DeepSeek very slow. Always saying server is busy

Pease reply!! do we have a SQL and BI or Tableau project for a CAR REPAIR & SERVICE Dataset, which can tell which specific cars suffer what damage on what parts, therefore telling the ruggedness of that car, how much it might cost you on its maintenance HENCE telling service providers what parts they should readily have in their inventory and also telling the customers which car is worth buying and which is not. or any similar DA project🙏

Is it gives u unexpected keyword argument for base_estimator, remove it so it'd be bagging = BaggingClassifier(DecisionTreeClassifier(), ...

Sir please tell me mind reading machine available in india

koi import export datasets pe project hai kya?

Is this field still relevant in 2025? I am a chartered accountant and would like to learn data science. Do you know if I can do it or is it only for the engineers?

If you teaching these things to indian, i hope it would be hindi to understand more because of our native language.

With all respect and love Thank you so much sir.. you cleared my doubts....thank youuuuu so much....

very funny how you took African as an example of poverty and financial struggle, you are an Indian you people are every where on this plat looking for just what to eat

Sir I am currently studying to become a data analyst your point of what level of python I should be known I feel little tough so what can you say about as a fresher fresher as myself can able to do and can you able to share your some of your works like data cleaning data manipulating building dashboards gathering data refreshing them etc so that I can able to prepare myself please sir

Wonderful video .. thanks Bhavesh !

Sir, can you please let me know is experience important in such domain, What if a person with year gap want to enter in such domain because I have networking experience but not analyst experience.

So what is the correction? Is that supposed to be like this..? - Parameters -> model.coef_, model.intercept_ - Hyber-parameters -> SVC(C=1, gamma=10, kernel='liner')

DEEPSEEK IS JUST A COPYCAT OF EXISTING AI PLATFORMS. NOTHING TO RAVE ABOUT.

Great explanation, thanks!!

Fir bhi bhai salary 7 lakh he... Is liye chill

Sam Altman laughed at Indians when asked if Indian can do it on limited budget. That aged well :D

Struggling with your accent

If I had teacher like you in my high school I would have never got afraid of Maths

hi i have a question ....if i want to go towards product manager side what is the course i can learn ....Can u please guide me?

I am very confused about the term "Open Source". When I ask Deepseek it always denies the fact. Would anyone be able to explain it?

Till now the best explanation. Amazing work.

How come OpenAI's models didn't come up with this method used by DeepSeek? Isn't this what AI is supposed to accomplish?

It gives full code even with dummy data. Just splendid

It's imagination is insane. It kind of assesses your own boundaries, scope regarding the subject matter and then dishes out the response.

Very good explanation and like how short it is.

Great analogy, thank you!❤

# importing the required libraries import numpy as np import pandas as pd from matplotlib import pyplot as plt from sklearn import linear_model import warnings # class to load the data set class DataSetLoading: # class attribute to store the dataset here dataFrame = pd.DataFrame() x_data_points = None # to store the x axis data points y_data_points = None # to store the y axis data points # init constructor to get the dataset as argument and the independent and dependent variable def __init__(self, dataset, independent_var, dependent_var): # converting the dataset into data frame using pandas dataframe class self.dataFrame = pd.read_csv(dataset) # loading the x and y data points self.x_data_points = pd.DataFrame(self.dataFrame[independent_var]) # x data points self.y_data_points = pd.DataFrame(self.dataFrame[dependent_var]) # y data points # showing this statement for confirmation print(" Dataset loaded Succesfully....") # class to train the machine learning model class ModelTrainer(DataSetLoading): # crating an object of LinearRegression() class from linear_model sub module simple_linear_regression = linear_model.LinearRegression() # method to train the model def modelTrain(self): # fitting the model by passing the x and y data points as data frame self.simple_linear_regression.fit(self.x_data_points, self.y_data_points) # showing this statement for confirmation print(" Model trained Succesfully.... ") # method to predict the values using model def modelPrediction(self, prediction_input): # for ignoring warnings warnings.simplefilter(action='ignore', category=UserWarning) # sending the prediction inputs as 2-D array form using numpy array predicted_output = self.simple_linear_regression.predict(np.array([[prediction_input]])) # returning the predicted output to the user return predicted_output # class to plot the data points into graph class DataPointPlot(ModelTrainer): # method to plotting the data points into graph def plottingDataPoints(self): # graph style selection plt.style.use("seaborn-v0_8-whitegrid") # for plotting the original points as scatter plot graph plt.scatter(self.x_data_points, self.y_data_points, label = "Original Value", color = "Blue") # for plotting the predicted points as line chart for best fit line plt.plot(self.x_data_points, self.simple_linear_regression.predict(np.array(self.x_data_points)), label = "Best Fit Line", color = "Red", linewidth = 5 ) plt.legend() # for showing the legend in the graph plt.title("Simple Linear Regression", fontsize = 26) # title plt.xlabel("year", fontsize = 20) # x axis plt.ylabel("per capita income (US$)", fontsize = 20) # y axis plt.show() # for showing the graph # checking if file is main file or not if __name__ == "__main__": # list of prediction inputs prediction_input_list = [2020, 2021, 2022, 2023, 2024, 2025, 2026] # creating an object and passing the arguments obj = DataPointPlot(dataset= "canada_per_capita_income.csv", independent_var= "year", dependent_var= "per capita income (US$)" ) # calling modelTrain method to train the model first obj.modelTrain() # using for loop we will predict the values for prediction_input in prediction_input_list: # passing the prediction value into model print(f"Prediction for year {prediction_input}: {obj.modelPrediction(prediction_input= prediction_input)[0, 0]} $") # plotting the graph for simple linear regression by calling the method obj.plottingDataPoints()

Marketing bhi to ho gayi achhi khasi 😂😂

trump must thank DeepSeek, the US can save $500 Billions !

goated Inder

Improvements are always cheap

Thanks for the video sir. Your video helped me land an internship in Machine Learning😊😊. I was asked to implement neural network from scratch.Thanks a lot sir your videos are very helpful.

Ai generated video kzbin.info/www/bejne/kJ-alKGmj8SmZqssi=nyfnldmgeYgNYNBG

But deepseek does not provide images. it was trained to provide text

Can you include, how to code AI agents using Deep Seek in this course?

Just made fair and clear statements about Deepseek. Cool

The best video i have watched w.r.t transformers! Cleared a lot of my doubts, thanks a lot sir

Hey Dhaval Sir.. please make some videos or lecture on time series forcasting projects.

Thank you Dhaval and Codebasics for amazing video. Really helpful !!!!

nice vid

Indian is falling behind in the race of technology

Does the deep learning course covers both Tensorflow and Pytorch ?

Where is india heading.. I think we are good only in servicing and copy paste.No innovative thoughts and ideas. Really appreciate China

DeepSeek R1is said to be “Open Source” but it isn’t - since they haven’t provided details on the training data etc it doesn’t meet the definitions which means people can get into copyright and IP trouble if they build apps on top of it - this is why Huggingface is now trying to reverse engineer it. Furthermore when you push R1it says it’s ChatGPT!!!