This slide is a part of the Introductory Machine Learning Lab Sessions that I'm currently conducting in this Spring 2018 semester at Daffodil International University. I hope this would be helpful for my students as well as other enthusiasts.

1. Machine Learning 101
Session 1: Introductory Concepts
Nafis Neehal, Lecturer, CSE, DIU.
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2. Contents
 What is Machine Learning
 Supervised, Unsupervised, Reinforcement
Learning
 Training, Test, Validation data
 Basic Workflow of any ML Project
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3. Machine
Learning
Make the Machine Learn
To Act Rationally
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4. Pizza!!
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5. Pizza
SizeVSPrice
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Pizza Size (inch) Pizza Price (bdt)
6 430
9 580
12 720
14 950
16 1250

6. Pizza
Graph
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Pizza Size (inch) Pizza Price (bdt)
6 430
9 580
12 720
14 950
16 1250
430
580
720
950
1250
0
200
400
600
800
1000
1200
1400
0 2 4 6 8 10 12 14 16 18
PizzaPrice
Pizza Size
Pizza Graph

7. Approximation
(Linear Regression
-SP)
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430
580
720
950
1250
0
200
400
600
800
1000
1200
1400
0 2 4 6 8 10 12 14 16 18
PizzaPrice
Pizza Size
Pizza Graph

8. GIRAFFE
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Ossicones
Back HairLong Neck
Tail with hair

9. Dataset
(Feature
Extraction)
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Picture No Neck
Length
Tail
Length
Ossicones Is it
Giraffe?
1 8 inch 4 inch Yes Yes
2 6 inch 3 inch Yes Yes
3 2 inch 0 inch No No
4 1 inch 0 inch No No

10. Approximate
Decision Tree
(Classification -SP)
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Neck >= 6 inch
Tail >= 3 inch
Ossicones
Not
Giraffe
Not
Giraffe
Not
GiraffeGiraffe
Yes No
Yes
Yes
No
No

11. Clustering(USP)
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12. IRISFlower
Dataset (Most
Popular Dataset)
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13. Train,Validation,
Test
(Tutorial)
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Data
1st Split Data Test Data
Training Data Validation Data
Training (60%) Validation (20%) Test (20%)
Train your model to
fit the parameters
Tune the hyper
parameters of your
model, avoid over
fitting, choose
model
Test your model to
determine accuracy
and performance

14. Overfittingand
Underfitting
Avoid these two scenarios in order to
generalize well
• Over fitting = High Variance
• Under fitting = High Bias
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15. Cause
&
Prevention
AVOID OVER FITTING
 Happens when number of features is much higher than
number of training examples
 Increase number of training data (sometimes might not
work)
 Use Regularization (will talk later about this)
AVOID UNDER FITTING
 When number of features is much lower than number of
training examples
 Increase number of features
 Might use compound features, or can go to a higher
dimensional feature space using Kernels (will discuss later)
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16. Machine
LearningProject
Framework
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1. Data Import
2. Data Preprocessing
3. Feature Engineering and Extraction
4. Model Selection
5. Train Model with Training Data
6. Tune the Hyper Parameters with Validation
Data (learning rate, regularization parameter,
number of layers in Neural Network etc.)
7. Execute Model on Test Data
8. Performance Evaluation
9. Go Back to step 3 and repeat and until
satisfactory accuracy achieved
10.Conclude