Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn

This Machine Learning with Python tutorial gives an introduction to Machine Learning and how to implement Machine Learning algorithms in Python. By the end of this video, you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and hands-on in Python for Linear Regression and K-Means clustering algorithms.

Below are the topics covered in this Machine Learning tutorial:
1. Why Machine Learning? ( 01:09 )
2. Applications of Machine Learning ( 01:50 )
3. How does Machine Learning work? ( 03:33 )
4. Machine Learning Workflow ( 04:53 )
5. Steps to download Anaconda ( 06:13 )
6. Types of Machine Learning ( 09:53 )
7. Linear Regression Demo ( 13:51 )
8. K-Means Clustering Demo ( 26:02 )
9. Use Case - Weather Analysis ( 39:27 )

What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse

➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

👉 Learn More At:

🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - [phone]

[SimplilearnOfficial]

Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin.



Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you.
Thanks for watching the video. Cheers!

[macmillan]

thanks brother, It was perfect, i was looking for a complete machine learning tutorial for several times. this did the job for me! thanks again!!!

[wolfisraging]

So awesome and great explanation, thank u so much

[CorporateGamer]

Your presentation is top notch, thank you for your contributions!

[eytamatiasfeldman]

the video was fantastic and really helpful! thank you so much!

[sergedaney]

that was great. Thanks a lot for sharing!

[XgiliX]

Hey Richard, thank you for your videos, they are awesome!
What would you recommend for "predictive maintenance"? Using Python/Java or MatLab?
Since the job of predicting something requires analyzing a lot of data, i would say MatLab.
But what do you think?

[xxxtylerxxx]

you can copy and paste the data into an excel file and save as a csv

[seanwilliams]

Thank you for excellent video tutorial. Question is about last demo (Reinforcement Training) using K-means algorithm. I think I missed something that links the Temp and Pressure data to physical location. I'm assuming from the analysis after the K-means has done its work, you are somehow able to relate each red, green or blue marker shown in their respective cluster, to its original physical location. How would you do this? Many thanks.

[tkar]

That was ONE hour of real joy. Thanks a lot.

[andrewchi]

Thank you for sharing this! My colleague said that Linear Regression in Excel counts as AI, so we watched this together :)

[xuhaib]

This world is full of beautiful people like you. Thank you for the awesome tutorial

[jayantadeb]

Concise, to the point and really informative

[JI]

really good to see the Python action for ML

[ThePornoslav]

I just subscribed. Thank you so so so much. The very best ML with python tutorial.

[elyakimlev]

Coming from Andrew Ng's course on ML in Octave, this Python library seems sooo high level. Saves a lot of time doing a lot of math. But I'm glad I know what it's doing behind the scenes.

[andersonphillips]

Great introduction to ML with Python! I have subscribed to the channel. I even had to research some syntax for a minor modification to execute the code in Spyder IDE. I am still learning Python, put I understood quite a bit of concepts based on the Python lessons I have completed to date. I plan to review more of your videos.

[savvys]

which of the code do you actually need to type in into the jupyter notebook?

[___________]

Thanks for the video it was easy to follow. It would help a lot if the datasets were public please make it available.... Thank you

[catherinedumaguit]

How did you get the output :coefficient and intercept?