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Seaborn – Basic Plot Types
Seaborn – Basic Plot Types Table Of Contents: Line Plot (sns.lineplot()) Scatter Plot (sns.scatterplot()) Bar Plot (sns.barplot()) Count Plot (sns.countplot()) Histogram (sns.histplot()) KDE Plot (sns.kdeplot()) (1) Line Plot (sns.lineplot()) A line plot is a type of graph that shows how something changes over time or along a continuous variable. It connects data points with a line to show trends and patterns. Example-1: Basic Line Plot import seaborn as sns import matplotlib.pyplot as plt # Sample Data data = { "year": [2015, 2016, 2017, 2018, 2019, 2020, 2021], "sales": [50, 55, 60, 80, 90, 100, 120] } # Create the plot
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Jupyter Lab Installation
Jupyter Lab Installation Table Of Contents: Download & Install Python. Verify Python Installation. Verify ‘pip’ Installation. Install JupyterLab. Verify JupyterLab Installation. Run JupyterLab. (1) Download & Install Python Download the latest version of the Python from the below link. https://www.python.org/downloads/ (2) Verify Python Installation After download and install you need to verify whether its installed correctly or not. Open a command prompt and run the below command to verify the Python installation. python – version (3) Verify ‘pip’ Installation pip – version (4) Install JupyterLab. Once Python and pip are installed, install Jupyter Lab using: pip install jupyterlab (5) Verify
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Seaborn – Basics Of Seaborn & Setup
Seaborn – Basics of Seaborn & Setup Table Of Contents: What is Seaborn? Why use it over Matplotlib? Installing Seaborn (pip install seaborn) Importing Seaborn & Matplotlib (import seaborn as sns) Understanding Seaborn’s built-in datasets (sns.get_dataset_names()) Loading datasets (sns.load_dataset(“tips”)) (1) What is Seaborn? Why Use It Over Matplotlib? What Is Seaborn? Why Use It Over Matplotlib ? (2) Installing Seaborn Library pip install seaborn (3) Verify The Installation pip show seaborn (4) Importing Seaborn & Matplotlib (import seaborn as sns) import seaborn as sns import matplotlib.pyplot as plt (5) Understanding Seaborn’s Built-in Datasets. import seaborn as sns # Get The
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Seaborn – Syllabus For Data Visualization
Seaborn – Learning Syllabus Table Of Contents: Basics of Seaborn & Setup Basic Plot Types Advanced Statistical Plots Pairwise Relationships & Correlation Plots Categorical Data Visualization Regression & Trend Analysis Customizing Seaborn Plots Working with Large Datasets & Complex Visualizations (1) Basics of Seaborn & Setup What is Seaborn? Why use it over Matplotlib? Installing Seaborn (pip install seaborn) Importing Seaborn & Matplotlib (import seaborn as sns) Understanding Seaborn’s built-in datasets (sns.get_dataset_names()) Loading datasets (sns.load_dataset(“tips”)) (2) Basic Plot Types Line Plot (sns.lineplot()) Scatter Plot (sns.scatterplot()) Bar Plot (sns.barplot()) Count Plot (sns.countplot()) Histogram (sns.histplot()) KDE Plot (sns.kdeplot()) (3) Advanced Statistical Plots
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Q & A – AWS Model Deployment
Q & A – AWS Model Deployement Table Of Contents: General ML Deployment Questions. Docker & Containerization. Cloud Deployment (AWS, GCP, Azure). API & Flask Integration. Scaling & Optimization. CI/CD & Automation. Security & Monitoring. (1) General ML Deployment Questions. What are the different ways to deploy a machine learning model? What is the difference between batch inference and real-time inference? What are the advantages and disadvantages of deploying models using Docker? What are edge AI deployments, and when should you use them? How do you handle model versioning in deployment? (2) General ML Deployment Questions. What is a Docker
