Generative AI with Heart Attack Prediction Kaggle Project

Generative AI with Heart Attack Prediction Kaggle Project
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12-25-2024, 02:36 PM -

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[h1]Free Download Generative AI with Heart Attack Prediction Kaggle Project[/h1]
Published: 12/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 23h 9m | Size: 8.48 GB
Master in Data Science and Use Gen AI tools to predict heart attacks using Kaggle datasets and ChatGPT-4o's super power

[h2]What you'll learn[/h2]
Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.
Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detect
Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning isn't just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and ne
Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithm
Data science application is an in-demand skill in many industries worldwide - including finance, transportation, education, manufacturing, human resources
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
What is Kaggle?
Registering on Kaggle and Member Login Procedures
Getting to Know the Kaggle Homepage:
Competitions on Kaggle
Datasets on Kaggle
Examining the Code Section in Kaggle
What is Discussion on Kaggle?
Courses in Kaggle
Ranking Among Users on Kaggle
Blog and Documentation Sections
User Page Review on Kaggle
Treasure in The Kaggle
Publishing Notebooks on Kaggle
What Should Be Done to Achieve Success in Kaggle?
First Step to the Project
Notebook Design to be Used in the Project
Examining the Project Topic
Recognizing Variables in Dataset
Required Python Libraries
Loading the Dataset
Initial analysis on the dataset
Examining Missing Values
Examining Unique Values
Separating variables (Numeric or Categorical)
Examining Statistics of Variables
Numeric Variables (Analysis with Distplot)
Categoric Variables (Analysis with Pie Chart)
Examining the Missing Data According to the Analysis Result
Numeric Variables - Target Variable (Analysis with FacetGrid)
Categoric Variables - Target Variable (Analysis with Count Plot)
Examining Numeric Variables Among Themselves (Analysis with Pair Plot)
Feature Scaling with the Robust Scaler Method for New Visualization
Creating a New DataFrame with the Melt() Function
Numerical - Categorical Variables (Analysis with Swarm Plot)
Numerical - Categorical Variables (Analysis with Box Plot)
Relationships between variables (Analysis with Heatmap)
Dropping Columns with Low Correlation
Visualizing Outliers
Dealing with Outliers
Determining Distributions of Numeric Variables
Transformation Operations on Unsymmetrical Data
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
Separating Data into Test and Training Set
Logistic Regression
Cross Validation for Logistic Regression Algorithm
Roc Curve and Area Under Curve (AUC) for Logistic Regression Algorithm
Hyperparameter Optimization (with GridSearchCV) for Logistic Regression Algorithm
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Hyperparameter Optimization (with GridSearchCV) for Random Forest Algorithm
Project Conclusion and Sharing
Data analysis is the process of studying or manipulating a dataset to gain some sort of insight
Big News: Introducing ChatGPT-4o
How to Use ChatGPT-4o?
Chronological Development of ChatGPT
What Are the Capabilities of ChatGPT-4o?
As an App: ChatGPT
Voice Communication with ChatGPT-4o
Instant Translation in 50+ Languages
Interview Preparation with ChatGPT-4o
Visual Commentary with ChatGPT-4o
ChatGPT for Generative AI Introduction
Accessing the Dataset
First Task: Field Knowledge
Continuing with Field Knowledge
Delving into the Details of Variables
Exploratory Data Analysis (EDA)
Categorical Variables (Analysis with Pie Chart)
Importance of Bivariate Analysis in Data Science
Numerical Variables vs Target Variable
Correlation Between Numerical and Categorical Variables and the Target Variable
Numerical Variables - Categorical Variables
Numerical Variables - Categorical Variables with Swarm Plot
Relationships between variables (Analysis with Heatmap)
Preparation for Modeling
Dropping Columns with Low Correlation
Struggling Outliers
Visualizing Outliers
Dealing with Outliers
Determining Distributions
Determining Distributions of Numeric Variables
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
Logistic Regression Algorithm
Cross Validation
ROC Curve and Area Under Curve (AUC)
ROC Curve and Area Under Curve (AUC)
Hyperparameter Tuning for Logistic Regression Model
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Generative AI is artificial intelligence (AI) that can create original content in response to a user's prompt or request
[h2]Requirements[/h2]
Desire to learn about Kaggle
Watch the course videos completely and in order
Internet Connection.
