Course Content
How and Why to Register
Dear, to register for the 6 months AI and Data Science Mentorship Program, click this link and fill the form give there: https://shorturl.at/fuMX6
0/2
Day-17: Complete EDA on Google PlayStore Apps
0/1
Day-25: Quiz Time, Data Visualization-4
0/1
Day-27: Data Scaling/Normalization/standardization and Encoding
0/2
Day-30: NumPy (Part-3)
0/1
Day-31: NumPy (Part-4)
0/1
Day-32a: NumPy (Part-5)
0/1
Day-32b: Data Preprocessing / Data Wrangling
0/1
Day-37: Algebra in Data Science
0/1
Day-56: Statistics for Data Science (Part-5)
0/1
Day-69: Machine Learning (Part-3)
0/1
Day-75: Machine Learning (Part-9)
0/1
Day-81: Machine Learning (Part-15)-Evaluation Metrics
0/2
Day-82: Machine Learning (Part-16)-Metrics for Classification
0/1
Day-85: Machine Learning (Part-19)
0/1
Day-89: Machine Learning (Part-23)
0/1
Day-91: Machine Learning (Part-25)
0/1
Day-93: Machine Learning (Part-27)
0/1
Day-117: Deep Learning (Part-14)-Complete CNN Project
0/1
Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
0/2
Day-121: Time Series Analysis (Part-1)
0/1
Day-123: Time Series Analysis (Part-3)
0/1
Day-128: Time Series Analysis (Part-8): Complete Project
0/1
Day-129: git & GitHub Crash Course
0/1
Day-131: Improving Machine/Deep Learning Model’s Performance
0/2
Day-133: Transfer Learning and Pre-trained Models (Part-2)
0/1
Day-134 Transfer Learning and Pre-trained Models (Part-3)
0/1
Day-137: Generative AI (Part-3)
0/1
Day-139: Generative AI (Part-5)-Tensorboard
0/1
Day-145: Streamlit for webapp development and deployment (Part-1)
0/3
Day-146: Streamlit for webapp development and deployment (Part-2)
0/1
Day-147: Streamlit for webapp development and deployment (Part-3)
0/1
Day-148: Streamlit for webapp development and deployment (Part-4)
0/2
Day-149: Streamlit for webapp development and deployment (Part-5)
0/1
Day-150: Streamlit for webapp development and deployment (Part-6)
0/1
Day-151: Streamlit for webapp development and deployment (Part-7)
0/1
Day-152: Streamlit for webapp development and deployment (Part-8)
0/1
Day-153: Streamlit for webapp development and deployment (Part-9)
0/1
Day-154: Streamlit for webapp development and deployment (Part-10)
0/1
Day-155: Streamlit for webapp development and deployment (Part-11)
0/1
Day-156: Streamlit for webapp development and deployment (Part-12)
0/1
Day-157: Streamlit for webapp development and deployment (Part-13)
0/1
How to Earn using Data Science and AI skills
0/1
Day-160: Flask for web app development (Part-3)
0/1
Day-161: Flask for web app development (Part-4)
0/1
Day-162: Flask for web app development (Part-5)
0/1
Day-163: Flask for web app development (Part-6)
0/1
Day-164: Flask for web app development (Part-7)
0/2
Day-165: Flask for web app deployment (Part-8)
0/1
Day-167: FastAPI (Part-2)
0/1
Day-168: FastAPI (Part-3)
0/1
Day-169: FastAPI (Part-4)
0/1
Day-170: FastAPI (Part-5)
0/1
Day-171: FastAPI (Part-6)
0/1
Day-174: FastAPI (Part-9)
0/1
Six months of AI and Data Science Mentorship Program
    Join the conversation
    Hafiz Muhammad Gulzar Alam 8 months ago
    Line Plot: Shows the relationship between two variables by connecting data points with straight lines. Bar Plot: Displays categorical data with rectangular bars, where the height or length of each bar represents the data value. Histogram: Represents the distribution of a continuous variable by dividing it into bins and displaying the frequency or count of observations within each bin. Scatter Plot: Displays the relationship between two continuous variables by placing individual data points on a two-dimensional plane. Pie Chart: Represents categorical data as a circular chart divided into sectors, where each sector represents a specific category and the area or angle of each sector represents the proportion of data. Box Plot: Visualizes the distribution of a continuous variable through quartiles, showing the median, interquartile range, and outliers. Heatmap: Displays data in a tabular form using colors to represent the values, with each cell color indicating the magnitude of the data. Area Plot: Shows the magnitude and proportion of different variables over time, with areas stacked on top of one another. Violin Plot: Combines a box plot and a kernel density plot to represent the distribution of a continuous variable. Network Graph: Represents relationships between entities as nodes and connections (edges) between them. Treemap: Displays hierarchical data using nested rectangles, where the area of each rectangle represents a specific value. Radar Chart: Displays multivariate data on a two-dimensional plane with multiple axes originating from a common center point. Bubble Chart: Represents three variables by using bubbles, where the x and y coordinates represent two variables, and the size of the bubble represents the third variable. Choropleth Map: Visualizes data by shading or coloring regions or areas based on the measured values, typically used for geographic or spatial data. Sankey Diagram: Illustrates the flow or movement of data or quantities between different entities using interconnected flows.
