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Un grand merci (:

Your content is great. Thanks a lot!

Incredible video! Thank you very much!

God bless you Dr. Pedram and thank you for the knowledge as always.

Always the best ❤❤❤ from the best pls can you include the KAN models IN THE COURSE

Great Very crucial tutorial! Thank you .Doc I want ask question have you experience on context sensitive spelling error detection and correction using deep learning approach? If you have please share for me.

Very detailed everything is cleared

Excellent videos, thank you very much. As Cora Coralina said: "Happy is he who transfers what he knows and learns what he teaches."

This is extremely helpful! Many thanks!

Thank you for such an insightful and helpful video tutorial! Do you have any tips on how to set a custom probability threshold for XGBoost in PyCaret (i.e. metrics, plots, tuning, and predictions)? I have approximately 30% of my dataset in the positive class, so would expect my XGBoost to perform best with a threshold close to 0.3, not the default 0.5.

Great, congratulations 🙇‍♀ But I'm not surprised 🙂 You are a great teacher!!

Sir please upload next module in deep learning

congratulations! We are so proud of you!

Lopez Kevin Williams James Thomas Eric

must be one of the very few tutorials on yt where the tutors voice is actually pleasant to follow

It's been a while since I've searched for good ML training, and your content is great, especially your GitHub. Thank you, Pedram.

just THANK YOU -- awesome !!

I am surprised you don't so many likes for this course. Its very well explained. Searching for the best explanation on youtube and I stumbled upon it and I am going to stick with it

Does it mean OLS is more straightforward than gradient descent? At least I have not seen the necessity to use gradient descent to estimate linear regression

Thank you for the video. Besides the disadvantages mentioned in others' comments, the feature scaling may also affect the interpretation or interpretability, especially when it comes to very complicated ML model.

Thank you for your videos. And your channel is my favorite place to learn ML. I feel comfortable with the overall content of the lecture, but at the same time, there are some points that raise questions in my mind. It might be the general feeling when learning something new.

Good work Pedram! It is hard to find meaningful material about clustering methods. Thank you!👋

Machine Learning is different from general programming in this way: Machine Learning involves designing algorithms that can learn from data and improve over time, whereas general programming requires explicit instructions for every step. In Machine Learning, the focus is on enabling the system to recognize patterns and make predictions based on the data it processes. There are three main types of Machine Learning: 1. Supervised Learning: Both input and output are provided, meaning the labels are known. The algorithm learns to map inputs to outputs based on this labeled training data. 2. Unsupervised Learning: Only the input is given. The machine must detect meaningful patterns or structures within the data without any labeled outcomes to guide it. 3. Reinforcement Learning: Both input and output are provided but not at the same time. The system interacts with the environment, receiving feedback through trial and error, and learns to make decisions that maximize cumulative rewards. Classification of the following algorithms according to the types of Machine Learning: 1. Both input and output are given, meaning the labels are known. - Type: Supervised Learning 2. Only the input is given. The machine should detect meaningful patterns. - Type: Unsupervised Learning 3. Both input and output are given, but not at the same time. There might be a delay. The machine needs to explore the environment through trial and error until it discovers a meaningful pattern. - Type: Reinforcement Learning ai.walsoftcomputers.com/

In real-world problems where the emphasis is either on prediction or inference, the choice between Machine Learning (ML) and Statistical Learning (SL) can depend on the specific goals: Problem Emphasizing Prediction (Less Inference): - Approach: Machine Learning (ML) - Reason: ML is generally more suited for problems where the primary goal is to make accurate predictions or classifications based on large datasets, often without needing to understand the underlying relationships between variables in detail. ML models like neural networks, ensemble methods, and gradient boosting are designed to optimize predictive performance and can handle complex, high-dimensional data. Problem Emphasizing Inference (Less Prediction): - Approach: Statistical Learning (SL) - Reason: SL focuses on understanding and interpreting the relationships between variables, often through simpler, more interpretable models. Techniques like linear regression, logistic regression, and hypothesis testing are used to infer relationships and test hypotheses about data. These methods are designed to provide insights into the underlying processes and can be used to draw conclusions about causal relationships or other aspects of the data structure. In summary: - Use ML for problems where accurate prediction is crucial and interpretability is less of a concern. - Use SL for problems where understanding relationships and making inferences is more important than achieving the highest predictive accuracy. ai.walsoftcomputers.com/

Pls can i use the vprom autoencoder with mcmc model or hmc model to catch daily & weekly forcast im trying use combination of economic reports and daily financial data im self learner i think its kind of art mix or im wrong ?

