What you would learn in Kaggle Master with Heart Attack Prediction Kaggle Project course?
Kaggle is an affiliate of Google LLC and is an online community for Data scientists and machine-learning practitioners. Kaggle allows users to search and publish datasets, investigate and develop models within the web-based science-based environment, collaborate alongside other data scientists and computer scientists, and participate in contests to tackle problems in data science.
Kaggle offers a zero-setup customized Jupyter Notebooks environment. You can access accessible GPUs and an extensive repository of published community codes and data.
Kaggle provides a service that allows data scientists to take on machine learning challenges. The challenges could range from predicting prices for housing to detecting cancerous cells. Kaggle is home to a large community of data scientists that have always been willing to assist other data scientists with their challenges. Alongside contests, Kaggle provides a wealth of training and other resources to aid you in gaining experience in machine learning.
Machine learning isn't only useful for predictive texting or phone voice recognition. Machine learning is continuously being used to solve new industries and new issues. It doesn't matter if you're a marketer, video game designer, or programmer, Oak Academy has a course that will assist you in applying machine learning to your job. It's difficult for us to think of our life without machine learning. Predictive texting, email filtering, and virtual assistants such as Amazon's Alexa and Siri on the iPhone Siri are all techniques built upon mathematical algorithms, machine-learning models, and algorithmic processes.
Machine learning is the term used to describe systems that can generate predictions using models trained on real-world data. For example, suppose we'd like to develop a system that recognizes whether a cat is present in a photograph. The first step is to gather photos to build our machine-learning model. In this stage of training, we feed images into the model together with details about whether or not they contain cats. When training the model, it can recognize patterns in the images the closest to cats. The model then uses the patterns it learned during training to identify if new images it's fed include cats. It is possible to employ a neural network to understand these patterns; however, machine learning could be far more straightforward than it sounds.
An Machine Learning course will teach you the technologies and concepts that underlie predictive text, virtual assistants, and artificial intelligence. It will help you develop the foundational skills needed to progress towards building neural networks and creating more complex tasks using learning the Python and R programs languages. The training in machine learning will help to keep up to date with the latest developments, technologies, and applications in this field.
We have more information than we ever have. However, just data can't provide a lot of information about the world surrounding us. It is necessary to analyze the data and find subtle patterns. This is the area where data science can help. This is where data science comes in. Data science employs algorithms to comprehend the data in its raw form. The primary distinction between data science and traditional data analysis is its emphasis on prediction. Data science seeks out patterns in data and then uses those patterns to forecast future data. It uses machine learning to process vast data, find patterns, and forecast patterns. Data science involves preparing, analyzing, processing, and analyzing data. It is a blend of many sciences and, as a discipline is developed, it creates new methods to analyze data and test the current methods.
Information Science application is a highly sought-after ability in various industries worldwide, such as education, finance, transportation manufacturing, human resources, and banking. Learn about data science with Python, statistical machine learning, statistics, and other subjects to increase your knowledge. Take advantage of data science courses when you're interested in the study, research, or analytics.
What can you expect to learn?
This class will begin with the basics and progress through the final "Kaggle" with examples.
In the course, you will be exposed to the following subjects:
What exactly is Kaggle?
Registration with Kaggle or Member Login Methods
Learning about the Kaggle Homepage
Competitions on Kaggle
Datasets on Kaggle
Studying Code Section in Kaggle Code Section in Kaggle
Is there a discussion on Kaggle?
Courses at Kaggle
The Rankings of Users on Kaggle
Blog and Sections on Documentation
Users Page Reviews on Kaggle
The Kaggle is a treasure. The Kaggle
Publishing Notebooks for Kaggle
What should be done to Be Successful in Kaggle?
Recognizing Variables In Dataset
Python Libraries are required. Python Libraries
Dataset loading Dataset
Initial analysis of the dataset
Reviewing Missing Values
Examining Unique Values
Differentiating variables (Numeric or Categorical)
Reviewing the Statistic of Variables
Numeric Variables (Analysis using Distplot)
Categoric Variables (Analysis using Pie Chart)
Analyzing the Data that is missing according to the Analysis Results
Numeric Variables Target Variable
Analyzing Numeric Variables about Each Other
Features Scaling using Robust Scaler Method Robust Scaler Method
Create a new DataFrame using the Melt() Function
Numerical Variables - Categorical Variables
Preparation of Modelling Project
- Kaggle is an affiliate of Google LLC, an online community for machine learning and data scientists practitioners.
- Kaggle provides a service on which data scientists compete with machine learning-related problems. These challenges could range from predicting the price of housing to finding out.
- Machine learning refers to the systems that can make predictions using a model trained on real-world data.
- Machine learning isn't only useful for text prediction or smartphone voice recognition. Machine learning is continuously being applied to new sectors and new technologies.
- Data science is the process of gathering, analyzing, or processing information. It draws on a variety of sciences, and as a discipline progresses, it develops through the development of new algorithms.
- Data science applications are an essential skill for numerous industries worldwide, including transportation, finance, manufacturing, education, human resources, and manufacturing.
- Data science uses algorithms to analyze the data in its raw form. The primary distinction between data science from traditional data analysis is the focus on predictions.
- Data scientists use machine learning to uncover hidden patterns in vast amounts of raw data to help solve real-world problems.
- What exactly is Kaggle?
- Signing up for a Kaggle account Kaggle as well as Member Login Methods
- Learning about the Kaggle Homepage
- Competitions on Kaggle
- Datasets on Kaggle
- Looking at Code Section in Kaggle Code Section in Kaggle
- How do you define Discussion Kaggle?
- Courses offered by Kaggle
- Ratings Among Users on Kaggle
- Blog and Sections for Documentation
- Kaggle User Review of the Page on Kaggle
- The Kaggle is a treasure. The Kaggle
- Publishing Notebooks for Kaggle
- What can you do to be Successful in Kaggle?
- The first step in the Project
- The Notebook Designs to be Utilized for the Project
- Reviewing the Project Topic
- Recognizing Variables in Datasets
- Essential Python Libraries
- The Dataset is loaded Dataset
- Initial analysis of the data
- Checking for missing values
- Examining Unique Values
- Differentiating variables (Numeric or Categorical)
- Studying the Statistic of Variables
- Numeric Variables (Analysis using Distplot)
- Categoric Variables (Analysis using Pie Chart)
- Analyzing the Data that is missing according to the Analysis Results
- Numeric Variables and Target Variables (Analysis using FacetGrid)
- Categoric Variables Target Variable (Analysis using the Count Plot)
- Examining the Numeric Variables among Themselves (Analysis with a Pair Plot)
- The Feature Scaling Method employs the Robust Scaler Method for the New Visualization
- Making a New DataFrame by using the Melt() Function
- Numerical Variables - Categorical Variables (Analysis using Swarm Plot)
- Numerical Variables - Categorical Variables (Analysis using Box Plot)
- Relations between variables (Analysis using Heatmap)
- Dropping Columns with low correlation
- Visualizing Outliers
- Handling Outliers
- The process of determining distributions of Numeric Variables
- Transformation Operations on Data that is Unsymmetrical
- The Application of One Hot Coding Method for Categorical Variables
- Scaling Feature Using The Robust Scaler Method for Machine Learning Algorithms
- Separating the Training and Test Set
- Logistic Regression
- Cross-Validation of 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