Are you interested in the exciting world of machine learning and Python programming? If so, then you?ve come to the right place! This article will explore the best YouTube channels out there that cover topics such as python. Keep reading to discover the top YouTube channels for learning about all things machine learning and Python-related. With these channels, you?ll unlock a world of knowledge about this compelling topic, so let?s get started!
Python Life
Python Life YouTube channel is a great resource for people looking to learn , Machine Learning with Python in Telugu. They provide content like full courses, tutorials and projects related to Python life topics like Machine Learning, Computer Vision, Deep Learning, Django and more, making it a great place for beginners to start in Telugu. do!
Learn Python with Rune
Learn Python with Rune is a YouTube channel dedicated to helping beginners learn Python. With courses on finance, data science, and machine learning, as well as beginner topics like Pandas and Python, viewers can learn all they need to get up and running with Python. Plus, specialized one-on-one coaching makes it a supportive and interactive learning experience.
Python Programmer
The Python Programmer YouTube channel is a amazing resource for learners of all levels ? from complete beginners to experienced professionals. Here, users can find Python tutorials, data science and machine learning with Python videos, book reviews, data analysis and analytics tutorials, math and Pandas lectures, and more. With its easy to understand lessons, Python Programmer is the go-to channel for learning all about Python programming.
Keith Galli
Keith Galli's YouTube channel offers viewers an in-depth look into the ever expanding and evolving world of data science and machine learning with python. With tutorials on python 3, data visualization and analysis, pandas library, data engineering and more, viewers of all levels can understand how to code and learn the fundamentals of this technology. His channel has been inspired by his research at the Massachusetts Institute of Technology.
Tech With Tim
The Tech With Tim YouTube channel is all about machine learning with python. He has a lot of great python tutorials and python projects that you can follow along with. He is also a big fan of the python machine learning library and often uses it in his tutorials. If you're looking to learn more about python or machine learning, this is a great channel to check out.
Hallden
Hallden is a YouTube channel that specializes in content related to Machine Learning with Python, Software Engineering, One Day Builds, How to Use Python, Automation with Python, Flutter, Programming, Python, Finance, and Coding. It offers helpful tips and tutorials on these topics and more, making it a great resource for anyone in the programming/coding community.
codebasics
Codebasics is a YouTube channel providing a range of tutorials on programming topics such as Machine Learning with Python, Python for Data Science, Julia Programming and Git/GitHub. It offers a wide variety of coding lessons, ranging from text-based explanations to hands-on examples and tutorials in Jupyter. It is a great resource for anyone wanting to learn the fundamentals of programming and Machine Learning with Python.
AI Sciences
AI Sciences is a YouTube channel dedicated to providing educational resources for those starting out or expanding their knowledge in the fields of machine learning with Python, data science, big data, deep learning, natural language processing, artificial intelligence, and Python programming. It offers tutorials for AI beginners, helping them to build a foundation of knowledge in the field to advance their skills.
edureka!
The edureka! YouTube channel is a great resource for those looking to learn about machine learning with python, big data, apache spark, data science, and other trending technologies. The channel features training videos and online tutorials that are perfect for beginners and those looking to brush up on their skills. Additionally, the channel covers android development, web development, and cloud computing.
As of now, the channel has over 6,367 videos and 3,320,000 subscribers. I highly encourage you to check out their videos if you're interested in learning more about these topics. Thanks for watching!
Unfold Data Science
Unfold Data Science is a YouTube channel that provides tutorials and courses on a variety of topics including machine learning with Python, data science, artificial intelligence, R programming, deep learning, data science course, data science projects, machine learning course and data analytics. It is a great resource for anyone interested in learning more about data science and its related fields.
PyData
PyData is an educational YouTube channel focused on topics related to machine learning with python. It offers a range of tutorials and guidance on data, analytics, big data analysis, computing, array, visualization and data science, all using python. An array of videos are available to explore advanced data concepts and related machine learning techniques. This channel is a great resource for anyone looking to get started with python and data analysis.
Simplilearn
Simplilearn's YouTube channel offers an array of tutorials and content related to fields such as Machine Learning with Python, Cyber Security, Digital Marketing, Data Science, Six Sigma, Artificial Intelligence, Business Analyst, Deep Learning, Social Media Marketing and Ethical Hacking, helping learners stay updated and get certified. With their courses, they help professionals advance their careers, brush up their knowledge and gain new skills.
NeuralNine
NeuralNine is a YouTube channel which provides tutorials on machine learning with python, programming tutorials and other resources related to machine learning using the popular Python language. It offers comprehensive and easy-to-understand tutorials for those who are interested in getting started with machine learning, building projects and gaining new skills.
freeCodeCamp.org
The freeCodeCamp.org YouTube channel is a perfect resource for learning to code. The channel features a variety of videos on topics such as machine learning with python, coding bootcamp, learn to code, software engineer, full stack, front end, web development, web design, and data science. The channel is a great resource for anyone looking to learn more about coding and software development.
As of now, the freeCodeCamp.org YouTube channel has 1,306 videos and 5,550,000 subscribers. The channel is constantly adding new videos and expanding their content offerings, so be sure to check back often for new and exciting content. Thanks for watching!
sentdex
Sentdex is an educational YouTube channel dedicated to teaching programming and machine learning with Python. They provide tutorials and how-to videos on a variety of topics to help viewers learn Python and take on related projects. Their videos are focused on providing a comprehensive understanding of the Python programming language and advanced topics such as machine learning.
