Data Collection: Overview | Codebasics

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Contents

1.1: AI Innovations
1.2: Will AI Replace Data Analyst Jobs?

2.1: What is Machine Learning
2.2: Classification vs Regression
2.3: Supervised vs Unsupervised Learning
2.4: ML Algorithms Overview
2.5: Tooling for ML

3.1: 10 Stages of AI Project Lifecycle
3.2: Requirements and Scope of Work (SOW)
3.3: Data Collection
3.4: Data Preparation & Exploratory Data Analysis
3.5: Feature Engineering
3.6: Model Selection & Training
3.7: Model Evaluation Metrics (Accuracy, Prediction, Recall & F1 Score)
3.8: Model Evaluation Metrics: When to use which Metric?
3.9: Model Fine Tuning
3.10: Model Deployment
3.11: Deployment & Monitoring Using ML Ops

4.1: The Role of Data in AI
4.2: Data Infrastructure in a Company
4.3: Data Collection: Overview
4.4: Data Storage and Transformation: Overview
4.5: Data Distribution: Privacy, Ethics & Governance

5.1: Data Collection
5.2: Data Cleaning
5.3: Data Transformation
5.4: Data Visualization & Insights
5.5: 4 General Use Cases

6.1: Why Humans are Better in Communication Compared To AI?
6.2: Using AI for Emails & Presentations

7.1: Job Titles & Resume Customization
7.2: Asking For Referral
7.3: Mock Interviews: Resume & Job-Specific Interview Questions
7.4: Mock Interviews: Technical Interview
7.5: Mock Interviews: Case Study & Behavioral

8.1: PDF File of Useful AI Prompts

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Data Collection: Overview

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This lecture is a part of Data Analytics Bootcamp 2.0.
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