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_Faizan 4 weeks ago
    1: Graphical Method, 2: Substitution Method, 3: Cramer's Rule, 4: Elimination (Addition/Subtraction) Method, 5: Matrix Method (Gaussian Elimination)
    Reply
    Rana Anjum Sharif 2 months ago
    Done
    Reply
    Rana Anjum Sharif 2 months ago
    Done
    Reply
    Muhammad Rameez 2 months ago
    Done
    Reply
    junaid amin 6 months ago
    Assignment done
    Reply
    kashan malik 6 months ago
    Assignments DONE
    Reply
    Shahid Umar 7 months ago
    Assignment done
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    saima saeed 7 months ago
    done
    Reply
    Javed Ali 7 months ago
    AOA, There are different methods to solving system of equation in linear algebra like graphing methods, substitution, elimination, and matrix methods
    Reply
    Javed Ali 7 months ago
    Matrix methods: These methods are useful when you want to solve a system efficiently and accurately using mathematical tools. Matrix methods involve using matrices to represent and manipulate systems of linear equations. A matrix is a rectangular array of numbers arranged in rows and columns. A system of linear equations can be written as an augmented matrix, which consists of a matrix with coefficients for each variable and an identity matrix with ones on its main diagonal. Matrix methods can perform operations such as row reduction, Gaussian elimination, Cramer’s rule, or inverse matrices to find the solution. However, matrix methods require some knowledge of algebra and calculus, as well as familiarity with matrices and their properties
    Javed Ali 7 months ago
    Elimination: This method is useful when you want to solve a system by reducing it to a single equation with two variables. Elimination works well when the constants of both equations are already isolated or when the coefficients of the Xs or Ys in both equations are the same. However, elimination can be difficult when one or both whole equations have to be multiplied by a number to make the variable cancel. Elimination also requires that both equations have X or Y isolated, which may not be possible in some case.
    Javed Ali 7 months ago
    Substitution: This method is useful when you want to solve a system by eliminating one variable at a time. However, substitution requires that at least one variable in one of the equations is already isolated, which may not be easy to find. Substitution also poses problems for those who tend to make arithmetic mistakes.
    Javed Ali 7 months ago
    Graphing: This method is useful when you want to visualize the relationship between the variables and check your answer visually. However, graphing can be tedious and time-consuming for large systems or complex equations. Graphing also requires that both equations have Y isolated, which may not be possible in some cases.
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