From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
AI and data science: Introduction - SQL Tutorial
From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
AI and data science: Introduction
- In a world where technology reigns supreme, artificial intelligence has risen to be the crown jewel. Consider this, the global AI market is exponentially growing. Business leaders report increased productivity through AI automation, and AI is expected to create millions of new jobs. It's clear, AI isn't just hype, it's here to stay, but let's get real. AI can seem like a daunting, mysterious realm. The jargon, the complexity, the fear of not being tech savvy enough. Hi, I'm Walter Shields and welcome to my course that will demystify AI and be your passport to unprecedented career opportunities.
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Contents
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Dive Into machine learning with regression3m 41s
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Understanding the essentials of linear regression4m 47s
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Navigating the world of multiple regression3m 59s
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Decoding the secrets of the regression line3m 44s
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Perfecting your model: Evaluation and validation3m 49s
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From Predicting to Understanding4m 46s
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Breaking Down Data Relationships with Regression4m 6s
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Crafting Elegance in Machine Learning Models2m 55s
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Hitting the Right Note - The Bias-Variance Trade-off4m 7s
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Fine-Tuning Your Models3m 33s
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Remixing Data with Bootstrapping3m 49s
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Regression Analysis: A Practical Application3m 54s
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Model Diagnostics and Assumption Testing5m 18s
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Unlocking the Secrets Behind Coefficients3m 55s
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Polynomial Paths and Interactions3m 33s
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Striking a Balance with Regularization Techniques3m 34s
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Beyond the Basics - Advanced Model Evaluation Metrics3m 58s
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Non-parametric Regression Methods4m 24s
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Case Studies in Regression Analysis3m 3s
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Importing necessary libraries and dataset overview3m 18s
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Loading the data7m 36s
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Checking the data info2m 13s
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Summary statistics of the dataset5m 49s
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Checking the distribution of the variables5m 42s
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Applying log transformation and re-checking distribution3m 6s
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Challenge: Preparation1m 5s
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Solution: Preparation1m 19s
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Bivariate analysis: Heat-map5m 23s
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Visualizing relationships: Age of homes and distance to work3m 52s
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Visualizing relationships: Highway access and property tax4m 9s
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Checking correlation after removing outliers3m 40s
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Visualizing relationships: Other pairs of variables5m 8s
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Challenge: Visualization1m 9s
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Solution: Visualization56s
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Creating the linear regression model and model summary: Part 19m 33s
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Creating the linear regression model and model summary: Part 27m 16s
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Creating the linear regression model and model summary: Part 35m 33s
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Dropping insignificant variables and re-creating the model7m 57s
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Checking assumptions for linear regression3m 18s
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Assumption 1: Checking for mean residuals2m 47s
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Assumption 2: Checking homoscedasticity3m 13s
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Assumption 3: Checking linearity2m 12s
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Assumption 4: Checking normality of error terms3m 24s
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Q-Q plot for checking the normality of error terms3m 14s
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Model performance comparison on train and test data6m 7s
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Applying cross-validation and evaluation4m 40s
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Challenge: Model building48s
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Solution: Model building1m 16s
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