This document provides an overview of key concepts in data science including machine learning, deep learning, artificial intelligence, and how they relate. It defines machine learning as using algorithms to learn from data without being explicitly programmed. Deep learning is a subset of machine learning using artificial neural networks. Artificial intelligence is the broader field of machines performing intelligent tasks. The document also discusses supervised, unsupervised, and reinforcement machine learning algorithms and how data science uses skills from statistics, machine learning, and visualization to analyze and manipulate large datasets.

1. DATA SCIENCE

2. INDEX
 Machine Learning
 Types of Machine Learning
 Deep Learning
 Artificial Intelligence
 R-Language
 How does Data science relate to AL,ML & DL

3. MACHINE LEARNING
 Machine learning works by finding a function, or a
relationship, from input X to output Y. The high level
and most commonly accepted.
 machine learning is the ability for computers to learn
and act without being explicitly programmed.
 Machine learning is an application of artificial
intelligence (AI) that provides systems the ability to
automatically learn and improve from experience
without being explicitly programmed.

4. Types of Machine Learning
 Supervised Machine Learning Algorithms To make predictions we
use this machine learning algorithm. Further, this algorithm
searches for patterns within the value labels. That was assigned to
data points.
 Unsupervised Machine Learning Algorithms No labels are
associated with data points. Also, these Machine Learning
algorithms organize the data into a group of clusters. Moreover, it
needs to describe its structure. Also, to make complex data look
simple and organized for analysis.
 Reinforcement Machine Learning Algorithms We use these
algorithms to choose an action. Also, we can see that it is based on
each data point. Moreover, after some time the algorithm changes
its strategy to learn better. Also, achieve the best reward.Machine
learning focuses on the development of computer programs that can
access data and use it learn for themselves.

5. DEEP LEARNING
 Deep Learning is a subfield of machine learning
concerned with algorithms inspired by the
structure and function of the brain called
artificial neural networks.
 Machine learning (ML) is a category of algorithm
that allows software applications to become more
accurate in predicting outcomes without being
explicitly programmed.

6.  The basic premise of machine learning is to build
algorithms that can receive input data and
use statistical analysis to predict an output while
updating outputs as new data becomes available.
 The processes involved in machine learning are
similar to that of data mining and predictive
modeling.

7.  Machine learning focuses only on solving real-world
problems. Also, it takes a few ideas of artificial intelligence.
Moreover, machine learning does through the neural
networks. That are designed to mimic human decision-
making capabilities.
 Machine Learning tools and techniques are the two key
narrow subsets. That only focuses more on deep learning.
Furthermore, we need to apply it to solve any problem. That
requires thought- human or artificial.
 Any Deep neural network will consist of three types of layers:
 The Input Layer
 The Hidden Layer
 The Output Layer

8. Artificial intelligence
 Artificial Intelligence is the broader concept of
machines being able to carry out tasks in a way that
we would consider “smart”.
 Machine Learning is a current application of AI
based around the idea that we should really just be
able to give machines access to data and let them
learn for themselves.

9.  Artificial intelligence refers to the simulation of a human
brain function by machines. This is achieved by creating
an artificial neural network that can show human
intelligence. The primary human functions that an AI
machine performs include logical reasoning, learning
and self-correction.
 Artificial intelligence is a wide field with many
applications but it also one of the most complicated
technology to work on. Machines inherently are not
smart and to make them so, we need a lot of computing
power and data to empower them to simulate human
thinking.

10. R LANGUAGE
 R Data science includes data analysis. It is an
important component of the skill set required for
many jobs in this area. But it’s not the only necessary
skill. They play active roles in the design and
implementation work of four related areas:
 Data architecture
 In data acquisition
 Data analysis
 In data archiving

11. Data science relate to AL,ML&DL
 Data science is an interdisciplinary field that has
skills used in various fields such as statistics,
Machine Learning, visualization, etc. It is a general
process and method that analyze and manipulate
data.
 Also, enables to find meaning and appropriate
information from large volumes of data. This makes
it possible for us to use data for making key decisions
in business, science, technology, and even politics.

12. W W W . S T E R L I N G I T T R A I N I N G S . C O M
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