Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
What am I going to get from this course?
Provides a basic conceptual understanding of how clustering works
Provides intuitive understanding of the mathematics behind various clustering algorithms
Walk through Python code examples on how to use various cluster algorithms
Show how clustering is applied in various industry applications
Check it on Experfy: https://www.experfy.com/training/courses/unsupervised-learning-clustering
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
What am I going to get from this course?
Provides a basic conceptual understanding of how clustering works
Provides intuitive understanding of the mathematics behind various clustering algorithms
Walk through Python code examples on how to use various cluster algorithms
Show how clustering is applied in various industry applications
Check it on Experfy: https://www.experfy.com/training/courses/unsupervised-learning-clustering
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
Machine Learning techniques used in Artificial Intelligence- Supervised, Unsupervised, Reinforcement Learning. It discusses about Linear Regression, Logistic Regression, SVM, Random forest, KNN, K-Means Clustering and Apriori Algorithm. It also Illustrates the applications of AI in various fields.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
محاضرة ألقيت بتنظيم من مجموعة برمج @parmg_sa
https://www.meetup.com/parmg_sa/events/238339639/
في الرياض، مقر حاضنة بادر. بتاريخ 20 جمادى الآخر 1438هـ، الموافق 18 مارس 2017
By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Similar to Machine learning and linear regression programming (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Agenda
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
3. • Arthur Samuel (1959)
Machine Learning: Field of study that gives computers the
ability to learn without being explicitly programmed.
• Tom Mitchell (1998)
A computer program is said to learn from experience E with
respect to some task T and some performance measure P, if its
performance on T, as measured by P, improves with experience E.
Machine Learning Definition
6. Implies huge data
volumes that cannot be
processed effectively with
traditional applications.
Big Data processing
begins with raw data that
is not aggregated and it is
often impossible to store
such data in the memory
of a single computer
Is about using Statistics
as well as other
programming methods to
find patterns hidden in
the data so that you can
explain some
phenomenon. Machine
Learning uses Data
Mining techniques and
other learning algorithms
to build models of what is
happening behind
some data.
Big Data Data Mining
Is an artificial
intelligence technique
that is broadly used
in Data Mining. ML uses
a training dataset to build
a model that can predict
values of target variables.
Data Mining uses the
predictive force of
Machine Learning by
applying various ML
algorithms on Big data.
Machine Learning
7. WHAT IS ARTIFICIAL INTELLIGENCE
• Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent
machines that work and react like humans. Some of the activities computers with artificial intelligence
are designed for include:
Knowledge
Gain
Reasoning
Problem
Solving
Learning
9. Types of Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Target/outcome
variable to be
predicted from set of
predictors is known
at training phase.
E.g. Regression,
Decision Tree,
Random Forest, KNN
Target/outcome
variable to be
predicted from set of
predictors is
unknown at training
phase.
E.g. Clustering (K-
means, Apriori)
Machine is trained to
take specific decision
Exposed to an
environment where it
trains itself
continually using trial
and error.
E.g. Markov Decision
process
10. Applications in real world
• Google search engine
• Self driving cars
• Facebook auto tagging
• Netflix movie recommendation
• Amazon product recommendation
• Healthcare diagnosis
• Speech recognition
• StackOverflow QA tagging
• Chatbot
11. Data as input
(Text files,
spreadsheet,
SQL database)
Feature Engineering
(Removing unwanted data,
Handle missing values,
Normalization or
Standardization)
Algorithm
Output/
Model
Pipeline solving ML Problem
13. Data Exploration/Feature Engineering
1. Variable Identification
• Predictor(s) n Target
• Type n Category of variable
2. Univariate Analysis
• Central tendency
• Measure of Dispersion
• Visualization Method
• Frequency table(categorical)
3. Bivariate Analysis
• Relation between 2 variables
• Correlation
• Chi-square test
• Z-test
4. Missing Value
Treatment
• Deletion
• Imputation
• Prediction Model
• KNN Imputation
5. Outlier Handling
Detection
• Very Important to handle outlier
• Visualization technique like box-
plot, scatter plot, Histogram
• Any value beyond -1.5IQR to
1.5IQR is an outlier
Treatment
• Remove
• Scale or Normalize
• Transform
• Impute
14. SUPERVISED LEARNING
• Supervised learning is used whenever we want to predict a certain outcome from
a given input, and we have examples of input/output pairs.
• We build a machine learning model from these input/output pairs, which
comprise our training set.
• Our goal is to make accurate predictions for new, never-before-seen data.
• Supervised learning often requires human effort to build the training set, but
afterward automates and often speeds up an otherwise laborious or infeasible
task.
15. TYPES OF SUPERVISED MODEL
• Regression :
• regression is the process of predicting a continuous value
• Classification
• predict a class label, which is a choice from a predefined list of possibilities.
