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 4 weeks ago
    Logistic regression is used for classification problems because it predicts the probability that a given input point belongs to a certain class. The core idea is to find a relationship between features and the probability of particular outcomes. Unlike linear regression which outputs a continuous number, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes.
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    Faheem Ullah 6 months ago
    Logistic Regression is often associated with classification problems because its primary purpose is to model the probability of a binary outcome. The logistic regression model predicts the probability that an observation belongs to a particular category (class 1) based on one or more independent variables. Despite having "regression" in its name, logistic regression is used for classification, not regression.
    Reply
    Sibtain Ali 6 months ago
    Assignment: Q1. Why is logistic regression a classification problem? Logistic regression is a statistical method used for binary classification problems, meaning problems where the outcome variable (or dependent variable) has two possible classes or categories. It's called "regression" because it involves the logistic function, but it's used for classification rather than regression tasks. Logistic Function (Sigmoid): Probability Interpretation: Decision Boundary: Maximum Likelihood Estimation: Linear Decision Boundary: and I have done this video with 100% practice.
    Reply
    tayyab Ali 6 months ago
    Assignment: Q1. Why is logistic regression a classification problem? Logistic regression is a statistical method used for binary classification problems, meaning problems where the outcome variable (or dependent variable) has two possible classes or categories. It's called "regression" because it involves the logistic function, but it's used for classification rather than regression tasks. Logistic Function (Sigmoid): Probability Interpretation: Decision Boundary: Maximum Likelihood Estimation: Linear Decision Boundary:
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture logistics regression with 100% practice.
    Shahid Umar 6 months ago
    Logistic Regression is the key to successfully classifying the feature values based on probability.
    Reply
    Javed Ali 6 months ago
    Assignment of Day-75, 15-dec-2023 ML (day-8)Why is logistic regression a classification problem?Logistic regression is a classification algorithm because it predicts the probability of an observation belonging to a specific class rather than predicting a continuous value as in regression problems. The logistic function and the thresholding process make it suitable for binary classification tasks, where the goal is to assign observations to one of two classes based on their feature values.
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    Javed Ali 6 months ago
    AOA, I learned in this lecture how the sigmoid function works in LOGISTIC REGRESSION in Python. STEPS OF A LOGISTIC REGRESSION: 1-Import Libraries 2-Load the data set [ df = sns.load_dataset('titanic' ) ] 3-Pre process the data 4-Remove the deck column 5-Impute missing values in age and fare 6-Impute missing values in embark and embarked town 7-Encode the categorical variables using for loop where object and categoy data types are given 8-Split the data in X and y column 9-Split the data in train and test 10-Call the model 11-Train the model 12-Predict 13-Evaluate the model 14-Plot the model of confusion matrix 15-Save the model 16-Load the modelALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey, Ameen.
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