Hello Friends, Today I am going to share about the Top 17+ Kaggle Courses which are available free of cost.
If you are a beginner in programming, python, machine learning, data science, Artificial Intelligence, etc, these Free Kaggle Courses will be beneficial for you to learn from scratch.
If you have some knowledge about these courses, then also you can take these courses, as these courses will teach you from beginner to Advanced level.
These courses cover a wide range of topics, from data science to machine learning. Whether you’re a beginner or an experienced data scientist, there’s a course for you. So what are you waiting for? Start learning today!
Machine learning is a field of computer science that uses algorithms to teach computers how to learn without being explicitly programmed. The goal of machine learning is to use data and statistics to make predictions and decisions, which can be used to improve systems like self-driving cars, or provide more personalized content on websites, or even just help you find the right people for a job.
Course Apply Link: https://www.kaggle.com/learn/intro-to-machine-learning
Data visualization is the process of creating visual representations of data to make information more approachable. It can be used in many different ways, from simple charts to complex infographics.
There are many types of data visualization:
- Charts: A chart is a visual way to represent information that shows relationships between one or more variables. They come in all kinds of shapes and sizes, including pie charts and bar graphs.
- Maps: A map is a type of chart that shows geographical locations on a flat surface. Maps can show where things are within an area (such as by country), or they can show how long it takes to travel from one place to another (such as by state).
- Graphs: A graph is like a line graph or bar graph except that it uses shapes or colors instead of lines and bars to represent data points. For example, you might use a graph for showing how many people live in each state compared with their population size.
Course Apply Link: https://www.kaggle.com/learn/data-visualization
Programming is a lot like math. It’s a process of solving problems by creating solutions to those problems. That’s what programming is: solving problems with code.
Programming is different from other fields in that it requires you to be able to see the big picture and think about things in a way that might not be obvious to someone else. This can be hard when you’re just getting started, but it will get easier as you practice more!
Programming is the creation of a program by writing computer code to solve a problem. Creating programs to implement algorithms. Programming is the process of translating pseudocode or flowchart representations of algorithms into computer programs.
Course Apply Link: https://www.kaggle.com/learn/intro-to-programming
Python is a high-level, general-purpose programming language that is used by a lot of people. It was made by Guido van Rossum in 1991, and the Python Software Foundation has worked to improve it since then. It was made with code readability in mind, and its syntax lets programmers say what they want to say in fewer lines of code.
- Hello, Python:
A quick introduction to Python syntax, variable assignment, and numbers
- Functions and Getting Help
Calling functions and defining our own, and using Python’s builtin documentation
- Booleans and Conditionals
Using booleans for branching logic
Lists and the things you can do with them. Includes indexing, slicing and mutating
- Loops and List Comprehensions
For and while loops, and a much-loved Python feature: list comprehensions
- Strings and Dictionaries
Working with strings and dictionaries, two fundamental Python data types
- Working with External Libraries
Imports, operator overloading, and survival tips for venturing into the world of external libraries
Course Apply Link: https://www.kaggle.com/learn/python
Pandas is a Python module that is available as open source and is most commonly utilized for activities relating to data science, data analysis, and machine learning. It is built on top of a package called Numpy, which supports arrays with more than two dimensions.
- Creating, Reading, and Writing: You can’t work with data if you can’t read it. Get started here.
- Indexing, Selecting & Assigning: Pro data scientists do this a dozen times a day. You can, too!
- Summary Functions and Maps: Extract insights from your data.
- Grouping and Sorting: Scale up your level of insight. The more complex the dataset, the more this matters
- Data Types and Missing Values: Deal with the most common progress-blocking problems
- Renaming and Combining: Data come in from many sources. Help it all make sense together
Course Apply Link: https://www.kaggle.com/learn/pandas
- Introduction: Review what you need for this course.
- Missing Values: Missing values happen. Be prepared for this common challenge in real datasets.
- Categorical Variables: There’s a lot of non-numeric data out there. Here’s how to use it for machine learning.
- Pipelines: A critical skill for deploying (and even testing) complex models with pre-processing.
- Cross-Validation: A better way to test your models.
- XGBoost: The most accurate modeling technique for structured data.
- Data Leakage: Find and fix this problem that ruins your model in subtle ways.
Course Apply Link: https://www.kaggle.com/learn/intermediate-machine-learning
Feature engineering is a process of modifying features, or features of the product, to make them more useful, desirable, desirable, and/or desirable.
Course Apply Link: https://www.kaggle.com/learn/feature-engineering
8. Intro to SQL
SQL stands for “Structured Query Language.” It’s a language that lets you write code to query databases, and is one of the most common languages used in databases.
