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Introduction on machine learning to begin machine learning with python tutorial series. This video describes what is machine learning, deep learning, machine learning application in real life. In next tutorial we will start writing python code to solve a simple problem using machine learning
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#MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource
Topics that are covered in this Machine Learning Video:
1:06 What is machine learning?
3:55 what is Deep Learning?
5:09 Machine Learning implementation in real life
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Machine Learning Tutorial Python – 2: Linear Regression Single Variable:
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Kaw and kar
You've simplified the subject to a great extent where others are complicating it. Found minor mistakes in the flow but that don't hamper much. Thanks for your efforts and contribution.
bring in hindi lang
I like it! Super! Keep Going!
Hello sir π
(I'm doing multi label text classification btw)
I asked before that if it is better to clean the training data or the dataset before training a machine learning algorithm. So I did it and cleaned it however I'm getting an accuracy of 70% to 79% only and I'm not really satisfied with that soooo
I'm just wondering how can I boost the accuracy? I think yeah , I need to add more data, I only have 2600 samples of my dataset which is obviously a small amount. Also is removing stopwords, punctuation marks or even characters with less than three of length already enough for cleaning? So that my model will have a good performance. Or should I include removing numbers? or any other things?
Thank you so much for your answer π You really help a lot of people , I've been reading other people's comments π salute to you sir π
I like the way you simplify the topic hence requesting you -can you teach about backward elimination in multilinear regression
use of OLS.summary()
I am confused on this topic
sir make a video on CNN,DNN,RNN,LSTM,deep-RNN etc
5:27
MARIJUANA?
Deep learning with marijuana is Fun ππ
You are doing a great job in explaining , please keep up the good work.
Machine learning tutorials with exercises:
https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw
Step by step guide on how to learn data science for free: https://www.youtube.com/watch?v=Vn_mmOuQkSA
Learn data analyst skills: https://www.youtube.com/watch?v=x6tnVOn4st4
Pandas tutorials: https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy
Switch career to data science from non-technical background: https://www.youtube.com/watch?v=P8BuKf9crA8
Complete python tutorials list: https://www.youtube.com/playlist?list=PLeo1K3hjS3usILfyvQlvUBokXkHPSve6S
Thank you very much for this video. I am non Computer science student.
Recently i worked as a Internet of things technology for start up (Bangalore). I was working as Firmware engineer.
There i got an opportunity to explore Python language for sending data from sensor to cloud using API's .Also got to know about Cloud technology and other computer science terms like client ,server,github . I started learning Python myself from internet resource(free courses).
I am beginner to machine learning.
Now i got a requirement on machine learning. Blood deferral data set means when the blood bank go to the blood bank, there will be various reasons they get rejected.
You have to find out which is the maximum condition that most of the donors are rejected.
There is 1 and 0 in that. There is a column various reasons column and whenever there is 1 means that donor is deferred of that reason. So I have to find out for which reason most of the donors are deferred.
How can we achieve this?
Sir you are awesome
I can not see the link for the exercise
Great videos. I am a java developer
What's the prerequisite knowledge needed to study machine learning??
i have a one big challenging doubt in my mind Really please help me
why do we find best accuracy model , what is the use with that ?
after finidng the best model what is the next process in organization
ex: Walmart wants to open new stores in NewYork, walmart has online customers shipping raw data (like customer location , name, phone number, sales ), so how can they find best locations in NewYork to open stores based on raw-data. what will be the Output here ?
you can please mail me with your explanation : dkishore090@gmail.com
5:20 Nice
Linear Regression (dummy variables): I appreciate your tutorial, I'm a beginner for Python ML. str conversion is one of the issues I'm facing. I watched your Linear Regression tutorial and I have converted one of my columns by using your dummies conversion method. The converted dummies are 10 and one minus 9. The problem is it couldn't predict for me. It produced the following error message: ValueError: shapes (1,3) and (11,) not aligned: 3 (dim 1) != 11 (dim 0). For details, look at the following converted data and error messages. You can contact me on email associated with this comment. I look forward to hearing from you.
test_score oral_score salary eleven five fourteen seven six ten
0 8 9 45000 0 1 0 0 0 0
1 8 6 45000 0 0 0 0 1 0
2 6 7 65000 0 1 0 0 0 0
3 10 10 65000 0 0 0 0 0 0
4 9 6 70000 0 0 0 1 0 0
5 8 10 62000 0 0 0 0 0 0
6 6 7 72000 0 0 0 0 0 1
7 7 8 82000 1 0 0 0 0 0
8 9 7 82000 0 0 0 0 0 0
9 8 8 83000 0 0 0 0 0 0
10 9 9 85000 0 0 1 0 0 0
11 8 6 82000 0 0 0 0 0 1
12 9 5 82000 0 0 0 0 0 1
three twelve two
0 0 0 0
1 0 0 0
2 0 0 0
3 0 0 1
4 0 0 0
5 1 0 0
6 0 0 0
7 0 0 0
8 0 1 0
9 0 0 0
10 0 0 0
11 0 0 0
12 0 0 0
X = final.drop('salary', axis = 'columns')
y = final.salary
#because of small data, no need to use train_test_split, but just train on the whole data.
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
model.predict([[8,9,1]])
—————————————————————————
ValueError Traceback (most recent call last)
<ipython-input-15-196b62f15502> in <module>
—-> 1 model.predict([[8,9,1]])
C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py in predict(self, X)
219 Returns predicted values.
220 """
–> 221 return self._decision_function(X)
222
223 _preprocess_data = staticmethod(_preprocess_data)
C:ProgramDataAnaconda3libsite-packagessklearnlinear_modelbase.py in _decision_function(self, X)
204 X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
205 return safe_sparse_dot(X, self.coef_.T,
–> 206 dense_output=True) + self.intercept_
207
208 def predict(self, X):
C:ProgramDataAnaconda3libsite-packagessklearnutilsextmath.py in safe_sparse_dot(a, b, dense_output)
140 return ret
141 else:
–> 142 return np.dot(a, b)
143
144
ValueError: shapes (1,3) and (11,) not aligned: 3 (dim 1) != 11 (dim 0)
β
nice information and very easy explained each and every concept sir thanks a lot…. !!!
sir, any chances we are getting a playlist on statistics required for data science?
Hey you are doing very good job. God bless you buddy.
sir, I need your help. kindly make video "How do I set up Jupyter/IPython Notebook for Django?"
Sir, the videos you have made are really beneficial. I requested you to make more videos on data science like NLP, Deep Learning. It could be really helpful to us. Thank you π
So eagerly awaited course,Thank you for making it possible.
Hello, do you have a course video on deep learning keras ?
Python Full Course for Beginners 2019
https://youtu.be/7Wo5ClXwGXE?list=PLxkgsT9p-ay5GGo9QgN-X9gW9-ZHn-Vsi&t=51
Very Nice tutorial
This is Epic!. i;'ve watched almost all video on this channel and this channel Is Pure gold.
Low subcribe?. may be cause there's not many programmer in this world hehee IMAO
What is the best book for ML?
Please make a series on Scipy, Scikit-learn and pyspark
Awesome, Good Show!!!
please make videos on support vector machines and when do we use it. similarly when do we SVM, PCA and Decision Tree & Random Forest?
Love this series, very helpful. You should consider making this into a course you can sell on Udemy. I'd definitely buy it.
Got clarity on "what is machine learning" first, with clear explanations thank you
Wow I just discovered your videos and I am so excited to dive into Python for Data Science. Been working with SAS but need to learn Open Source. Thank you for all you do.
hi . Can you please share some videos on Exploratory Data analysis (using python). It'll be very helpful. Thanks in advance.
Please make a series on Beautiful Soup and Web crawling.And btw you have got an exceptionally great way of teaching.
please make a series on data wrangling
So YouTube got I am learning machine learning with machines learning
reat video and teaching method. You have an art of keeping things simple but still teach advanced concepts. I get a very good and quick overview and understanding from your videos. Thanks a lot.
can u plz give diffrence between machine data and human genarated data
nice
Aaaannnddd Subscribed !
Very good video, I have been looking to learn machine learning for a while now but I couldn't find good resources until now! Thanks, keep it up.
welcome
Great timing!
Just completed Python intermediate and advance, and here is my favorite Python teacher with a new series of Machine learning.
So much excited for this series.
Happy Pythoning:)
Oh what a lovely series!
Thank you so much π
How regularly will you upload videos for this series?