Any device such as mobile phone, computer, or tablet where you can watch the lesson.
Learning determination and patience.
Nothing else! It's just you, your computer and your ambition to get started today
Desire to improve Data Science, Machine Learning, Python Portfolio with Kaggle
Free software and tools used during the course
A working computer (Windows, Mac, or Linux)
Motivation to learn the the second largest number of job postings relative AI among all others
Desire to learn Generative AI & ChatGPT
Curiosity for Artificial Intelligence and Data Science
Basic python knowledge
LIFETIME ACCESS
[h2]Description[/h2]
Hello There, Welcome to the" Generative AI with Heart Attack Prediction Kaggle Project " course. Master in Data Science and Use Gen AI tools to predict heart attacks using Kaggle datasets and ChatGPT-4o's super powerArtificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age. In today's data-driven world, the ability to analyze data, extract meaningful insights, and apply machine learning algorithms is more important than ever. This course is designed to guide you step by step through this journey, from the fundamentals of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.Machine learning defines systems that make predictions using models trained on real-world data. For instance, let's say we want to create a system that can determine whether an image contains a cat. First, we gather many images to train our machine learning model. During the training phase, we feed the images to the model along with information about whether or not they contain a cat. Throughout the training process, the model learns patterns in the images most closely associated with cats. The model can then use these learned patterns to predict whether a new image contains a cat.A machine learning course teaches you the concepts and technologies behind AI, including predictive text, virtual assistants, and much more. By using programming languages like Python and R, you will develop the foundational skills needed to build neural networks and create more complex functions.We have more data than ever before. However, data alone doesn't tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes into play. Data science uses algorithms to make sense of raw data. The key difference between data science and traditional data analysis is its focus on prediction.Data science is a highly sought-after skill across many industries worldwide, including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to expand your knowledge.If you're an aspiring data scientist, Kaggle is the best place to start. Many companies offer job offers to those who rank highly in their competitions. In fact, if you reach one of the top positions, Kaggle could become your full-time job.What This Course Offers:In this course, you will gain a deep understanding of the entire data analysis and machine learning pipeline. Whether you're new to this field or looking to expand your existing knowledge, our hands-on approach will equip you with the skills needed to tackle real-world data challenges.You will begin by diving into the fundamentals of EDA, where you'll learn how to explore, visualize, and interpret datasets. Through step-by-step guidance, you'll master the techniques for cleaning, transforming, and analyzing data to uncover trends, patterns, and outliers-crucial steps before moving on to predictive modeling.Why ChatGPT-4o?This course uniquely integrates the next-generation AI tool, ChatGPT-4o, to assist you throughout your learning journey. You'll see firsthand how this cutting-edge AI is transforming data analysis workflows and unlocking new levels of efficiency and creativity.Mastering Machine Learning:Once your foundation in EDA is solid, the course will guide you through advanced machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and more. You'll learn not only how these algorithms work but also how to implement and optimize them using real-world datasets. By the end of the course, you'll be proficient in selecting the right models, fine-tuning hyperparameters, and evaluating model performance with confidence.What You'll Learn:Exploratory Data Analysis (EDA): Master the techniques for analyzing and visualizing data, detecting trends, and preparing data for modeling.Machine Learning Algorithms: Implement algorithms like Logistic Regression, Decision Trees, and Random Forest, and understand when and how to use them.ChatGPT-4o Integration: Leverage the AI capabilities of ChatGPT-4o to automate workflows, generate code, and improve data insights.Real-World Applications: Apply the knowledge gained to solve complex problems and make data-driven decisions in industries such as finance, healthcare, and technology.Next-Gen AI Techniques: Explore advanced techniques that combine AI with machine learning, pushing the boundaries of data analysis.Why This Course Stands Out:This course stands out by blending theory with practice. Unlike traditional data science courses, you'll not only learn data analysis and machine learning but also apply these skills in real-world scenarios with guidance from ChatGPT-4. The hands-on projects ensure that you'll be able to tackle any data challenge in your career. Data science is crucial across fields, from government security to dating apps, and careers in this field are in high demand. Whether you're new to Data Science with Python or an experienced