Amazing- I really liked how casually you would think to add colors, trend line and straight away go and add a few lines to reflect in output. Shows how comfortable you’re- for a beginner like me- that was so instructive when you speak out your thoughts on the go.

You literally give the brief on machine learning in a very simple and easy way

This is the clearest ML Tutorial I’ve ever watched ❤❤❤

This video is amazing! You explain things so clearly, and the quality is excellent.

🐍My heart is pounding so hard! I can't believe it was that 'simple' to make a model!! It gives me so much confidence to start learning more and add more models to the code I've made

I didn't have any prior knowledge of Data Science or Machine Learning, but as a visual learner, I finally understand the purpose of the mathematical equation y = f(X). Initially, after watching a couple of videos and starting from a math tutorial, I was confused about the relevance of math in this field. But now I see its importance, and I am grateful for this new understanding. Thanks @DataProfessor

This was the easiest video on the ML model. Thanks Prof.

You made the process very simple for us! Great Video! Keep up the good Work!

🐍Amazing depth covered in a short time-frame! Thank-you for compiling this introductory ML tutorial!

After this tutorial, I can now start my ML Journey confidently. May God bless you Data Professor to continue doing this good work. Cheers

This is amazing Data Professor. Hope to see more of this stuff.

Excellent video. You explained everything very simple. Which i learnt 1 month in classroom you just explained in few minutes. It is quick recap for me. Thank you..

🐍 This was SO helpful. I feel like I had just been going through the motions in that process before, but you explained WHY each step is done. And I will definitely be using headings from now on to create that table of contents. Thanks, Data Professor, for another great video!

🐍This was my first video on ML and you made it really easy to follow through. I did this because I wanted to feel how it is like to do this , and I like it. I will definitely follow this through and study the actual maths behind it. Thank you for keeping me motivated.

Awesome as always.. I'm applying this to my QSAR dataset in a bit.. Thanks for churning out great n useful content as always.. You d bestest Data Professor.

The only video that I understood about ML. Thank you!

🐍 Thanks for this. I appreciate you not just going straight into the core material, but talking about the organization with the headers.

One of the best beginner videos available on YT😍

TY you saved me for training our model for my Final Year Project

Hi! Great video. A quick question... when do we use linear regression as in statistics and when do we use it as a ML algorithm? Both try to find coefficients, both try to minimize the error (however it is defined) but I am still confused on what is the difference between the classical linear regression I learnt in basic algebra course and the ML procedure. Thanks!

🐍 Your explanation was clear, concise, and appreciated! You are an amazing teacher who hits all the important points without adding any confusion. Thank you for your time in doing this!

This was awesome, thanks to your knowledge I feel I can have fun with experimenting with different algorithms and its parameters.🐍

I usually don't spend time commenting on youtube, but dude this is a great video! Easy to follow alone, and very helpful. Thank you

🐍 awesome video. This one was so much easier to follow along compared to other tutorials I have watch.

🐍 this video got me through a few projects. Thank you!

Thank you, I love this video. I am new to ML. Very thorough in your thoughts and explanation. Question: What do you do with the prediction afterwards?

The best ML tutorial i just watched. Hat's off for your work sir. I really enjoyed and explanations were really great. Thanks again. You earned a subscriber here. keep up the great work sir.

Thank you so so much for this tutorial, your videos deserve much more views! Could you please also do a video explaining how to do k-cross validation as well as the methods to measure and compare the accuracy of ML models (confusion matrix, F1 score ect.)? Thank you so much for your videos I learn much more from you than my other profs at the university!

Brilliant! Extremely helpful... Thank you!

The explanations are so easy to understand.

I really love this video very explicit and well thought !!❣❣

Best video I have find on youtube. Have you created playlists related to the AI, ML, NN 🤔

🐍 great video, simple and direct approach, simple models, simple problem. It'd be nice to have more of those simple and direct videos with simple classification and clustering problems, just to get a grasp on them :D

this is literally the first machine learning that i have built looking to take it further Thanks for the tutorial 🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍🐍

Amazing tutorial! Thank you very much!!!

Great video! I enjoyed a lot and learned a lot. Many thanks for that.

🐍wow!!thanks...sticking around you definitely will get me throught my internship with flying colours!

before this video, Machine learning was sorcery and scary. Not anymore. Thank you for your neat and casual explanations that make me love the science and the math behind those functions. I am really looking forward to giving it a shot and learning more. thanks a lot. like and sub-earned

This is super. Thank you so much. Keep it up the good work...

Thank you for the video. I learned alot. Recently, i am working on Speech recognition. Hopefully, I am gonna start building a model for it too.

🐍Amazing intro to ml. It really felt easy to build my first ml model

🐍Thanks for this video. It will help me move forward in my final project in the university. Keep up the Good work. God bless you🙏.

it was great learning from you thank you

🐍 thank you for the great tutorial of the building machine learning model.

🐍thank you! Really enjoyed the detailed steps and explanations.

🐍THanks for such an excellent introduction to running ML models in python - so clearly explained and super helpful tips on working within notebooks!

🐍 Love the video. Thank you so much! Wondering what should be my next step after this video?

Python really is a whole different beast.. Just to accomplish the task we used numpy, pandas, sklearn.model_selection, sklearn.linear_model, sklearn.metrics, sklearn.ensemble, and matplotlib. That's 7 libraries.. So overwhelming... Thanks for the guide. I did it locally within VSCode, so had to keep tweaking and installing libraries along the way, but it worked out well. The ML journey begins. Thanks for the training wheels!

