What is Machine Learning?
Machine learnin Why do we use Machine learning?
g
The subfield of artificial intelligence that
focuses on the development of algorithms that Machine learning is branch of artificial learning that has the
can learn from the data and improve their ability to learn from the past data without the need of
performance on a specific task without the need being explicitly programmed. Machine learning algorithms have
for being explicitly programmed. the abilility to make predictions/ decisions based on this
pass data. They are capable of understanding the patterns in the
Traditional Programming data which may/may not be visible to human mind
The following are several key reasons why we use machine
Data Traditional
Output
learning:-
Programming ① Automation & Efficiency:- ML automates repetitive task and
Program and processes large datasets quickly, enhancing overall efficiency.
Machine learning ② Data Driven Insights:- ML identify hidden patterns and provide valuable
insights, used for better decision-making.
Data Machine
learning program ③ Improved Accuracy & Precision:- Since ML models learn from past data
Output and make decisions accordingly meaning they improve over time,
leading to much accurate & reliable classification 4 predictions.
Thus we can conclude that while in traditional programming
it is require to provide data and program to get the ④ Personalization & Adaptability:- ML enables personalized experience, enhancing
output, Machine learning on the other hand is capable user engagement & has the ability to adapt new data, making it suitable
of learning from experience and make decisions/ predictions for dynamic 4 evolving environment.
based on the old data without the need of being
programmed explicitly.
Types of Machine learning ÷
→ Unsupervised Machine Learning ÷
→ Supervised Machine learning:-
① Supervised machine learning is sub branch of artificial ① Unsupervised machine learning is
intelligence that focuses on training model and make predictions based on unlabelled data , w
based on the labelled training data. patterns and relationships in
② It involves a learning process where the model learns from ② The goal of unsupervised learnin
known examples to predict and classify unknown, unseen or patterns, similarities or clusters
future instances accurately. used for various purposes such
③ supervised machine learning has 2 key components:- dimensity reduction.etc
→ Input data ③ Unsupervised machine learning
→ corresponding output labels. → Recommendation system
→ Anomaly detection: used
Raw Input dataset,
data Classify the data
thing Prediction
Machine learnin Why do we use Machine learning?
g
The subfield of artificial intelligence that
focuses on the development of algorithms that Machine learning is branch of artificial learning that has the
can learn from the data and improve their ability to learn from the past data without the need of
performance on a specific task without the need being explicitly programmed. Machine learning algorithms have
for being explicitly programmed. the abilility to make predictions/ decisions based on this
pass data. They are capable of understanding the patterns in the
Traditional Programming data which may/may not be visible to human mind
The following are several key reasons why we use machine
Data Traditional
Output
learning:-
Programming ① Automation & Efficiency:- ML automates repetitive task and
Program and processes large datasets quickly, enhancing overall efficiency.
Machine learning ② Data Driven Insights:- ML identify hidden patterns and provide valuable
insights, used for better decision-making.
Data Machine
learning program ③ Improved Accuracy & Precision:- Since ML models learn from past data
Output and make decisions accordingly meaning they improve over time,
leading to much accurate & reliable classification 4 predictions.
Thus we can conclude that while in traditional programming
it is require to provide data and program to get the ④ Personalization & Adaptability:- ML enables personalized experience, enhancing
output, Machine learning on the other hand is capable user engagement & has the ability to adapt new data, making it suitable
of learning from experience and make decisions/ predictions for dynamic 4 evolving environment.
based on the old data without the need of being
programmed explicitly.
Types of Machine learning ÷
→ Unsupervised Machine Learning ÷
→ Supervised Machine learning:-
① Supervised machine learning is sub branch of artificial ① Unsupervised machine learning is
intelligence that focuses on training model and make predictions based on unlabelled data , w
based on the labelled training data. patterns and relationships in
② It involves a learning process where the model learns from ② The goal of unsupervised learnin
known examples to predict and classify unknown, unseen or patterns, similarities or clusters
future instances accurately. used for various purposes such
③ supervised machine learning has 2 key components:- dimensity reduction.etc
→ Input data ③ Unsupervised machine learning
→ corresponding output labels. → Recommendation system
→ Anomaly detection: used
Raw Input dataset,
data Classify the data
thing Prediction