developer looking to transition, our "Generative AI for Heart Attack Prediction with Kaggle" course is designed to boost your CV and enhance your skills.In this course, you will Learn:What is Kaggle?Registering on Kaggle and Member Login ProceduresGetting to Know the Kaggle Homepage: Competitions on KaggleDatasets on KaggleExamining the Code Section in KaggleWhat is Discussion on Kaggle?Courses in KaggleRanking Among Users on KaggleBlog and Documentation SectionsUser Page Review on KaggleTreasure in The KagglePublishing Notebooks on KaggleWhat Should Be Done to Achieve Success in Kaggle?Recognizing Variables In DatasetRequired Python LibrariesLoading the DatasetInitial analysis on the datasetExamining Missing ValuesExamining Unique ValuesSeparating variables (Numeric or Categorical)Numeric Variables (Analysis with Distplot)Examining the Missing Data According to the Analysis ResultNumeric Variables - Target VariableExamining Numeric Variables Among ThemselvesCreating a New DataFrame with the Melt() FunctionPreparation for Modelling ProjectModelling ProjectProject SharingBig News: Introducing ChatGPT-4oHow to Use ChatGPT-4o?Chronological Development of ChatGPTWhat Are the Capabilities of ChatGPT-4o?As an App: ChatGPTVoice Communication with ChatGPT-4oInstant Translation in 50+ LanguagesInterview Preparation with ChatGPT-4oVisual Commentary with ChatGPT-4oChatGPT for Generative AI IntroductionAccessing the DatasetFirst Task: Field KnowledgeContinuing with Field KnowledgeLoading the Dataset and Understanding VariablesDelving into the Details of VariablesLet's Perform the First AnalysisExamining Statistics of VariablesExploratory Data Analysis (EDA)Categorical Variables (Analysis with Pie Chart)Importance of Bivariate Analysis in Data ScienceNumerical Variables vs Target VariableCategoric Variables vs Target VariableCorrelation Between Numerical and Categorical Variables and the Target VariableNumerical Variables - Categorical VariablesNumerical Variables - Categorical Variables with Swarm PlotRelationships between variables (Analysis with Heatmap)Preparation for ModelingDropping Columns with Low CorrelationStruggling OutliersVisualizing OutliersDealing with OutliersDetermining DistributionsDetermining Distributions of Numeric VariablesApplying One Hot Encoding Method to Categorical VariablesFeature Scaling with the RobustScaler Method for Machine Learning AlgorithmsSeparating Data into Test and Training SetLogistic Regression AlgorithmCross ValidationROC Curve and Area Under Curve (AUC)Hyperparameter Optimization (with GridSearchCV)Hyperparameter Tuning for Logistic Regression ModelDecision Tree AlgorithmSupport Vector Machine AlgorithmRandom Forest AlgorithmWhat is Kaggle?Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-Published: data & code.Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems. In addition to the competitions, Kaggle also has many tutorials and resources that can help you get started in machine learning.If you are an aspiring data scientist, Kaggle is the best way to get started. Many companies will give offers to those who rank highly in their competitions. In fact, Kaggle may become your full-time job if you can hit one of their high rankings.What is machine learning?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 around 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. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.What is data science?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 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.Is Kaggle good for beginners?Despite the differences between Kaggle and typical data science, Kaggle can still be a great learning tool for beginners. Each competition is self-contained. You don't need to scope your own project and collect data, which frees you up to focus on other skills.How does Kaggle work?Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem.Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena.What type of skills do you need to compete on Kaggle?You should be comfortable with data analysis and machine learning if you're looking to get involved in competitions.Data science is a very broad term that can be interpreted in many ways depending on who you talk to. But suppose we're talking specifically about competitive data science like what you see on Kaggle. In that case, it's about solving problems or gaining insights from data.It doesn't necessarily involve machine learning, but you will need to understand the basics of machine learning to get started. There are no coding prerequisites either, though I would recommend having some programming experience in Python or R beforehand.That being said, if competitive data science sounds interesting to you and you want to get started right away, we have a course for that on Duomly!How does one enter a competition on Kaggle?The sign-up process for entering a competition is very straightforward: Most competitions ask competitors to submit code that meets specific criteria at the end of each challenge. However, there may be times when they want competitors to explain what algorithms they used or provide input about how things work.What are some Kaggle competitions I could consider solving?Suppose you want to solve one of their business-related challenges. In that case, you'll need to have a good understanding of machine learning and what models work well with certain types of data. Suppose