OBJECTIVES OF THE
TRAINING
DAY 1
- INTRODUCE TO MACHINE LEARNING AND THEIR
CONCEPTS
- SUPERVISED AND UNSUPERVISED LEARNING
- ALGORITHMS
- DEEP LEARNING CONCEPTS
DAY 2
- K Nearest Neighbor Theorem
- Practical uses of KNN
- Practice / Exercises using KNN model
DAY 3
- Naïve Bayes Algorithm
- Computing naïve bayes
- Practice / Exercises using naïve bayes
DAY 4
- Linear Regression
- Computation of Least Square Method (LSM)
- Practice / Exercises using Linear Regression model
DAY 5
- Deep Learning
- Practice case study of mask or with no mask
- Practice / Exercises using Linear Regression model
What is machine
learning?
Artificial intelligence (AI)
A h u ge set of tools for making comp u ters
beha v e intelligentl y
Artificial intelligence (AI)
A h u ge set of tools for making comp u ters
beha v e intelligentl y
Machine learning is the most pre v alent subset
of AI
Defining machine learning :
A set of tools for making inferences and predictions from data
Defining machine learning : w hat can it do?
Predict fu tu re events
Will it rain tomorrow?
Yes (75% probabilit y)
Infer the causes of events and behav iors
Why does it rain?
Time of the year, h u midit y levels, temperat u re , location , etc
Infer pa erns
What are the di erent types of weather conditions?
Rain, sunny, overcast, fog , etc
Defining machine learning : how does it work?
Interdisciplinar y mix of statistics and comp u ter science
Abilit y to learn w itho u t being e x plicitl y programmed
Learn pa erns from e x isting data and applies it to new data
Relies on high - q u alit y data
... more to come thro u gho u t the course!
Data science
Data science is abo u t disco v ering and
comm u nicating insights from data
MACHINE LEARNING FOR EVERYONE
Data science
Data science is abo u t making discoveries and
creating insights from data
Machine learning is o en an important tool
for data science work
MACHINE LEARNING FOR EVERYONE
Machine learning model
A statistical representation of a real -w orld process based on data
MACHINE LEARNING FOR EVERYONE
Machine learning model
A statistical representation of a real -w orld process based on data
MACHINE LEARNING FOR EVERYONE
Machine learning model
A statistical representation of a real -w orld process based on data
MACHINE LEARNING FOR EVERYONE
Machine learning model
A statistical representation of a real -w orld process based on data
MACHINE LEARNING FOR EVERYONE
Let's practice !
Machine learning
concepts
Three types of machine learning
1) Reinforcement learning
2) Supervised learning
3) Unsupervised learning
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Training data
Training data : ex isting data to learn from
Training a model : when a model is being b u ilt from training data
Can take nanoseconds to weeks
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Supervised learning training data
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Supervised learning training data
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Supervised learning training data
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Supervised learning training data
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Supervised learning training data
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After training (supervised learning )
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After training (supervised learning )
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After training (supervised learning )
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Supervised vs unsupervised learning
Supervised learning
Training data is " labeled "
Unsupervised learning
Training data only has feat u res
Useful for:
Anomal y detection
Clustering , e.g., dividing data into
groups
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Unsupervised learning training data
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Unsupervised learning training data
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After training (unsupervised learning )
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Unsupervised Learning
In realit y, data doesn ' t always come w ith labels
Requires man u al labor to label
Labels are u nkno wn
No labels: model is unsupervised and nds its o wn pa erns
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Let's practice !
Machine learning
workflow
Machine learning workflo w
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Our scenario
O u r dataset : NYC propert y sales from 2015-
2019
Includes:
Square feet
Neighborhood
Year b u ilt
Sale price
And more !
O u r target : Sale price
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Step 1: E x tract features
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Step 2: Split dataset
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Step 3: Train model
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Step 3: Train model
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Step 4: Evaluate
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Step 4: Evaluate
Test dataset : "unseen" data
Man y ways to evaluate:
What is the a v erage error of the predictions ?
What percent of apartments did the model acc u ratel y predict w ithin a 10% margin ?
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Step 4: Evaluate
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Step 4: Evaluate
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Step 4: Evaluate
If not , t u ne the model and re - train it:
e.g., change the model 's options , add / remo v e feat u res
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Machine learning workflo w
MACHINE LEARNING FOR EVERYONE
Summary of steps
1. E x tract feat u res
Choosing feat u res and manip u lating the dataset
2. Split dataset
Train and test dataset
3. Train model
Inp u t train dataset into a machine learning model
4. Evaluate
If desired performance isn't reached: t u ne the model and repeat Step 3
MACHINE LEARNING FOR EVERYONE
Let's practice !
https://forms.gle/4udVXNB8opA149GEA