SQL is a structured language that uses keywords to define different types of data and operations. The syntax is similar to other languages like C++ or Java, so it’s easy to pick up once you’ve got a basic understanding of those languages.
Course Apply Link: https://www.kaggle.com/learn/intro-to-sql
9. Advanced SQL
Advanced SQL includes selecting columns, aggregate functions like MIN() and MAX(), the CASE WHEN statement, JOINs, the WHERE clause, GROUP BY, declaring variables, and subqueries.
Course Apply Link: https://www.kaggle.com/learn/advanced-sql
Deep Learning is a subset of Machine Learning that maps the input to the output through the use of mathematical functions. Deep learning was developed by Yann LeCun. These functions can pull out information or patterns from the data that are not repeated. This lets them figure out how the input and the output are related. 03-Oct-2022
Course Apply Link: https://www.kaggle.com/learn/intro-to-deep-learning
11. Computer Vision
Computer vision is the process of analyzing images and other data from the real world to create useful information. The computer vision system is used for a variety of applications in image recognition, object detection, motion detection, and mapping.
Course Apply Link: https://www.kaggle.com/learn/computer-vision
12. Time Series
A time series is a sequence of data points that represent one point in time, usually with a specific start date and an end date. Time series can be used to study the trend of an entity over time, or they can be used to monitor the progress of a process over many days, weeks or months.
Course Apply Link: https://www.kaggle.com/learn/time-series
13. Data Cleaning
Data cleaning is a process that can be used to clean data. The purpose of data cleaning is to remove information that may not be important or accurate, as well as to make sure the data is in a format that will make it easier for users to understand.
Course Apply Link: https://www.kaggle.com/learn/data-cleaning
Some of you may have heard the term “artificial intelligence” before. It’s a broad term that refers to machines that can think and act like humans, but aren’t actually human.
Ai ethics refers to ethical issues that arise when we use artificial intelligence in ways that are similar to how humans act. For example, if we were to create an AI to help us do our jobs, we might want it to be helpful and make our lives easier. But what if it gets bored or angry? We might want it to be kind and patient, but would it be able to understand what we mean when we tell it not to hurt people? (Yes)
These are questions that need answers before we start experimenting with ai ethics technology. And they’re questions that need answers now! If we don’t know exactly how our AI will behave, then we won’t know if its behavior is ethical or not—and then what?
Course Apply Link: https://www.kaggle.com/learn/intro-to-ai-ethics
Geospatial analysis is the process of deriving information from geographical data. This data can be in the form of points, lines, or polygons, and can be either vector or raster data. Geospatial analysts use a variety of techniques to analyze this data, including spatial statistics, to answer questions about the distribution, patterns, and trends of the phenomena being studied.
Geospatial analysis is a powerful tool for understanding our world. By analyzing data that is geo-referenced, we can better understand the patterns and relationships between different phenomena. This understanding can help us make better decisions about everything from urban planning to disaster relief.
Course Apply Link: https://www.kaggle.com/learn/geospatial-analysis
Machine learning is a powerful tool that can be used to automatically detect patterns and make predictions. However, the inner workings of machine learning algorithms can be complex and opaque, making it difficult to understand why the algorithm made a particular decision.
This lack of explainability can be a problem when machine learning is used for critical applications, such as medical diagnosis or credit approval. If the algorithm gets it wrong, it can be difficult to figure out why, and how to fix it.
There is active research into methods for making machine learning algorithms more explainable. Some approaches focus on making the algorithms themselves more transparent, while others aim to provide post-hoc explanations of the algorithm’s decisions.
Explainability is an important consideration when choosing a machine learning algorithm for a particular application. In some cases, a more explainable algorithm may be worth sacrificing some accuracy for the sake of transparency.
Course Apply Link: https://www.kaggle.com/learn/machine-learning-explainability
Game AI and Reinforcement Learning are two of the most popular and widely used AI technologies today. Game AI is used to create intelligent agents that can act and react in a game environment, while Reinforcement Learning is used to train agents to perform optimally in a given environment. Both technologies have been used extensively in a variety of applications, ranging from video games to autonomous vehicles.
Game AI and Reinforcement Learning share a number of similarities, including the use of agents and the ability to learn from experience. However, there are also some key differences between the two. Game AI is typically more focused on creating agents that can act intelligently in a given environment, while Reinforcement Learning is more focused on training agents to perform optimally in a given environment.
Both Game AI and Reinforcement Learning are important technologies that are widely used today. If you’re interested in either of these fields, then it’s worth taking some time to learn more about them.
Course Apply Link: https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning