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Stock Prediction using Machine Learning and Python | Machine Learning Training | Edureka



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This Edureka “Stock Prediction using Machine Learning” takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc. Topics covered in the tutorial are as follow:
01:30 Introduction to Stock Prediction
03:00 LSTM Architecture
05:35 Stock Prediction Model
26:35 Conclusion

🔸Dataset & code: https://bit.ly/2x6BPe0

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Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.
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PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance
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Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking
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Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website – Sales Prediction
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Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada
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Is this program for me?

If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.
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42 thoughts on “Stock Prediction using Machine Learning and Python | Machine Learning Training | Edureka
  1. Hello mam! Thank you for uploading the video and teaching us the partten, However, I am getting the following 2 errors. Kindly help in rectifying the error.

    1. #During reshaping: (Video 16:10) I am getting IndexError: tuple index out of range ( when x_train.shape[1], 1)). When i am changing it to x_train.shape[0], 1)), this errors disappers from this cell.

    2. Just after initialising the RNN: (In cell 74), NameError: name 'X_train' is not defined.

    Please help.

  2. how we can increase the accuracy of stock price prediction using RNN please, still I'm getting low accuracy using your video above.

  3. Could you please advise why do you have 1198/1198 under each Epoch while you had your batach_size set up to 32? In order to recreate it I had to set up the batch size to 1 and it takes 1 min to go through each epoch. Also would you know why the first 4-5 values predicted in my model are way higher than the real stock price value but after that the model is more or less aline with the real value for the rest of the test period (I've checked the code many times and it is exactly the same). Thanks.

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