    Reply
    Talha Afzal 11 months ago
    Line Plot: Shows the relationship between two variables by connecting data points with straight lines.Bar Plot: Displays categorical data with rectangular bars, where the height or length of each bar represents the data value.Histogram: Represents the distribution of a continuous variable by dividing it into bins and displaying the frequency or count of observations within each bin.Scatter Plot: Displays the relationship between two continuous variables by placing individual data points on a two-dimensional plane.Pie Chart: Represents categorical data as a circular chart divided into sectors, where each sector represents a specific category and the area or angle of each sector represents the proportion of data.Box Plot: Visualizes the distribution of a continuous variable through quartiles, showing the median, interquartile range, and outliers.Heatmap: Displays data in a tabular form using colors to represent the values, with each cell color indicating the magnitude of the data.Area Plot: Shows the magnitude and proportion of different variables over time, with areas stacked on top of one another.Violin Plot: Combines a box plot and a kernel density plot to represent the distribution of a continuous variable.Network Graph: Represents relationships between entities as nodes and connections (edges) between them.Treemap: Displays hierarchical data using nested rectangles, where the area of each rectangle represents a specific value.Radar Chart: Displays multivariate data on a two-dimensional plane with multiple axes originating from a common center point.Bubble Chart: Represents three variables by using bubbles, where the x and y coordinates represent two variables, and the size of the bubble represents the third variable.Choropleth Map: Visualizes data by shading or coloring regions or areas based on the measured values, typically used for geographic or spatial data.Sankey Diagram: Illustrates the flow or movement of data or quantities between different entities using interconnected flows.
    Reply
    Talha Afzal 11 months ago
    There are numerous types of visualization plots used in data analysis and data visualization. Here are some commonly used types of visualization plots:1. Line Plot: Shows the relationship between two variables by connecting data points with straight lines.2. Bar Plot: Displays categorical data with rectangular bars, where the height or length of each bar represents the data value.3. Histogram: Represents the distribution of a continuous variable by dividing it into bins and displaying the frequency or count of observations within each bin.4. Scatter Plot: Displays the relationship between two continuous variables by placing individual data points on a two-dimensional plane.5. Pie Chart: Represents categorical data as a circular chart divided into sectors, where each sector represents a specific category and the area or angle of each sector represents the proportion of data.6. Box Plot: Visualizes the distribution of a continuous variable through quartiles, showing the median, interquartile range, and outliers.7. Heatmap: Displays data in a tabular form using colors to represent the values, with each cell color indicating the magnitude of the data.8. Area Plot: Shows the magnitude and proportion of different variables over time, with areas stacked on top of one another.9. Violin Plot: Combines a box plot and a kernel density plot to represent the distribution of a continuous variable.10. Network Graph: Represents relationships between entities as nodes and connections (edges) between them.11. Treemap: Displays hierarchical data using nested rectangles, where the area of each rectangle represents a specific value.12. Radar Chart: Displays multivariate data on a two-dimensional plane with multiple axes originating from a common center point.13. Bubble Chart: Represents three variables by using bubbles, where the x and y coordinates represent two variables, and the size of the bubble represents the third variable.14. Choropleth Map: Visualizes data by shading or coloring regions or areas based on the measured values, typically used for geographic or spatial data.15. Sankey Diagram: Illustrates the flow or movement of data or quantities between different entities using interconnected flows.These are just a few examples, and there are many more types of visualization plots available, each suitable for different types of data and analysis objectives.
    Saima Yousaf 12 months ago
    Please explain how do we know which type of Plot to select for the Dataset in question. You explained all data types but no highlight on "Why would we select a Bar Plot on a specific data, or Tree maps, or Heat maps etc.
    Reply
    Saima Yousaf 12 months ago
    Thanks Ammar, Andrew abela guide helped alot Ammar but still working on it. As it showed me errors on that AppsStore Data.
    Shahid Umar 12 months ago
    In this lecture, we learned Scatter, Line, Bar, Box, and spider plots and Pi, Donut, and area charts.
    Reply
    0% Complete