MODULE 01

Overview of Artificial Intelligence and Machine Learning

What is Artificial Intelligence?

Artificial Intelligence, or AI, is the result of our efforts to automate tasks normally
performed by humans, such as image pattern recognition, document classification,
or a computerized chess rival.

https://www.ibm.com/topics/artificial-intelligence

What is Machine Learning?

Machine Learning, or ML, focuses on the creation of systems or models that can
learn from data and improve their performance in specific tasks, without the need to
be explicitly programmed, making them learn from past experiences or examples to
make decisions on new data.
Machine Learning is a branch of artificial intelligence that develops algorithms by
learning the hidden patterns of the datasets used it to make predictions on new
similar type data, without being explicitly programmed for each task.

https://www.javatpoint.com/machine-learning

Machine learning is used in many different applications, from image and speech
recognition to natural language processing, recommendation systems, fraud
detection, portfolio optimization, automated task, and so on. Machine learning
models are also used to power autonomous vehicles, drones, and robots, making
them more intelligent and adaptable to changing environments.

Model: A model is the representation that explains the observations. The trained model is
the result of applying an ML algorithm with a data set. This trained model, now primed with
specific patterns and understandings from the dataset, is subsequently used to draw
inferences from new observations
Algorithm: An algorithm is a procedure implemented in code that guides a model in learning
from data it's given. There are many machine learning algorithms.
Training: Training is the iterative process of applying the learning algorithm. Consists in:
* Applying the model (as is) to the variables of the observations and obtain the results
according to the model.
* Comparing the model results with the actual values.
* Establishing a way to calculate the error between the model and reality.
* Using the error as a basis to update the model in order to reduce the error.
* Repeating until the model reaches the error levels that we have proposed and is capable of
generalizing with observations that it has not seen in training.
Testing: Measuring the performance of a data model using test data that it hasn't been
trained on.
Deployment: Integration of the model into a production environment.
Dataset: The dataset is the raw material an ML model uses and interacts with. It can consist
of images, text, numerical values, and anything else that can be put together as relevant
data.

Types of Machine Learning

There are several types of machine learning, each with special characteristics and
applications. Some of the main types of machine learning algorithms are as follows:
 Supervised Machine Learning
In supervised learning, sample labelled data are provided to the machine learning
system for training, and the system then predicts the output based on the training
data. The system uses labelled data to build a model that understands the datasets
and learns about each one. After the training and processing are done, we test the
model with sample data to see if it can accurately predict the output. The mapping of
the input data to the output data is the objective of supervised learning. The
managed learning depends on oversight, and it is equivalent to when an understudy
learns things in the management of the educator. Spam filtering is an example of
supervised learning.
Supervised learning can be grouped further in two categories of algorithms:
Classification
Regression

 Unsupervised Machine Learning

Unsupervised learning is a learning method in which a machine learns without any
supervision. The training is provided to the machine with the set of data that has not
been labelled, classified, or categorized, and the algorithm needs to act on that data
without any supervision. The goal of unsupervised learning is to restructure the input
data into new features or a group of objects with similar patterns.
In unsupervised learning, we don't have a predetermined result. The machine tries to
find useful insights from the huge amount of data. It can be further classifieds into
two categories of algorithms:
Clustering
Association

 Reinforcement Learning
Reinforcement learning is a feedback-based learning method, in which a learning
agent gets a reward for each right action and gets a penalty for each wrong action. The
agent learns automatically with these feedbacks and improves its performance. In
reinforcement learning, the agent interacts with the environment and explores it. The
goal of an agent is to get the most reward points, and hence, it improves its
performance.
 The robotic dog, which automatically learns the movement of his arms, is an example
of Reinforcement learning.
https://www.geeksforgeeks.org/types-of-machine-learning/?ref=lbp

History of Machine Learning

Before some years (about 40-50 years), machine learning was science fiction, but
today it is the part of our daily life. Machine learning is making our day to day life
easy from self-driving cars to Amazon virtual assistant "Alexa". However, the
idea behind machine learning is so old and has a long history. Below some
milestones are given which have occurred in the history of machine learning:

The early history of Machine Learning (Pre-1940):

o 1834: In 1834, Charles Babbage, the father of the computer, conceived a

device that could be programmed with punch cards. However, the machine
was never built, but all modern computers rely on its logical structure.
o 1936: In 1936, Alan Turing gave a theory that how a machine can determine
and execute a set of instructions.

The era of stored program computers:

o 1940: In 1940, the first manually operated computer, "ENIAC" was invented,
which was the first electronic general-purpose computer. After that stored
program computer such as EDSAC in 1949 and EDVAC in 1951 were
invented.
o 1943: In 1943, a human neural network was modeled with an electrical circuit.
In 1950, the scientists started applying their idea to work and analyzed how
human neurons might work.

Computer machinery and intelligence:

o 1950: In 1950, Alan Turing published a seminal paper, "Computer Machinery

and Intelligence," on the topic of artificial intelligence. In his paper, he
asked, "Can machines think?"

