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
    Rana Anjum Sharif 3 weeks ago
    Done
    Reply
    Muhammad Rameez 4 weeks ago
    Done
    Reply
    Naresh Kumar Guriro 5 months ago
    Great way of teaching sir thanks.
    Reply
    Faheem Ullah 6 months ago
    I think these models can perform well but this process is little bit slow as compared to neural networks
    Reply
    Shahid Umar 6 months ago
    In this lecture, the ensemble algorithm continued with boosting technique with its methods like Adaptive Boosting (AdaBoost), Gradient Boosting, Xtream Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Stochastic Gradient Boosting (SGB), Linear Programming Boost (LPBoost), and TotalBoost.
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture the types of Boosting algorithms AdaBoost (Adaptive Boosting), Gradient Boosting, CatBoost (Categorical Boosting), Stochastic Gradient Boosting, LPBoost (Linear Programming Boosting, TotalBoost (Total Boosting), and Light GBM 8-XGBoost (eXtreme Gradient Boosting).
    Reply
    Sibtain Ali 6 months ago
    I learned in this video the types of Boosting Algorithms ( 1-AdaBoost (Adaptive Boosting), 2-Gradient Boosting, 3-CatBoost (Categorical Boosting, 4-Stochastic Gradient Boosting, 5-LPBoost (Linear Programming Boosting), 6-TotalBoost (Total Boosting), 7-Light GBM, and 8-XGBoos).
    Reply
    Javed Ali 6 months ago
    AOA,I learned in this lecture about types of Boosting algorithms, which are1-AdaBoost (Adaptive Boosting) 2-Gradient Boosting 3-CatBoost (Categorical Boosting) 4-Stochastic Gradient Boosting 5-LPBoost (Linear Programming Boosting) 6-TotalBoost (Total Boosting) 7-Light GBM 8-XGBoost (eXtreme Gradiant Boosting) ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey or ap k walid-e-mohtram ko karwat karwat jannat ata farmay,Ameen.
    Reply
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