you want to do one of their custom competition. You'll need to have a background in computer science to code in the language associated with the problem.How do Kaggle competitions make money?Many companies on Kaggle are looking for solutions, so there is always a prize attached to each competition. If your solution is strong enough, you can win a lot of money!Some of these competitions are just for fun or learning purposes but still award winners with cash or merchandise prizes.What tools should I use to compete on Kaggle?The most important tool that competitors rely on every day is the Python programming language. It's used by over 60% of all data scientists, so it has an extremely large community behind it. It's also extremely robust and has many different packages available for data manipulation, preprocessing, exploration to get you started.TensorFlow is another popular tool that machine learning enthusiasts use to solve Kaggle competitions. It allows quick prototyping of models to get the best possible results. Several other tools are used in addition to Python and Tensorflow, such as R (a statistical programming language), Git (version control), and Bash (command-line interface). Still, I'll let you research those on your own!What is the main benefit of using Kaggle to solve problems?Kaggle aims to give you the tools necessary to become a world-class data scientist. They provide you with access to real data in real-time so you can practice solving problems similar to what companies face around the world.Who would be interested in using Kaggle?With many tutorials and datasets readily available, Machine Learning enthusiasts would be very interested in Kaggle.It is an excellent place to learn more about machine learning, practice what they've learned, and compete with other data scientists. This will help them become better at their craft.Data analysts that want to use machine learning in their work can refer to Kaggle when choosing tools to improve the performance of business-related tasks such as forecasting sales numbers or predicting customer behavior.In addition, businesses who are looking for third-party solutions can benefit from Kaggle's extensive list of companies offering the service they need.If you need machine learning services, don't hesitate to contact us. We have a team of experts who can help you with your needs.What is the difference between machine learning and artifical intelligence?Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.What does a data scientist do?Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.How long does it take to become a data scientist?This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field.How can I learn data science on my own?It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Oak Academy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated.What skills should a data scientist know?A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python - although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings, data scientists require knowledge of visualizations. Data visualizations allow them to share complex data in an accessible manner.VIs data science a good career?The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds. If this sounds like a great work environment, then it might be a promising career for you.Why would you want to take this course?Our answer is simple: The quality of teaching.When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.Video and Audio Production QualityAll our videos are created/produced as high-quality video and audio to provide you with the best learning experience.You will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadWe offer full support, answering any questions.If you are ready to learn Now Dive into; " Generative AI with Heart Attack Prediction Kaggle ProjectMaster in Data Science and Use Gen AI tools to predict heart attacks using Kaggle datasets and ChatGPT-4o's super power " course.See you in the course!
[h2]Who this course is for[/h2]
Anyone who wants to start learning AI & ChatGPT
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.
For those who want to compete in data science and machine learn by learning about Kaggle
Anyone who wants to learn Kaggle
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 is interested in Artificial Intelligence, Machine Learning, Deep Learning, in short Data Science
Anyone who needs a complete guide on how to start and continue their career with AI & Prompt Engineering
And also, who want to learn how to develop Prompt Engineering
Data Analyst who want to apply generative AI tools to automate repetitive tasks, streamline data workflows, and generate insights.
Data Engineer who wants to optimize data pipelines and automate data-related tasks.
AI and Machine Learning Enthusiasts who want to deepen their understanding of how generative AI models, like ChatGPT, can be applied to real-world data tasks.
Business Analysts who wants to understand how generative AI can assist in generating business insights from raw data
Students or Beginners in Data Science who want to get familiar with cutting-edge AI tools and apply them to basic data analysis, engineering, or project automation.
Homepage:

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https://www.udemy.com/course/generative-ai-with-heart-attack-prediction-kaggle-project/

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