Deep Learning vs Statistical Machine Learning: When to use What?

10.1: All Jargons, Key Takeaways & Tooling

11.1: Quiz

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.

Hemanand Vadivel

Ex- Data Analytics Manager, 8+ Years in Europe, Microsoft Certified, Certified Supply Chain Professional

I’m a mechanical engineer who transitioned to a full time Data & Analytics manager in the UK & Germany by teaching myself Power BI, excel & anything else that was required to solve the problem. I have worked with complex data across Supply Chain, Sales, Marketing, Revenue Management, Finance and HR functions over the last 8 years to deliver effective solutions. To me, Analytics is an extra sensory organ that anyone can develop to become a superhuman at work. I have conducted several workshops and internal training to help non-technical people to become superhuman at work and now, really excited for this opportunity to share all that knowledge with you in this course.

Deep Learning vs Statistical Machine Learning: When to use What?

Prev Next

Lecture Content Locked

This course is now closed to new enrollments. If you've already enrolled, your access continues as usual.

Contents

1: Welcome to AI Experience

1.1: Why This Course?

1.2: What Will You Learn? (Course Overview)

1.3: Join Discord for Networking

1.4: The AI family tree

2: Enhance your Learning Experience

2.1: Join Discord for doubt clearing support & networking

2.2: Setting Video Resolution and Video Speed Globally

3: Demystify AI: Machine Learning

3.1: What is Machine Learning

3.2: Classification vs Regression

3.3: Supervised vs Unsupervised Learning

3.4: ML Algorithms Overview

3.5: Tooling for ML

3.6: Introduction: Building Your Online Credibility on LinkedIn

3.7: Task: Register Your Voice on AI

3.8: Do This to Get Max Out of This Course

3.9: Takeaways & Jargons

3.10: Quiz

4: Demystify AI: Deep Learning

4.1: What is Deep Learning

4.2: Deep Learning vs Statistical Machine Learning: When to use What?

4.3: Credits Scoring to ChatGPT: Overview of Neural Network Architectures

4.4: Tooling for Deep Learning

4.5: Bank Employee To AI Engineer: Transition Story

4.6: Task: Get Engagement by Sharing an AI Learning Resource

4.7: Takeaways & Jargons

4.8: Quiz

5: AI/ML Project Lifecycle

5.1: 10 Stages of AI Project Lifecycle

5.2: Requirements and Scope of Work (SOW)

5.3: Data Collection

5.4: Data Preparation & Exploratory Data Analysis

5.5: Feature Engineering

5.6: Model Selection & Training

5.7: Model Evaluation Metrics (Accuracy, Prediction, Recall & F1 Score)

5.8: Model Evaluation Metrics: When to use which Metric?

5.9: Model Fine Tuning

5.10: Model Deployment

5.11: Deployment & Monitoring Using ML Ops

5.12: Online Credibility: Engage Meaningfully AI Influencers List 2024

5.13: Task: Post About AI/ML Project Steps

5.14: Takeaways & Jargons

5.15: Quiz

6: Demystify AI: Gen AI, LLMs and NLP

6.1: What is Generative AI (or Gen AI)?

6.2: Evolution of Gen AI Models

6.3: What is LLM? Analogy Based Simple Explanation

6.4: What is NLP?

6.5: Embeddings and Vector Database

6.6: Retrieval Augmented Generation (RAG)

6.7: Prompt Engineering

6.8: Tooling for Gen AI, LLM

6.9: Task: Share Your Unique Perspectives on Gen AI

6.10: Takeaways & Jargons

6.11: Quiz

7: Data & AI

7.1: The Role of Data in AI

7.2: Data Infrastructure in a Company

7.3: Data Collection: Overview

7.4: Data Storage and Transformation: Overview

7.5: Data Distribution: Privacy, Ethics & Governance

7.6: Online Credibility: Mental Model of Content Creation

7.7: Task: Emphasize the Importance of Data in AI

7.8: Takeaways & Jargons

7.9: Quiz

8: 6 Industry Case Studies

8.1: Text Classification: Support Ticket Prioritization

8.2: Image Classification: Crop Yield Detection New

8.3: RAG-Based Gen AI: ChatGPT for Private Organizational Data

8.4: Chatbot: Food Delivery Chatbot

8.5: LLM Powered Real Estate Chatbot New

8.6: Recommendation System: Book Recommendations

8.7: Task: Build a Case Study on a Company that leverages AI

8.8: Takeaways & Jargons

8.9: Quiz

9: AI Career Role, Skills and Salaries

9.1: AI Career Roles: Technical and Non-Technical

9.2: Which AI Career is the Best For You? AI Career Role Selection Guide