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
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    Muhammad_Faizan 2 weeks ago
    --> Clustering Algorithms: 1. K-means: An unsupervised learning method in which we set the value of K, which is the number of Categories/groups. * it decreases the intra-cluster variance * it increases the inter-cluster variance 2. Hierarchical Clustering: An unsupervised learning method that checks the similarities concerning major, then minor, and then minor. (the concept of multicollinearity) 3. DBSCAN: An unsupervised learning method that divides data points into clusters based on their density and noise. 4. Gaussian Mixture Models: (GMM) a probabilistic model used in machine learning and statistics to represent the presence of multiple subpopulations within an overall population, even when we don't know which subpopulation an individual data point belongs to. GMM assumes that the data points are generated from a mixture of several Gaussian distributions with unknown parameters. --> Dimensionality Reduction Algorithms: technique used to find the most useful features from a dataset. 1. Principle Component Analysis (PCA): Tells us about multicolinearity among features. 2. t-SNE (t- distributed Stochastic Neighbour Embedding) it gives us visualizations to identify most useful features based on variances. * the term t-distribution means the distribution when there are less number of samples) 3. Auto-Encoders: works on neural Networks) 4. SVD (Singular Value Decomposition): converts matrix into single vector. --> Anomally Dectection Algorithms: (Outlier Detection) 1. Isolation Forest 2. Local Outlier Factor (LOF) 3. One Classs SVM (Support Cector Machines) 4. Associatin Rule Learning 5. Topic Modeling 6. Neural Network Based Model
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    Najeeb Ullah 4 weeks ago
    unsupervised machine learning introduction done
    Reply
    Rana Anjum Sharif 3 months ago
    Done
    Reply
    Shahid Umar 7 months ago
    Unsupervised learning starts from this lecture. This lecture contains detail of three types of unsupervised learning algorithms (1) Clustering Algorithms (2) Dimensionality Reduction Algorithms (3) Anomaly Detection Algorithms
    Reply
    Javed Ali 8 months ago
    AOA, I did this lecture today. ALLAH PAK aap ko sahat o aafiyat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey aur aap ke walid-e-mohtram ko karwat karwat jannat ata farmaye,Ameen.
    Reply
    FEROZ SHAH 8 months ago
    done
    Reply
    Mehak Iftikhar 8 months ago
    Done
    Reply
    Danish Ammar 8 months ago
    Done
    Reply
    Najeeb Ullah 8 months ago
    done
    Reply
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