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https://itakigawa.github.io/
Exploring practices in
machine learning and machine discovery
for heterogeneous catalysis
Ichi Takigawa
Institute for Liberal Arts and Sciences, Kyoto University
Institute for Chemical Reaction Design and Discovery, Hokkaido University
RIKEN Center for Advanced Intelligence Project
Share a viewpoint from the ML side (as I am an ML researcher, not a chemist)
after >7 years struggling in heterogeneous catalyst design and discovery
With great people in chemistry!
Prof. Ken-ichi
SHIMIZU
Prof. Takashi
TOYAO
Prof. Satoru Takakusagi
Prof. Zen Maeno
Prof. Takashi Kamachi
Keisuke Suzuki
Shoma Kikuchi
Shinya Mine
Takumi Mukaiyama
Motoshi Takao
Yuan Jing
Gang Wang
Duotian Chen
Kah Wei Ting
Taichi Yamaguchi
Koichi Matsushita
S.M.A.H. Siddiki
Prof. Koji Tsuda (U Tokyo)
This talk
Gas-phase reactions on solid-phase catalyst surface (Heterogeneous catalysis)
Industrial Synthesis (e.g. Haber-Bosch), Automobile Exhaust Gas Purification, Methane Conversion, etc.
https://en.wikipedia.org/wiki/Heterogeneous_catalysis
Reactants
(Gas)
Catalysts
(Solid)
Nano-particle
surface
High Temperature, High Pressure
Adsorption
Diffusion
Dissociation
Recombination
Desorption
Heterogeneous catalysis
Gas-phase reactions on solid-phase catalyst surface (Heterogeneous catalysis)
Industrial Synthesis (e.g. Haber-Bosch), Automobile Exhaust Gas Purification, Methane Conversion, etc.
https://en.wikipedia.org/wiki/Heterogeneous_catalysis
Reactants
(Gas)
Catalysts
(Solid)
Nano-particle
surface
High Temperature, High Pressure
Adsorption
Diffusion
Dissociation
Recombination
Desorption
Involves devilishly complex too-many-factor processes.
A solid surface shares its border with the external world.
God made the bulk; the surface was invented by the devil —— Wolfgang Pauli
Heterogeneous catalysis
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning
approach. https://doi.org/10.26434/chemrxiv-2022-695rj
Our recent research: Results
Our Target:
Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning
approach. https://doi.org/10.26434/chemrxiv-2022-695rj
• Discovered more than 100 catalysts better
than the previously reported best catalyst.
Our recent research: Results
Our Target:
Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning
approach. https://doi.org/10.26434/chemrxiv-2022-695rj
• Discovered more than 100 catalysts better
than the previously reported best catalyst.
• 300 catalysts tested in total by 44 cycles of
ML prediction + experiment
Our recent research: Results
Our Target:
Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning
approach. https://doi.org/10.26434/chemrxiv-2022-695rj
• Discovered more than 100 catalysts better
than the previously reported best catalyst.
• 300 catalysts tested in total by 44 cycles of
ML prediction + experiment
• The optimal catalyst Pt(3)/Rb(1)-Ba(1)-
Mo(0.6)-Nb(0.2)/TiO2 was hardly predictable
by human experts
Our recent research: Results
Our Target:
Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning
approach. https://doi.org/10.26434/chemrxiv-2022-695rj
• Discovered more than 100 catalysts better
than the previously reported best catalyst.
• 300 catalysts tested in total by 44 cycles of
ML prediction + experiment
• The optimal catalyst Pt(3)/Rb(1)-Ba(1)-
Mo(0.6)-Nb(0.2)/TiO2 was hardly predictable
by human experts
• Notably, Nb was never used in training.
