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
About Lesson

 

Outliers: Data Science Mein Ghair Mutawaqa Mehman

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Muhammad Uzair Madni 4 months ago
Outliers are the anomalies in the dataset that disturb the machine learning and give biased results.
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Muhammad Uzair Madni 4 months ago
Phir Program to var giya
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Abdullah Shahid 4 months ago
- Outliers are unexpected or random values - Outliers leads to causes wrong prediction like missing values - Outliers are anomalies in data - Outliers are mistakes during data collection - Outliers are will cause to wrong insights from data
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Hafiz Muhammad Gulzar Alam 4 months ago
Outliers are the anomalies of the data that will disturb the whole analysis of the data. 2. So we have to find them and reduce or impute them using different methods. 3. There are also different types of outliers so we have to consider them while detecting them. 4. We can use statistical methods like Inter Quartile Range or Visualization methods to detect outliers and to deal with them we can remove them, transform them, or impute them. 5. So we must deal with these because if not then it will create issues in the future.
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Ihtasham Aslam 4 months ago
Program to war gya
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Shadat Ali 4 months ago
hello bro
Yahya khan 5 months ago
waar jata h...................
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Hamza Zubair 7 months ago
program to warrr gyaaa
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Talha Afzal 7 months ago
1. Outliers are anomiles of data that disturb our insight about data set. 2. to get accurate insights from data we must need to remove outliers. 3. there are differnet statistical or visualization ways to remove outhliers like histogram, box plot that is also called Inter Quartile Range and Z-score method. 4. We can remove outliers, transform them and in many cases we can impute them by their mean, median and mode. 5. Inshort, if we don't detect and remove outliers, it will squid over dataset and we don't get our desierd results from data.
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Talha Afzal 7 months ago
program to war gya
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Taimoor Ali 7 months ago
1. Outliers are the anomalies of the data that will disturb the whole analysis of the data. 2. So we have to find them and reduce or impute them using different methods. 3. There are also different types of outliers so we have to consider them while detecting them. 4. We can use statistical methods like Inter Quartile Range or Visualization methods to detect outliers and to deal with them we can remove them, transform them, or impute them. 5. So we must deal with these because if not then it will create issues in the future.
Reply
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