Siddhardhan
Siddhardhan's YouTube channel is a great resource for those interested in machine learning, data science and artificial intelligence, offering tutorials and training on a variety of topics ranging from machine learning with Python and machine learning projects to courses in deep learning and machine learning courses in Python. With helpful video content and insightful explanations, the channel provides a comprehensive learning experience.
Programming with Mosh
Programming with Mosh is a popular YouTube channel that covers a wide range of topics related to programming and coding, such as machine learning with Python, JavaScript, React, Node, Angular, .NET, and ASP.NET. The channel provides helpful tutorials and tips for those who are just starting out in coding and want to become a professional programmer. It is an excellent resource for all skill levels.
Great Learning
Great Learning YouTube channel is a comprehensive resource for learning about Machine Learning with Python, Artificial Intelligence, Artificial Neural Network, Data Science, Machine Learning, Data Analytics, and Big Data Analytics. Through their content, they provide courses and tutorials to help viewers learn more about the fields of Data Analytics, Big Data, and AI. They offer practical advice and insights on real-world applications of these topics, making them an invaluable resource for anyone looking to master the latest technology.
Patrick Loeber
Patrick Loeber's YouTube channel is an excellent resource for those wanting to learn about machine learning with Python. Patrick's channel offers video tutorials, tips, strategies, and insights into the world of machine learning and technology, as well as lifestyle advice. With an emphasis on knowledge acquisition, Patrick's channel is a great resource for those interested in quickly mastering the latest technologies.
Computer Science
Data Professor
Data Professor is a popular YouTube channel which provides tutorials about data science and machine learning using Python. It covers topics such as data mining, data scientist, computational biology, data analyst, knowledge discovery in data, drug discovery, and R programming. It is perfect for anyone wanting to learn and excel in the fields of data science and machine learning.
Learn with Whiteboard
Learn with Whiteboard is a popular YouTube channel hosted by Amarpreet Singh, focused on teaching people how to use Machine Learning with Python, Digital Marketing and Brand Building strategies. It also covers topics such as Social Media, Search Engine Optimization, Whiteboard Marketing, Whiteboard Programming and Whiteboard Entrepreneurship. Followers of the channel can gain valuable insight into Machine Learning and its practical applications.
Understanding the Basics of Python for Machine Learning
Python is quickly becoming one of the most popular programming languages in the Machine Learning space. That’s why it is important to understand the basics of Python if you want to get started in the field.
Python provides a wide range of libraries, frameworks and data science packages that make it a great language to use when developing Machine Learning applications. It is also easy to learn and understand, making it popular among people starting out in the field. To get started, you should have a basic understanding of the basic syntax and functions associated with the language. Additionally, you should familiarize yourself with some of the helpful libraries and frameworks available for developing ML applications with Python, such as Scikit-Learn, TensorFlow and Keras.
Once you feel comfortable with the basics of Python, you can then move on to exploring the various Machine Learning algorithms available. These algorithms allow you to extract meaningful insights from data and provide the ability to make predictions. Knowing how to use them is a great skill to have for any Machine Learning practitioner. It is also important to understand how to apply these algorithms and how to interpret the results generated. With time and practice, you will become an expert in the fundamentals of Machine Learning with Python.
Steps to Create Your Machine Learning Model Using Python
Building a machine learning model using Python involves a few steps that should be followed to ensure the model is accurate and performs as expected. Here’s what you need to do to create your machine learning model using Python:
- The first step is to understand your data and identify the problem you want to address. You’ll need to understand the data set you have and clean it if necessary, standardizing the data types and filling in missing values. Once the data is ready, you can move on to identifying the problem and collecting the appropriate data for the machine learning model.
- The second step is to choose the best model for the problem and design it. You can use various Python libraries such as SciKit-learn for this task. You can also use various techniques such as classification, clustering, and regression. After deciding on the model, you can use libraries such as NumPy and Pandas to manipulate the data and build the model.
- The third step is to train and test the model using Python. You’ll need to split the data into training and testing sets, build the model using training data, and validate its performance by testing the model on unseen data. You can then use the metrics such as accuracy, precision, and recall to evaluate its performance. After the model is tested, you can make any changes if needed to improve its performance. Finally, you can deploy the model and use it in a production environment.
Creating a machine learning model using Python is a complex, multi-step process that should be done with care. However, the result will be worth the effort, and Python provides the tools necessary to make that a reality. With the right knowledge and some practice, you’ll be well on your way to building a successful model.
Identifying and Overcoming Common Challenges in Machine Learning Development with Python
Python is open source and has an extensive library of modules which makes it ideal for data exploration and manipulation tasks. However, there can be some common challenges associated with developing machine learning applications using Python.
- One of the frequent challenges in machine learning development with Python can be dealing with missing values in datasets. This involves identifying any missing values, deciding on how to handle them, and then implementing the necessary methods. Depending on the complexity of the project, this can be a time-consuming and frustrating task that requires careful consideration of the methods to be used. A few common approaches that can be used are discarding the rows with missing values, applying the mean or median value to those rows, and using a machine learning model to generate predicted values.
- Another common challenge with machine learning development with Python is dealing with highly imbalanced datasets. This involves dealing with datasets that have greatly unequal amounts of instances or classes within the dataset. This can lead to overfitting or bias in your model if not handled properly. To properly address this, utilize techniques such as oversampling, undersampling, adding noise, and generating synthetic samples. Also consider using algorithms that have built-in support for working with imbalanced problems such as decision trees.
By correctly recognizing and addressing common challenges associated with machine learning development using Python, the development process can be more efficient and successful. Researching the appropriate techniques for specific applications and testing out different approaches are important steps to take when developing machine learning applications with Python.