Im waiting for more videos 😞 were are you

Question of the day: 1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement Learning (Just what I am guessing based on the options)

When using timeseries_dataset_from_array to create datasets, train_dataset, test_dataset and val_dataset had all uniform tensors except the last one which were partials, i.e. their samples and targets were as follows: for samples, targets in train_dataset: if samples.shape != (batch_size, sequence_length, 14): print(samples.shape) print(targets.shape) for samples, targets in test_dataset: if samples.shape != (batch_size, sequence_length, 14): print(samples.shape) print(targets.shape) for samples, targets in val_dataset: if samples.shape != (batch_size, sequence_length, 14): print(samples.shape) print(targets.shape) (103, 120, 14) (103,) (118, 120, 14) (118,) (206, 120, 14) (206,) This gave the error: Epoch 1/10 --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-146-e09ecdf9d4ec> in <cell line: 15>() 13 ] 14 model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"]) ---> 15 history = model.fit(train_dataset, 16 epochs=10, 17 validation_data=val_dataset, Only one input size may be -1, not both 0 and 1 [[{{node functional_9_1/flatten_10_1/Reshape}}]] [Op:__inference_one_step_on_iterator_41264] Although I remove the partial batches, I still get the error. I do not get the same error when fitting the dataset with the CNN.

Excellent explanations and with clarity. Waiting for all the modules!!

great explanation sir

I discovered your videos yesterday, and I have to admit that they are exceptionally good.I have long been wanting to advance my knowledge and skills within time series forecasting but haven't been able to find a course that seemed worth my time. However, I have been binging the Deep Forecasting course - it just perfectly matches my current background knowledge and skills within programming, it covers the methods and models that I am interested in mastering, and everything is explained really well. I cannot wait for the coming modules!

is SVM good for regression, i mean in general compared to KNN, Random Forest, XGBoost?

No GBDTs?

is there any chance to get the presentations? I cant find this materials in your github account.

oh my dear god, it is the greatest ML videos I have ever seen in my life, I cant understand the concepts or I get bored, but I can watch this videos everyday all day long, thanks dear Pedram <3

perfect videos so many people missing these valuable informations

golden explanation

The concepts are so well explained. I love the way each phase of the process is taught in a clear way. Im better able to grasp concepts of machine learning now with clarity. Thanks for you efforts!

Should i learn staitisitcial learning or machine learning first or it depends

Excellent videos. Highly recommended.

the end of the video 😂 the lectures are just awesome 👏

thanks Man for Your great explanation

perfect videos,hope it will find proper audience. maye eftekhari ham vatan.

Thanks for making this video. I enjoyed watching it. I'm looking forward to the video on time series forecasting.

This is my personal experience (not so good one) with the PyCaret library. *First Disappointment:* After installing it with `pip install pycaret` on my Linux machine, I started Python from the command line and ran `import pycaret`. This is the message I received: ``` RuntimeError: ('PyCaret only supports Python 3.9, 3.10, 3.11. Your actual Python version: ', sys.version_info(major=3, minor=12, micro=4, releaselevel='final', serial=0), 'Please DOWNGRADE your Python version.') ``` Alright, I can live with that. After creating a virtual environment with a downgraded Python version and playing with PyCaret for some time, I was quite impressed by the rich capabilities-until I tried to analyze the performance of a trained model. *Second Disappointment:* My script contains the following line: ```python experiment1.plot_model(tuned_gbr, plot='learning') # Learning Curve ``` This line does work and I get to see the plot, but when I close the plot by clicking on the "X" mark or pressing Ctrl+W, the script crashes with the following output: ``` File "/usr/lib64/python3.11/tkinter/__init__.py", line 1732, in __setitem__ self.configure({key: value}) File "/usr/lib64/python3.11/tkinter/__init__.py", line 1721, in configure return self._configure('configure', cnf, kw) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.11/tkinter/__init__.py", line 1711, in _configure self.tk.call(_flatten((self._w, cmd)) + self._options(cnf)) _tkinter.TclError: invalid command name ".!navigationtoolbar2tk.!button2" ``` Actually, any plot type will crash in the same way except for the 'pipeline' plot, which does continue executing the remainder of the script after closing. This is very unfortunate, as I was looking forward to the amazing PyCaret plotting and analysis capabilities, which are now completely useless as they crash the program. Maybe it all works in Jupyter Notebook and Google Colab, but I don't use those platforms. If you can't run a simple script, it ends there for me. *Conclusion:* PyCaret is an excellent resource for learning. I'll try to adopt its concepts, pipelines, and methodologies, but I'll have to write my code myself as this thing unfortunately doesn't work. I hope developers will continue improving it and maybe it will become better with time. For now, it's a learning material, which I'll definitely examine in more detail. Just by going through the PyCaret documentation, I am fascinated by how much there is to learn.

Exactly what I needed

7:03 topics that will be covered in the video

Hi many thanks for sharing great information in this area.

Hi Pedram, could we have access to the pdf of the sessions?

Hi Pedram, I really need your help. I'm working on a project that detects seasonality using pycaret setup and it's all working great except for that the setup function shows seasonality present even with the most randomest data eg:- just a data of random values ranging from 20-25. Is there a way to fix this?