Course Content
How and Why to Register
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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 4 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
    Done
    Reply
    Shahid Umar 6 months ago
    In this lecture, I learned about the goal of boosting, how does boosting work?, the advantages of boosting, application of boosting, method or types of boosting.
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture the Boosting Ensemble method.
    Reply
    Javed Ali 6 months ago
    AOA, I learned in this lecture about boosting algorithms from Ensemble family and how they work.1-Initialize weights: At the start of the process, each training example is given an equal weight. 2-Train a weak learner: makes a weak learner strong 3-Error calculation: The error of the weak learner on the training data is computed. 4-Update weights: Weight are updated according to the mistakes 5-Repeat: steps 2-4 are repeated several times. 6-Combine weak learners: The final prediction is based on the weighted total of the weak learners. 7-Forcast:And also learned advantages of boosting, which are1-Improve Performance: (reduces bias and variance, results in more accurate and robust predictions) 2-Ability to Handle Complex Data:( (handling complicated data like non-linear correlation and interaction) 3-Robustness to Noise: (handling outliers effectively ) 4-Flexibitily: ( allowing for customization and adaptation to various problem domains) 5-Interpretablity:And I also learned about applications of Boosting algorithms which are1-Classification problems: (spam detection, fraud detection, and disease diagnosis) 2-Regression problems: (housing price prediction and stock market trends) 3-Natural language processing (NLP) task: (sentiment analysis and text classification) 4-Image and speech recognition: 5-Recommendation Systems: (product recommendations and movie recommendations) 6-Time series analysis: 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.
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