Machine intelligence in Games:

 o 1952: Arthur Samuel, who was the pioneer of machine learning, created a
program that helped an IBM computer to play a checkers game. It performed
better more it played.
o 1959: In 1959, the term "Machine Learning" was first coined by Arthur
Samuel.

The first "AI" winter:

o The duration of 1974 to 1980 was the tough time for AI and ML researchers,
and this duration was called as AI winter.
o In this duration, failure of machine translation occurred, and people had
reduced their interest from AI, which led to reduced funding by the
government to the researches.

Machine Learning from theory to reality

o 1959: In 1959, the first neural network was applied to a real-world problem to
remove echoes over phone lines using an adaptive filter.
o 1985: In 1985, Terry Sejnowski and Charles Rosenberg invented a neural
network NETtalk, which was able to teach itself how to correctly pronounce
20,000 words in one week.
o 1997: The IBM's Deep blue intelligent computer won the chess game against
the chess expert Garry Kasparov, and it became the first computer which had
beaten a human chess expert.

Machine Learning at 21st century

2006:

o Geoffrey Hinton and his group presented the idea of profound getting the
hang of utilizing profound conviction organizations.
o The Elastic Compute Cloud (EC2) was launched by Amazon to provide
scalable computing resources that made it easier to create and implement
machine learning models.

2007:

o Participants were tasked with increasing the accuracy of Netflix's

recommendation algorithm when the Netflix Prize competition began.
 o Support learning made critical progress when a group of specialists utilized it
to prepare a PC to play backgammon at a top-notch level.

2008:

o Google delivered the Google Forecast Programming interface, a cloud-based

help that permitted designers to integrate AI into their applications.
o Confined Boltzmann Machines (RBMs), a kind of generative brain
organization, acquired consideration for their capacity to demonstrate
complex information conveyances.

2009:

o Profound learning gained ground as analysts showed its viability in different

errands, including discourse acknowledgment and picture grouping.
o The expression "Large Information" acquired ubiquity, featuring the difficulties
and open doors related with taking care of huge datasets.

2010:

o The ImageNet Huge Scope Visual Acknowledgment Challenge (ILSVRC) was

presented, driving progressions in PC vision, and prompting the advancement
of profound convolutional brain organizations (CNNs).

2011:

o On Jeopardy! IBM's Watson defeated human champions., demonstrating the

potential of question-answering systems and natural language processing.

2012:

o AlexNet, a profound CNN created by Alex Krizhevsky, won the ILSVRC,

fundamentally further developing picture order precision and laying out
profound advancing as a predominant methodology in PC vision.
o Google's Cerebrum project, drove by Andrew Ng and Jeff Dignitary, utilized
profound figuring out how to prepare a brain organization to perceive felines
from unlabeled YouTube recordings.

2013:
 o Ian Goodfellow introduced generative adversarial networks (GANs), which
made it possible to create realistic synthetic data.
o Google later acquired the startup DeepMind Technologies, which focused on
deep learning and artificial intelligence.

2014:

o Facebook presented the DeepFace framework, which accomplished close

human precision in facial acknowledgment.
o AlphaGo, a program created by DeepMind at Google, defeated a world
champion Go player and demonstrated the potential of reinforcement learning
in challenging games.

2015:

o Microsoft delivered the Mental Toolbox (previously known as CNTK), an open-

source profound learning library.
o The performance of sequence-to-sequence models in tasks like machine
translation was enhanced by the introduction of the idea of attention
mechanisms.

2016:

o The goal of explainable AI, which focuses on making machine learning

models easier to understand, received some attention.
o Google's DeepMind created AlphaGo Zero, which accomplished godlike Go
abilities to play without human information, utilizing just support learning.

2017:

o Move learning acquired noticeable quality, permitting pretrained models to be

utilized for different errands with restricted information.
o Better synthesis and generation of complex data were made possible by the
introduction of generative models like variational autoencoders (VAEs) and
Wasserstein GANs.
o These are only a portion of the eminent headways and achievements in AI
during the predefined period. The field kept on advancing quickly past 2017,
with new leap forwards, strategies, and applications arising.
Machine Learning at present:
The field of machine learning has made significant strides in recent years, and its
applications are numerous, including self-driving cars, Amazon Alexa, Catboats, and
the recommender system. It incorporates clustering, classification, decision tree,
SVM algorithms, and reinforcement learning, as well as unsupervised and
supervised learning.

Present day AI models can be utilized for making different expectations, including
climate expectation, sickness forecast, financial exchange examination, and so on.
Machine Learning Workflow

Machine learning workflow refers to the series of stages or steps involved in the process of
building a successful machine learning system.

The various stages involved in the machine learning workflow are-

1. Data Collection
2. Data Preparation
3. Choosing Learning Algorithm
4. Training Model
5. Evaluating Model
 6. Predictions

Let us discuss each stage one by one.

1. Data Collection-

In this stage,

 Data is collected from different sources.

 The type of data collected depends upon the type of desired project.
 Data may be collected from various sources such as files, databases etc.
 The quality and quantity of gathered data directly affects the accuracy of the desired
system.

2. Data Preparation-

In this stage,

 Data preparation is done to clean the raw data.