I have been spending the past 4 months trying to actually do something tangible but haven't found anything practical like this one Just took an hour and half I belive and I am done with my first mach ine learning modle This has enhanced my confience and now I am going to build more Thankf Professor

Great video ! After making the model can we deploy this to AWS? or do we need to reprogram in AWS again?

🐍 Great content, please continue with your amazing effort. I have learnt so much from this video! Can you make more machine learning projects, I found this video super helpful!

Very well explained. Now i know where to start and what to do....

This is very informative, thanks!

Thank you Prof. Very helpful

Thanks. Super helpful!

Very helpful, thank you!!!

🐍 Great video Data Professor! I really liked this tutorial because you didn't miss explaining terms you used without making any assumptions

Excellent video to help understand the basic behind training new models. Just one suggestion, I missed a little more explanation on how the different parameters of the training algorithm would affect the end result.

WOW....very interesting and very helpful....Thank you

Really thanks a lot. Its very much useful

🐍 Great way of explaining machine learning! Made it really easy to understand!

amazing u have really helped me out

Great Tutoring from the Data Professor.

Is there a way to export the data into a new csv with the model results included

Thank you so much. this is really helpful

Amazing explanation ,Thank you:)

Hi ! Can you please mention in one line what exactly we found at the end I see it shows linear regression but what exactly does it depicts?

🐍 I'm doing a classification model for my thesis and your video has been really useful, thank you so much for the content. Could you make a video explaining the feature extraction? it'd be really helpful. Thank you again I love your videos.

Simply awesome.

good work; the presentation indeed helped

🐍thank you for posting this tutorial. It is helpful and easy to follow. 👍

The part I did not unterstand was the evaluation. For example. In the end we got a Training MSE fo Liniar regression of 1.007. Is the good or bad? Is the 1.028 form Random forest a better or worst result.

🐍 Finally a simple video, although I had a book for all this I think the pacing was too slow to start me off. Now I am more interested in know why and how some of this code work and for what reason. This is my process on being able to really understand and process information for coding lol Thanks for the video!

🐍appreciate the video, very clear and informative

you are amazing! thank you so much!

This was really nice content i have been struggling from where to start for my project and this video has just given me the way thanks @Data professor😇😇😇

Thanks for this video. I want to develop and train a Machine learning model that will be used to develop a robotic system in maize cultivation.

Thank you, very helpful

Great video , I thought the comparison at the end , would show the actual values from the testset vs the predicted values for the Y , could you explain the difference?

Amazing tutorial!!!!

Great video man!

Great job Thanks 🙏

Great start👌

🐍Wow. This is my first time witnessing how models can be created. Great video as well.

Great ❤video for me really helpful ❤

already so much better than datascience 365 tutorials!

So, if we have experimental data already trained and tested, does this mean we can predict the solubility of the new molecule yet to be synthesized? Thanks for simplifying it.

thank you so much really!

Awesome 👏 thank you

🐍Very helpful video as I am starting my journey into training AI. Your instructions were very clear and easy to follow. Thanks for sharing!

Thanks for this awesome tutorial, in the video you gave the link to your article on building machine learning model. I tried to read but it is only available to medium members, please is there a way i can access it? thank you

Great tutorial

Great tutorial :D

How would you go about determining how accurate your model is against the test dataset?

🐍 I loved this. thanks so much.

🐍 Thank you for your guided tutorial

Good job. Thank you for putting this online 🐍

🐍 This is great video & so helpful to understanding machine learning. I Can follow your tutorial well. Thank you

thank you very much

🎯 Key Takeaways for quick navigation: 00:00 📋 *Introduction to Building Your First Machine Learning Model in Python* - Overview of building a machine learning model in Python using scikit-learn and Google Colab. - Naming conventions and organizing Jupyter Notebooks. 01:12 📊 *Loading and Exploring the Dataset* - Introduction to the delani dataset, which contains information about molecule solubility. - Explanation of comma-separated values (CSV) format and dataset columns. 05:11 📦 *Data Preparation: Splitting Data into Features (X) and Target (Y)* - How to separate the dataset into features (X) and the target variable (Y). - Explanation of the training set and testing set split. 09:22 ⚙️ *Building a Linear Regression Model* - Importing the Linear Regression model from scikit-learn. - Training the model on the training dataset. 13:18 🧪 *Model Evaluation: Mean Squared Error and R-squared* - Calculating Mean Squared Error (MSE) and R-squared for both training and testing sets. - Organizing and presenting the results in a Pandas DataFrame. 22:13 🌲 *Building a Random Forest Regressor Model* - Introduction to using the Random Forest Regressor model. - Organizing notebook sections and headings for clarity in the notebook structure. 23:47 📊 *Building Regression Models* - Explains the distinction between regression and classification models based on the nature of the target variable. - Demonstrates how to create a random forest regressor with specified parameters. - Covers the training process of the regression model. 25:01 📈 *Model Performance Evaluation* - Discusses using mean squared error and R2 score for evaluating model performance. - Shows how to apply these metrics to the random forest regression model. - Emphasizes being cautious about typos in code. 26:08 🧷 *Combining Model Results* - Explains the process of combining the results of linear regression and random forest regression models. - Demonstrates how to concatenate the results into a single DataFrame. - Provides tips on reindexing and organizing the combined data. 28:03 📊 *Data Visualization of Prediction Results* - Introduces data visualization using matplotlib for comparing predicted and actual values. - Guides in creating a scatter plot with labeled axes. - Adds a trendline to the plot using numpy's polyfit function. 29:56 🎉 *Conclusion and Further Exploration* - Summarizes the process of building a machine learning model in Python using scikit-learn. - Encourages viewers to explore different models, tweak learning parameters, and refer to scikit-learn's documentation. - Requests viewers to share their model-building experiences in the comments. Made with HARPA AI