‘Machine Learning’
What is machine learning?
Machine learning (ML) is a type of
artificial intelligence (AI) that allows
software applications to become more accurate at predicting outcomes without
being explicitly programmed to do so. Machine learning algorithms use
historical data as input to predict new output values.
Type.
1. Supervised learning
2. Unsupervised
learning
3. Reinforcement
learning
Machine
learning lifecycle
Machine learning has given the computer systems the abilities
to automatically learn without being explicitly programmed. But how does a
machine learning system work? So, it can be described using the life cycle of
machine learning. Machine learning life cycle is a cyclic process to build an
efficient machine learning project.
- Gathering
Data
- Data
preparation
- Data
Wrangling
- Analyse
Data
- Train
the model
- Test
the model
- Deployment
Exploratory Data Analysis (EDA) is
an approach to analyze the data using visual techniques. It is used to discover
trends, patterns, or to check assumptions with the help of statistical summary
and graphical representations.
What
is Data Preparation
Data preparation is the process of preparing raw data so that it
is suitable for further processing and analysis. Key steps include collecting,
cleaning, and labeling raw data into a form suitable for machine learning (ML)
algorithms and then exploring and visualizing the data.
Model Building :
In this phase data science team needs to develop data sets for training,
testing, and production purposes. These data sets enable data scientist to
develop analytical method and train it, while holding aside some of data for
testing the model.
What is Model Evaluation
Model evaluation is the process of using different evaluation
metrics to understand a machine learning model’s performance, as well as its
strengths and weaknesses. Model evaluation is important to assess the efficacy
of a model during initial research phases, and it also plays a role in model
monitoring.
Machine Learning Techniques
Machine learning is a data analytics technique that teaches
computers to do what comes naturally to humans and animals: learn from
experience. Machine learning algorithms use computational methods to directly
"learn"
from data without relying on a predetermined equation as a model.
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