Feature Scaling with the Robust Scaler Method 02:29
    • 49: Separating Data into Test and Training Set 07:04
    • 50: quiz 01:00
    • 51: Logistic Regression 06:54
    • 52: Cross Validation 05:41
    • 53: Roc Curve and Area Under Curve (AUC) 08:17
    • 54: Hyperparameter Optimization (with GridSearchCV) 12:54
    • 55: Decision Tree Algorithm 05:05
    • 56: Support Vector Machine Algorithm 05:02
    • 57: Random Forest Algorithm 06:17
    • 58: Hyperparameter Optimization (with GridSearchCV) 10:53
    • 59: quiz 01:00
    • 60: Project Conclusion and Sharing 03:32
    • 61: quiz 02:00
    • 62: Machine Learning with Real Hearth Attack Prediction Project 01:00

Course media

Description

Machine Learning, python, statistics, data science, machine learning python, python data science, machine learning a-z, data scientist, r, python for data science

Hello there,

Welcome to the “ Machine Learning Project: Heart Attack Prediction Analysiscourse.

Machine Learning & Data Science - Boost your Machine Learning skills with a real hands-on heart attack prediction project

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that.

A machine learning course teaches you the techniques and concepts behind the predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.

Do you know data science needs will create 11.5 million job openings by 2026?


Do you know the average salary is $100.000 for data science careers!

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

  • If you want to learn one of the employer’s most requested skills?

  • If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

  • If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you " Machine Learning with Real Hearth Attack Prediction Project " a straight-forward course for the Python Programming Language and Machine Learning.

In the course, you will have a down-to-earth way explanation of the project. With this course, you will carry out a data science project from start to finish. I made it simple and easy with a real-life example.

We will open the door of the Data Science and Machine Learning world and will move deeper. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn.

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn

Dive in now into; Machine Learning Project: Heart Attack Prediction Analysis

Data Science & Machine Learning - Boost your Machine Learning, and statistics skills with a real heart attack analysis project

See you in the course!

Who is this course for?

  • Anyone who wants to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
  • Those who want to compete in data science and machine learning
  • Those who want to improve their CV in Data Science, Machine Learning, Python with Kaggle
  • Anyone who is interested in Artificial Intelligence, Machine Learning, Deep Learning, in short Data Science
  • Anyone who have a career goal in Data Science
  • Anyone who needs a complete guide on how to start and continue their career with machine learning
  • Students Interested in Beginning Data Science Applications in Python Environment
  • Students Wants to Learn the Application of Supervised Learning (Classification) on Real Data Using Python

Requirements

  • Desire to master on machine learning a-z, python, data science, statistics
  • Knowledge of Python Programming Language
  • Knowledge of data visualization libraries like Seaborn, Matplotlib in Python
  • Knowledge of basic Machine Learning
  • Be Able to Operate & Install Software On A Computer
  • Free software and tools used during the course
  • Determination to learn and patience.

Questions and answers

Currently there are no Q&As for this course. Be the first to ask a question.

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Reviews

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FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

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An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.