Our recent research: Results
Our Target:
Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
Decision tree ensembles (with UQ)
i.e. histogram on data-dependent partitions
• ExtraTrees regressor
• Gradient Boosted Trees regressor
+ Abstracted (coarse grained) featurization of
chemical compositions
Input representations by elemental features
e.g. “composition-based feature vector (CBFV)”
Pt(3)/Ba(2)-Mo(1)-Tm(1)-
Eu(0.5)-Dy(0.5)/TiO2
Pt(3)/Mo(1)-Ba(1)-Tb(1)-
Ho(1)-Cs(0.5)/TiO2
Pt(3)/Rb(1)-Ba(1)-
Mo(0.6)-Nb(0.2)/TiO2
Our recent research: Method
Decision tree ensembles (with UQ)
i.e. histogram on data-dependent partitions
• ExtraTrees regressor
• Gradient Boosted Trees regressor
+ Abstracted (coarse grained) featurization of
chemical compositions
Input representations by elemental features
e.g. “composition-based feature vector (CBFV)”
Pt(3)/Ba(2)-Mo(1)-Tm(1)-
Eu(0.5)-Dy(0.5)/TiO2
Pt(3)/Mo(1)-Ba(1)-Tb(1)-
Ho(1)-Cs(0.5)/TiO2
Pt(3)/Rb(1)-Ba(1)-
Mo(0.6)-Nb(0.2)/TiO2
Very Conservative Prediction
(Histogram)
Very Radical Representation
(Discard specific details)
Our recent research: Method
Decision tree ensembles (with UQ)
i.e. histogram on data-dependent partitions
• ExtraTrees regressor
• Gradient Boosted Trees regressor
+ Abstracted (coarse grained) featurization of
chemical compositions
Input representations by elemental features
e.g. “composition-based feature vector (CBFV)”
Pt(3)/Ba(2)-Mo(1)-Tm(1)-
Eu(0.5)-Dy(0.5)/TiO2
Pt(3)/Mo(1)-Ba(1)-Tb(1)-
Ho(1)-Cs(0.5)/TiO2
Pt(3)/Rb(1)-Ba(1)-
Mo(0.6)-Nb(0.2)/TiO2
This talk will hopefully explain why we go for such a standard method choice
(even though I’m a ML researcher doing research also in GNNs and Transformers)
Very Conservative Prediction
(Histogram)
Very Radical Representation
(Discard specific details)
Our recent research: Method
At first I had an optimistic image of the unfamiliar field of "Materials Informatics"...
(after I worked in machine learning for bioinformatics for 10 years)
Step 1 Step 2 Step 3
We give all possible types of
available data into ML
ML becomes smarter
than standard experts
ML suggests more and more
promising materials
My prologue: Materials informatics?
Three lessons learned as I experienced this illusion being shattered…
Takeaways
Three lessons learned as I experienced this illusion being shattered…
1. The goals of ML and ‘materials/chemical science’ are fundamentally different.
What we need here is not ML but a much harder problem of ‘machine discovery.’
Takeaways
Three lessons learned as I experienced this illusion being shattered…
1. The goals of ML and ‘materials/chemical science’ are fundamentally different.
What we need here is not ML but a much harder problem of ‘machine discovery.’
2. If we go for a hypothesis-free + off-the-shelf solution, exploration by decision tree
ensembles, combined with UQ and abstracted (coarse grained) feature
representations, will give a very strong baseline.
Takeaways
Three lessons learned as I experienced this illusion being shattered…
1. The goals of ML and ‘materials/chemical science’ are fundamentally different.
What we need here is not ML but a much harder problem of ‘machine discovery.’
2. If we go for a hypothesis-free + off-the-shelf solution, exploration by decision tree
ensembles, combined with UQ and abstracted (coarse grained) feature
representations, will give a very strong baseline.
3. If we want more than that, we can’t be hypothesis free. Any strategies to narrow
down the scope as well as domain expertise really matters.
