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1. Machine learning (ML) is a subdomain of artificial
intelligence (AI) that focuses on developing systems
that learn or improve performance—based on the data
they ingest. Artificial intelligence is a broad word
that refers to systems or machines that resemble
human intelligence.
What is Machine Learning?
• Machine Learning is the field of study that gives
computers the capability to learn without being
explicitly programmed. ML is one of the most
exciting technologies that one would have ever come
across. As it is evident from the name, it gives
the computer that makes it more similar to humans:
The ability to learn. Machine learning is actively
being used today, perhaps in many more places than
one would expect.

2. Network Layer 4-2
Chapter 4: Network Layer
Chapter goals:
• understand principles behind
network layer services:
• network layer service models
• forwarding versus routing
• how a router works
• routing (path selection)
• dealing with scale
• advanced topics: IPv6, mobility
• instantiation, implementation in
the Internet

3. Supervised, semi
supervised learning,
clustering,
classification,
regression, SVM,
Big data basics, its
components and Hadoop
installations

4. Features of Machine learning
• Machine learning is data driven technology. Large amount of data
generated by organizations on daily bases. So, by notable relationships
in data, organizations makes better decisions.
• Machine can learn itself from past data and automatically improve.
• From the given dataset it detects various patterns on data.
• For the big organizations branding is important and it will become more
easy to target relatable customer base.
• It is similar to data mining because it is also deals with the huge amount
of data.

5. Properties ML
• Learning from Data: Machine learning algorithms learn patterns and relationships from
data rather than being explicitly programmed to perform a task.
• Adaptability: Machine learning models can adapt and improve their performance over
time as they are exposed to more data.
• Automation: Once trained, machine learning models can automate the process of
making predictions or decisions based on new data.
• Generalization: Machine learning models aim to generalize patterns from the training
data to make predictions or decisions on new, unseen data.
• Scalability: Machine learning methods can often scale to handle large amounts of data
and complex tasks.
• Robustness: Well-designed machine learning models can be robust to noise and outliers
in the data, making them suitable for real-world applications.

6. Available ML
Algorithms

7. Evaluation Metrics
Confusion Matrix (N X N matrix)
It is extremely useful for measuring precision-recall, Specificity, Accuracy, and
most importantly, AUC-ROC curves.

8. Accuracy: The proportion of correctly classified instances out of the
total instances.
Precision / positive predictive value : It measures the proportion of
true positive predictions among all positive predictions made by the
model.
TP
(TP + FP)
Recall (Sensitivity): It measures the proportion of true positives that
were correctly identified by the model out of all actual positives.
TP
(TP + FN)
Recall is important when the cost of false negatives is high..

9. F1 Score: The harmonic mean of precision and recall. It provides a balance
between precision and recall. It is calculated as
2 * (Precision * Recall)
(Precision + Recall)
Specificity: It measures the proportion of true negatives that were correctly
identified by the model out of all actual negatives.
TN
(TN + FP)
ROC AUC (Receiver Operating Characteristic Area Under the Curve): It
measures the area under the ROC curve
sensitivity
1-specificity

10. Mean Absolute Error (MAE): The average of the absolute differences
between predicted and actual values.
Mean Squared Error (MSE): The average of the squared differences
between predicted and actual values.
Root Mean Squared Error (RMSE): The square root of the MSE.
R-squared (Coefficient of Determination): It measures the proportion of the
variance in the dependent variable that is predictable from the independent
variables.

11. Algorithms Evaluation
Metrics
Regression Mean Absolute Error, Mean
Squared Error, Root Mean
Squared Error, R-squared,
Mean Absolute Percentage
Error ,Median Absolute
Error
Classification Accuracy, Precision,
Recall (Sensitivity), F1-
score, ROC-AUC, Precision-
Recall Curve, Confusion
Matrix
Especially in binary
classification
Precision, Recall, and F1-
score
Clustering Metrics Silhouette Score,
Calinski-Harabasz Index,
Davies-Bouldin Index,
Adjusted Rand Index (ARI)

12. What is Supervised learning?
• Supervised learning, as the name indicates, has the
presence of a supervisor as a teacher. Supervised
learning is when we teach or train the machine
using data that is well-labelled. Which means some
data is already tagged with the correct answer.
After that, the machine is provided with a new set
of examples(data) so that the supervised learning
algorithm analyses the training data(set of
training examples) and produces a correct outcome
from labeled data.
• For example, a labeled dataset of images of
Elephant, Camel and Cow would have each image
tagged with either “Elephant”,“Camel”or “Cow.”
• Key Points:
Supervised learning involves training a machine from labeled data.
Labeled data consists of examples with the correct answer or
classification.
The machine learns the relationship between inputs (fruit images) and
outputs (fruit labels).

14. Machine learning,
Supervised, semi supervised learning,
clustering, classification,
regression, SVM,
Big data basics, its components and Hadoop installations
Machine learning
https://www.geeksforgeeks.org/machine-learning/
Supervised : types - classification, regression (also unsupervised is here), clustering
https://www.geeksforgeeks.org/supervised-unsupervised-learning/
semi supervised learning,
https://www.geeksforgeeks.org/ml-semi-supervised-learning/
SVM : https://www.geeksforgeeks.org/support-vector-machine-algorithm/
Big data basics, its components
https://www.geeksforgeeks.org/what-is-big-data/
https://www.geeksforgeeks.org/difference-between-traditional-data-and-big-data/
Hadoop
https://www.geeksforgeeks.org/hadoop-an-introduction/
installations
https://www.javatpoint.com/hadoop-installation
https://medium.com/@DataEngineeer/how-to-set-up-hadoop-on-windows-a-step-by-step-guide-37d1ab4bee57

15. • Types of Unsupervised Learning