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Activity: Slide Makeover

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ART OF TEACHING @ CODEBASICS · TEACHING LAB 1 · PRACTICE 1
Slide Makeover
A real death-by-PowerPoint slide is on the table. Four decisions stand between it and a Codebasics-grade lesson. Operate carefully.
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How the surgery works
1
Meet the patient, one genuinely terrible (and terribly common) slide.
2
Make 4 makeover decisions. Pick what a Codebasics instructor would do.
3
Watch the slide transform after every correct call, then download your surgery report (PDF).
THE PATIENT
Machine Learning, Introduction
Submitted by a (fictional) new instructor. Take a good look. Feel the pain of the back row.
THE SLIDE, AS SUBMITTED
Machine Learning, Introduction, Types, Algorithms, Applications & Future Scope 🤖
• Machine learning is a subset of artificial intelligence that enables systems to learn from data
• Types: supervised learning, unsupervised learning, semi-supervised, reinforcement learning
• Algorithms: linear regression, logistic regression, decision trees, random forest, SVM, KNN, K-means...
• Our internal churn model achieved 87% accuracy on the holdout set (see appendix 4B)
• Applications: recommendations, fraud detection, medical imaging, self-driving cars, chatbots
• Future scope: AGI, edge ML, AutoML, quantum machine learning, neuromorphic computing
• Prerequisites: statistics, linear algebra, calculus, Python, SQL, cloud fundamentals
• NOTE: please hold all questions until the end of the section!!
🤖🧠⚡
Eight bullets · 7.5px text · three accent colors · an underlined red title · emoji clipart · and a “hold all questions” threat. This slide breaks at least five Codebasics rules at once. Let’s operate.
DECISION 1 OF 4
Slide Makeover
Eight bullets, three colors, a robot emoji. What is the FIRST surgery?
THE PATIENT (as submitted)
Machine Learning, Introduction, Types, Algorithms, Applications & Future Scope 🤖
• Machine learning is a subset of artificial intelligence that enables systems to learn from data
• Types: supervised learning, unsupervised learning, semi-supervised, reinforcement learning
• Algorithms: linear regression, logistic regression, decision trees, random forest, SVM, KNN, K-means...
• Our internal churn model achieved 87% accuracy on the holdout set (see appendix 4B)
• Applications: recommendations, fraud detection, medical imaging, self-driving cars, chatbots
• Future scope: AGI, edge ML, AutoML, quantum machine learning, neuromorphic computing
• Prerequisites: statistics, linear algebra, calculus, Python, SQL, cloud fundamentals
• NOTE: please hold all questions until the end of the section!!
🤖🧠⚡
A.  Make the fonts bigger so the back row can finally read all eight bullets.
B.  Split it: one idea per slide. This slide keeps only the core message.
C.  Add a professional background image behind the text to make it less boring.
Reveal the correct surgery
Principle: One idea per slide.

Bigger fonts on eight bullets is a louder document; a background image is lipstick on a document. The surgery is subtraction: this slide earns ONE job - the core message - and every other bullet gets its own slide or gets cut.
AFTER DECISION 1
Machine learning finds patterns in data, so you don’t have to write the rules.
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DECISION 2 OF 4
Slide Makeover
The 87% accuracy stat was buried in bullet 4. Where does it live now?
CURRENT STATE
Machine learning finds patterns in data, so you don’t have to write the rules.
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A.  Bold it inside a bullet list on the next slide.
B.  Put it in the slide footer as a citation.
C.  Give it its own billboard slide: 87% huge, one quiet line under it.
Reveal the correct surgery
Principle: Big fonts, a slide is a billboard.

A number that proves your whole story deserves a slide, not a hiding place. 46-80pt, green, alone. The learner reads it in one second and remembers it next week - that is what slides are FOR.
AFTER DECISION 2
87%
accuracy, our churn model, on data it had never seen
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DECISION 3 OF 4
Slide Makeover
The robot emoji collage is gone. What imagery replaces it?
CURRENT STATE
87%
accuracy, our churn model, on data it had never seen
Codebasics INC
A.  One real photo from daily life, with a single caption connecting it to ML.
B.  A tasteful set of flat icons: robot, brain, gears, lightning.
C.  No imagery at all - imagery is decoration and decoration distracts.
Reveal the correct surgery
Principle: Photos over clipart, one image, one feeling.

Icon sets are clipart in a suit. A real photo of a real moment (your commute, your kitchen) gives the concept a body the learner already owns. And 'no imagery ever' throws away the fastest highway to memory - the rule is fewer, realer images, not zero.
AFTER DECISION 3
Where you already met ML today
Your morning commute: maps reroute, music queues, fraud checks, all ML.
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DECISION 4 OF 4
Slide Makeover
You still must teach the 4 types of ML. How do they appear?
CURRENT STATE
Where you already met ML today
Your morning commute: maps reroute, music queues, fraud checks, all ML.
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A.  A clean 2x2 grid - all four visible so learners see the full map at once.
B.  One type per click: the first appears, gets taught, then the next.
C.  Skip the types - taxonomy is boring, jump straight to a demo.
Reveal the correct surgery
Principle: Reveal one thing at a time.

Show all four at once and learners read type 4 while you teach type 1 - the grid steals your own audience. Click-to-appear keeps the whole class on the same idea at the same moment. (And skipping structure entirely isn't simplicity, it's abdication.)
AFTER DECISION 4
The 4 types of machine learning
1 · Supervised, learning with an answer key
types appear one per click as you teach
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The transformation
BEFORE, 1 slide, 8 ideas, 0 attention
Machine Learning, Introduction, Types, Algorithms, Applications & Future Scope 🤖
• Machine learning is a subset of artificial intelligence that enables systems to learn from data
• Types: supervised learning, unsupervised learning, semi-supervised, reinforcement learning
• Algorithms: linear regression, logistic regression, decision trees, random forest, SVM, KNN, K-means...
• Our internal churn model achieved 87% accuracy on the holdout set (see appendix 4B)
• Applications: recommendations, fraud detection, medical imaging, self-driving cars, chatbots
• Future scope: AGI, edge ML, AutoML, quantum machine learning, neuromorphic computing
• Prerequisites: statistics, linear algebra, calculus, Python, SQL, cloud fundamentals
• NOTE: please hold all questions until the end of the section!!
🤖🧠⚡
AFTER, 4 slides, 1 idea each
Machine learning finds patterns in data, so you don’t have to write the rules.
Codebasics INC
87%
accuracy, our churn model, on data it had never seen
Codebasics INC
Where you already met ML today
Your morning commute: maps reroute, music queues, fraud checks, all ML.
Codebasics INC
The 4 types of machine learning
1 · Supervised, learning with an answer key
types appear one per click as you teach
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YOUR RESULT
– / 4
Complete the steps above
(If the buttons are not interactive in your LMS view, score yourself using the reveals above.)
Every slide you save, saves a classroom.
Now open the deck for your own baseline video. Find its worst slide. Operate with the same four moves, and be as ruthless with your slides as you just were with this one.
Codebasics INC · Art of Teaching Crash Course · Session 2: Inside Our Teaching Lab 1, Practice 1: Slide Makeover