 Data collected from the real world is transformed to a clean dataset.
 Raw data may contain missing values, inconsistent values, duplicate instances etc.
 So, raw data cannot be directly used for building a model.

Different methods of cleaning the dataset are-

 Ignoring the missing values

 Removing instances having missing values from the dataset.
 Estimating the missing values of instances using mean, median or mode.
 Removing duplicate instances from the dataset.
 Normalizing the data in the dataset.

This is the most time consuming stage in machine learning workflow.

3. Choosing Learning Algorithm-

In this stage,

 The best performing learning algorithm is researched.

 It depends upon the type of problem that needs to solved and the type of data we have.
 If the problem is to classify and the data is labeled, classification algorithms are used.
 If the problem is to perform a regression task and the data is labeled, regression
algorithms are used.
 If the problem is to create clusters and the data is unlabeled, clustering algorithms are
used.
The following chart provides the overview of learning algorithms-

4. Training Model-

In this stage,

 The model is trained to improve its ability.

 The dataset is divided into training dataset and testing dataset.
 The training and testing split is order of 80/20 or 70/30.
 It also depends upon the size of the dataset.
 Training dataset is used for training purpose.
 Testing dataset is used for the testing purpose.
 Training dataset is fed to the learning algorithm.
 The learning algorithm finds a mapping between the input and the output and generates
the model.
5. Evaluating Model-

In this stage,

 The model is evaluated to test if the model is any good.

 The model is evaluated using the kept-aside testing dataset.
 It allows to test the model against data that has never been used before for training.
 Metrics such as accuracy, precision, recall etc are used to test the performance.
 If the model does not perform well, the model is re-built using different hyper parameters.
 The accuracy may be further improved by tuning the hyper parameters.

6. Predictions-

In this stage,

 The built system is finally used to do something useful in the real world.
 Here, the true value of machine learning is realized.

Process Data Preparation & Preprocessing

Introduction to Data Preparation and Preprocessing
Deep learning and Machine learning are becoming more and more important in today’s ERP
(Enterprise Resource Planning). During the process of building the analytical model using
Deep Learning or Machine Learning the data set is collected from various sources such as a
file, database, sensors, and much more.

But, the collected data cannot be used directly for performing the analysis process. Therefore,
to solve this problem Data Preparation is done. It includes two techniques that are listed
below -

Data Preparation Architecture

Data Preparation process is an important part of Data Science. It includes two concepts such
as Data Cleaning and Feature Engineering. These two are compulsory for achieving better
accuracy and performance in the Machine Learning and Deep Learning projects.

What is Data Preprocessing?

Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In
other words, whenever the data is gathered from different sources it is collected in raw format
which is not feasible for the analysis.

Therefore, certain steps are executed to convert the data into a small clean data set. This
technique is performed before the execution of the Iterative Analysis. The set of steps is
known as Data Preprocessing. It includes -

Need of Data Preparation Process and Preprocessing

For achieving better results from the applied model in Machine Learning and Deep Learning
projects the format of the data has to be in a proper manner, this is where term Data
Preparation is used. Some specified Machine Learning and Deep Learning model need
information in a specified format, for example, Random Forest algorithm does not support
null values, therefore to execute random forest algorithm null values has to be managed from
the original raw data set.

Another aspect of Data Preparation and analysis is that the data set should be formatted in
such a way that more than one Machine Learning and Deep Learning algorithms are executed
in one data set, and the best out of them is chosen.
How is Data Preprocessing performed?
Data Preprocessing is carried out to remove the cause of
unformatted real-world data which we discussed above.

First of all, let’s explain how missing data can be handled during
Data Preparation. Three different steps can be executed which are
given below -

Let’s move further and discuss how we can deal with noisy data. The
methods that can be followed for better Data Preparation and
analysis are given below -

In this Data Preparation process sorting of data is performed

concerning the values of the neighborhood. This method is also
known as local smoothing.

In the approach, the outliers may be detected by grouping similar

data in the same group, i.e., in the same cluster.

A Machine Learning algorithm can be executed for the smoothing of

data during Data Preprocessing
. For example, Regression Algorithm can be used for the
smoothing of data using a specified linear function.

The noisy data can be deleted manually by the human being, but it is
a time-consuming Data Preparation process, so mostly this method
is not given priority.
To deal with the inconsistent data manually and perform Data
Preparation and analysis properly, the data is managed using
external references and knowledge engineering tools like the
knowledge engineering process.

https://www.javatpoint.com/data-preparation-in-machine-learning

Exploratory Data Analysis ( EDA)

https://verpex.com/blog/website-tips/eda-in-machine-learning
Exploratory Data Analysis(EDA) is the main step in the process of various
data analysis. It helps data to visualize the patterns, characteristics, and
relationships between variables. Python provides various libraries used
for EDA such as NumPy, Pandas, Matplotlib, Seaborn, and Plotly.

INTRODUCTION TO PYTHON & LIBRARIES ( NUMPY , PANDAS &

MATPLOTLIB)
https://www.kaggle.com/code/chats351/introduction-to-numpy-pandas-and-matplotlib

https://www.w3schools.in/matplotlib/tutorials/plot-types