Takeaways
Get weight (g) & height (cm)
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
5 6.25 7.5 8.75 10
90
112.5
135
157.5
180
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Get weight (g) & height (cm)
Weight (g)
Height (cm)
Apple
Orange
Apple
Orange
Machine Learning converts data into predictions
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis

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Exploring Practices in Machine Learning and Machine Discovery for Heterogeneous Catalysis

  • 1. https://itakigawa.github.io/ Exploring practices in machine learning and machine discovery for heterogeneous catalysis Ichi Takigawa Institute for Liberal Arts and Sciences, Kyoto University Institute for Chemical Reaction Design and Discovery, Hokkaido University RIKEN Center for Advanced Intelligence Project
  • 2. Share a viewpoint from the ML side (as I am an ML researcher, not a chemist) after >7 years struggling in heterogeneous catalyst design and discovery With great people in chemistry! Prof. Ken-ichi SHIMIZU Prof. Takashi TOYAO Prof. Satoru Takakusagi Prof. Zen Maeno Prof. Takashi Kamachi Keisuke Suzuki Shoma Kikuchi Shinya Mine Takumi Mukaiyama Motoshi Takao Yuan Jing Gang Wang Duotian Chen Kah Wei Ting Taichi Yamaguchi Koichi Matsushita S.M.A.H. Siddiki Prof. Koji Tsuda (U Tokyo) This talk
  • 3. Gas-phase reactions on solid-phase catalyst surface (Heterogeneous catalysis) Industrial Synthesis (e.g. Haber-Bosch), Automobile Exhaust Gas Purification, Methane Conversion, etc. https://en.wikipedia.org/wiki/Heterogeneous_catalysis Reactants (Gas) Catalysts (Solid) Nano-particle surface High Temperature, High Pressure Adsorption Diffusion Dissociation Recombination Desorption Heterogeneous catalysis
  • 4. Gas-phase reactions on solid-phase catalyst surface (Heterogeneous catalysis) Industrial Synthesis (e.g. Haber-Bosch), Automobile Exhaust Gas Purification, Methane Conversion, etc. https://en.wikipedia.org/wiki/Heterogeneous_catalysis Reactants (Gas) Catalysts (Solid) Nano-particle surface High Temperature, High Pressure Adsorption Diffusion Dissociation Recombination Desorption Involves devilishly complex too-many-factor processes. A solid surface shares its border with the external world. God made the bulk; the surface was invented by the devil —— Wolfgang Pauli Heterogeneous catalysis
  • 5. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. https://doi.org/10.26434/chemrxiv-2022-695rj Our recent research: Results Our Target: Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
  • 6. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. https://doi.org/10.26434/chemrxiv-2022-695rj • Discovered more than 100 catalysts better than the previously reported best catalyst. Our recent research: Results Our Target: Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
  • 7. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. https://doi.org/10.26434/chemrxiv-2022-695rj • Discovered more than 100 catalysts better than the previously reported best catalyst. • 300 catalysts tested in total by 44 cycles of ML prediction + experiment Our recent research: Results Our Target: Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
  • 8. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. https://doi.org/10.26434/chemrxiv-2022-695rj • Discovered more than 100 catalysts better than the previously reported best catalyst. • 300 catalysts tested in total by 44 cycles of ML prediction + experiment • The optimal catalyst Pt(3)/Rb(1)-Ba(1)- Mo(0.6)-Nb(0.2)/TiO2 was hardly predictable by human experts Our recent research: Results Our Target: Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
  • 9. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. https://doi.org/10.26434/chemrxiv-2022-695rj • Discovered more than 100 catalysts better than the previously reported best catalyst. • 300 catalysts tested in total by 44 cycles of ML prediction + experiment • The optimal catalyst Pt(3)/Rb(1)-Ba(1)- Mo(0.6)-Nb(0.2)/TiO2 was hardly predictable by human experts • Notably, Nb was never used in training. Our recent research: Results Our Target: Pt(3)/X1-X2-X3-X4-X5/TiO2 RWGS Calalyst