16. CLASSIFICATION
• Binary Classification : Distinguishing between exactly two classes
• Multiclass classification : Classification between more than two classes.
17. Types of regression
1. Simple Linear Regression
Single predictor + single target
y = m*x + c
2. Multiple Linear Regression
Multiple predictors + single target
y = m1*x1 + m2*x2 + c
3. Polynomial Regression
One or many predictors + single target
Y = mn * x^n + … + m2*x^2 + m1*x1 + c
4. Stepwise Regression
Useful in case of multiple predictors
Add or Remove predictors as needed
Forward selection
Backward elimination
5. Lasso Regression
6. Ridge Regression
7. ElasticNet Regression
18. Simple Linear Regression
• Single predictor and single target
• Y = b0 + b1*X
• Minimum sum squared error
• Standard packages are already available
• Formula
• Programming example
19. Classification
Type of supervised learning
Output or target is a categorical outcome
Example
Mail spam or no spam
Weather rainy, sunny, humid
Stock price up or down
Predictor(s) Algorithm
Categorical
Target
20. Types of Classification
1. K-nearest Neighbor Classifier
2. Logistic Regression
3. Naïve Bayes 6. Support Vector Machine
Classifier
5. Random Forest Classifier
4. Decision Tree Classifier
22. Unsupervised learning
• Unsupervised learning is the training of machine using
information that is neither classified nor labelled
For instance, Given an image having both dogs and cats which have not seen ever.
Machine tries to find pattern
based on shape of head,
ears, body structure etc.
23. Reinforcement Learning
• Reinforcement learning (RL) is an area of machine learning concerned with
how software agents ought to take actions in an environment so as to maximize some
notion of cumulative reward. (source : Wikipedia)
Eg : you go near fire , its warm : positive reinforcement
you touch fire, it burns your hand : negative reinforcement learn not to touch
fire
• Algorithms for RL include – MonteCarlo methods, Markov Decision Processes, Q-
learning etc
24. ML in Python:
• Numpy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
Non-
Programming:
• Weka
• Orange
• RapidMiner
• Qlik Sense
• xls
Deep Learning:
• Tensorflow
• Keras
• PyTorch
• Theano
Tools And Packages
26. LINEAR REGRESSION
• Linear regression, or ordinary least squares (OLS), is the simplest and most classic
linear method for regression. Linear regression finds the parameters m and b that
minimize the mean squared error between predictions and the true regression
targets, y, on the training set.
28. HOME PRICES
area price
2600 550000
3000 565000
3200 610000
3600 680000
4000 725000
Given these home prices, find
out the price of homes whose
area is
3300 square feet
5000 square feet
36. EVALUATING MODEL PERFORMANCE
• The performance of a regression model can be understood by knowing the error
rate of the predictions made by the model. You can also measure the performance
by knowing how well your regression line fit the dataset.
• Let’s try to understand how to measure the performance of regression models.
• A good regression model is one where the difference between the actual or
observed values and predicted values for the selected model is small and unbiased
for train, validation and test data sets.
37. EVALUATING MODEL PERFORMANCE
• To measure the performance of your regression model, some statistical metrics are used. They
are-
• Mean Absolute Error(MAE)
• Root Mean Square Error(RMSE)
• Coefficient of determination or R2
• Adjusted R2
38. MEAN ABSOLUTE ERROR(MAE)
• This is the simplest of all the metrics. It is measured by taking the average of the absolute
difference between actual values and the predictions.
40. ROOT MEAN SQUARE ERROR(RMSE)
• The Root Mean Square Error is measured
by taking the square root of the average
of the squared difference between the
prediction and the actual value.
• It represents the sample standard
deviation of the differences between
predicted values and observed
values(also called residuals). It is
calculated using the following formula:
42. COEFFICIENT OF DETERMINATION OR R^2
• It measures how well the actual
outcomes are replicated by the
regression line.
• It helps you to understand how well the
independent variable adjusted with the
variance in your model.
• That means how good is your model
for a dataset.
• The mathematical representation for
R^2 is
Here, SSR = Sum Square of
Residuals(the squared difference
between the predicted and the
average value)
SST = Sum Square of Total(the
squared difference between the
actual and average value)
43. COEFFICIENT OF DETERMINATION OR R^2 (CONT.)
• Here the green line represents the regression line
and the red line represents the average line. The
differences in data points from these lines are
taken in the equation.
• Usually, the value of R^2 lies between 0 to 1(it
can be negative if the regression line somehow
has a worse fit than the average!). The closer its
value to one, the better your model is. This is
because either your regression line has well fitted
the dataset or the data points are distributed with
low variance. Which lessens the value of the Sum
of Residuals. Hence, the equation gets closer to
one.
list of possibilities. classification approach can be thought of as a means of categorizing or "classifying" some unknown items into a discrete set of "classes."
plt.scatter(df['area'],df['price'] , marker = '*', color = 'red')