Course Content
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Day-17: Complete EDA on Google PlayStore Apps
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Day-25: Quiz Time, Data Visualization-4
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Day-27: Data Scaling/Normalization/standardization and Encoding
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Day-30: NumPy (Part-3)
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Day-31: NumPy (Part-4)
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Day-32a: NumPy (Part-5)
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Day-32b: Data Preprocessing / Data Wrangling
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Day-37: Algebra in Data Science
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Day-56: Statistics for Data Science (Part-5)
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Day-69: Machine Learning (Part-3)
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Day-75: Machine Learning (Part-9)
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Day-81: Machine Learning (Part-15)-Evaluation Metrics
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Day-82: Machine Learning (Part-16)-Metrics for Classification
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Day-85: Machine Learning (Part-19)
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Day-89: Machine Learning (Part-23)
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Day-91: Machine Learning (Part-25)
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Day-93: Machine Learning (Part-27)
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Day-117: Deep Learning (Part-14)-Complete CNN Project
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Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
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Day-121: Time Series Analysis (Part-1)
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Day-123: Time Series Analysis (Part-3)
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Day-128: Time Series Analysis (Part-8): Complete Project
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Day-129: git & GitHub Crash Course
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Day-131: Improving Machine/Deep Learning Model’s Performance
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Day-133: Transfer Learning and Pre-trained Models (Part-2)
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Day-134 Transfer Learning and Pre-trained Models (Part-3)
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Day-137: Generative AI (Part-3)
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Day-139: Generative AI (Part-5)-Tensorboard
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Day-145: Streamlit for webapp development and deployment (Part-1)
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Day-146: Streamlit for webapp development and deployment (Part-2)
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Day-147: Streamlit for webapp development and deployment (Part-3)
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Day-148: Streamlit for webapp development and deployment (Part-4)
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Day-149: Streamlit for webapp development and deployment (Part-5)
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Day-150: Streamlit for webapp development and deployment (Part-6)
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Day-151: Streamlit for webapp development and deployment (Part-7)
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Day-152: Streamlit for webapp development and deployment (Part-8)
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Day-153: Streamlit for webapp development and deployment (Part-9)
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Day-154: Streamlit for webapp development and deployment (Part-10)
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Day-155: Streamlit for webapp development and deployment (Part-11)
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Day-156: Streamlit for webapp development and deployment (Part-12)
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Day-157: Streamlit for webapp development and deployment (Part-13)
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How to Earn using Data Science and AI skills
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Day-160: Flask for web app development (Part-3)
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Day-161: Flask for web app development (Part-4)
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Day-162: Flask for web app development (Part-5)
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Day-163: Flask for web app development (Part-6)
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Day-164: Flask for web app development (Part-7)
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Day-165: Flask for web app deployment (Part-8)
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Day-167: FastAPI (Part-2)
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Day-168: FastAPI (Part-3)
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Day-169: FastAPI (Part-4)
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Day-170: FastAPI (Part-5)
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Day-171: FastAPI (Part-6)
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Day-174: FastAPI (Part-9)
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Six months of AI and Data Science Mentorship Program
    Join the conversation
    Muhammad Rameez 3 weeks ago
    https://www.kaggle.com/muhammadrameez242 , ye meri kaggle ki I'd hai yha par Ap mukamal assignments simple roman urdu mein mill jai gi codes mein
    Reply
    Rana Anjum Sharif 4 weeks ago
    The number 42 has gained prominence as a default or commonly used value for random_state due to its association with the novel "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, where it is humorously depicted as the answer to the ultimate question of Life, The Universe, and Everything.
    Reply
    Anila Gulzar Toor 6 months ago
    Using 42 has become a bit of a tradition in programming (inspired by "The Hitchhiker's Guide to the Galaxy" by Douglas Adams) and it's often used as a placeholder value when a specific number isn't critical to the application. By setting random_state to a fixed value we ensure that every time the code run will split the data in the same way to achieve reproducibility in your results. If we don't set a random seed (random_state) different runs of the code result in different training and testing sets which can make it challenging to compare and reproduce results.
    Reply
    Sibtain Ali 6 months ago
    I have done this video with 100% practice.
    Reply
    tayyab Ali 6 months ago
    I have done this lecture with 100% practice.
    Reply
    Shahid Umar 6 months ago
    In this lecture, discussion about coding for Support Vector Machine (SVM) in Python.
    Reply
    Javed Ali 6 months ago
    AOA, I learned in this lecture about the ML algorithm of SUPPORT VECTOR MACHINE ( S.V.M ) in PythonSTEPS OF SUPPORT VECTOR MACHINE ( S.V.M )1- Import Libraries 2- Import ML libraries 3- Load the data set of iris 4- Split the data in X and y 5- Split the data in train and test ( set the random_state=42 ) 6- Call the model of SVC 7- Train the model 8- Predict 9- Evaluate the model 10- Plot the model and data in confusion matrix 11- Save the model 12- Load the modelALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey Ameen.
    Reply
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