  • 10. Decision tree ensembles (with UQ) i.e. histogram on data-dependent partitions • ExtraTrees regressor • Gradient Boosted Trees regressor + Abstracted (coarse grained) featurization of chemical compositions Input representations by elemental features e.g. “composition-based feature vector (CBFV)” Pt(3)/Ba(2)-Mo(1)-Tm(1)- Eu(0.5)-Dy(0.5)/TiO2 Pt(3)/Mo(1)-Ba(1)-Tb(1)- Ho(1)-Cs(0.5)/TiO2 Pt(3)/Rb(1)-Ba(1)- Mo(0.6)-Nb(0.2)/TiO2 Our recent research: Method
  • 11. Decision tree ensembles (with UQ) i.e. histogram on data-dependent partitions • ExtraTrees regressor • Gradient Boosted Trees regressor + Abstracted (coarse grained) featurization of chemical compositions Input representations by elemental features e.g. “composition-based feature vector (CBFV)” Pt(3)/Ba(2)-Mo(1)-Tm(1)- Eu(0.5)-Dy(0.5)/TiO2 Pt(3)/Mo(1)-Ba(1)-Tb(1)- Ho(1)-Cs(0.5)/TiO2 Pt(3)/Rb(1)-Ba(1)- Mo(0.6)-Nb(0.2)/TiO2 Very Conservative Prediction (Histogram) Very Radical Representation (Discard specific details) Our recent research: Method
  • 12. Decision tree ensembles (with UQ) i.e. histogram on data-dependent partitions • ExtraTrees regressor • Gradient Boosted Trees regressor + Abstracted (coarse grained) featurization of chemical compositions Input representations by elemental features e.g. “composition-based feature vector (CBFV)” Pt(3)/Ba(2)-Mo(1)-Tm(1)- Eu(0.5)-Dy(0.5)/TiO2 Pt(3)/Mo(1)-Ba(1)-Tb(1)- Ho(1)-Cs(0.5)/TiO2 Pt(3)/Rb(1)-Ba(1)- Mo(0.6)-Nb(0.2)/TiO2 This talk will hopefully explain why we go for such a standard method choice (even though I’m a ML researcher doing research also in GNNs and Transformers) Very Conservative Prediction (Histogram) Very Radical Representation (Discard specific details) Our recent research: Method
  • 13. At first I had an optimistic image of the unfamiliar field of "Materials Informatics"... (after I worked in machine learning for bioinformatics for 10 years) Step 1 Step 2 Step 3 We give all possible types of available data into ML ML becomes smarter than standard experts ML suggests more and more promising materials My prologue: Materials informatics?
  • 14. Three lessons learned as I experienced this illusion being shattered… Takeaways
  • 15. Three lessons learned as I experienced this illusion being shattered… 1. The goals of ML and ‘materials/chemical science’ are fundamentally different. What we need here is not ML but a much harder problem of ‘machine discovery.’ Takeaways
  • 16. Three lessons learned as I experienced this illusion being shattered… 1. The goals of ML and ‘materials/chemical science’ are fundamentally different. What we need here is not ML but a much harder problem of ‘machine discovery.’ 2. If we go for a hypothesis-free + off-the-shelf solution, exploration by decision tree ensembles, combined with UQ and abstracted (coarse grained) feature representations, will give a very strong baseline. Takeaways
  • 17. Three lessons learned as I experienced this illusion being shattered… 1. The goals of ML and ‘materials/chemical science’ are fundamentally different. What we need here is not ML but a much harder problem of ‘machine discovery.’ 2. If we go for a hypothesis-free + off-the-shelf solution, exploration by decision tree ensembles, combined with UQ and abstracted (coarse grained) feature representations, will give a very strong baseline. 3. If we want more than that, we can’t be hypothesis free. Any strategies to narrow down the scope as well as domain expertise really matters. Takeaways
  • 18. Get weight (g) & height (cm) Apple Orange Machine Learning converts data into predictions
  • 19. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions
  • 20. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions
  • 21. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions
  • 22. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions
  • 23. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions
  • 24. 5 6.25 7.5 8.75 10 90 112.5 135 157.5 180 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Get weight (g) & height (cm) Weight (g) Height (cm) Apple Orange Apple Orange Machine Learning converts data into predictions