Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
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.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
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/
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
In machine learning, support vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
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.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
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/
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
In machine learning, support vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This talk is developed to address a refresher course at Yanam for one full day. I have introduced the audience to clustering, both hierarchical and non-hierarchical. Clustering methods such as K-Means, K-Mediods, etc all introduced with live demonstrations.
Lecture 11 - KNN and Clustering, a lecture in subject module Statistical & Ma...Maninda Edirisooriya
Supervised ML technique, K-Nearest Neighbor and Unsupervised Clustering techniques are learnt in this lesson. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
The method of identifying similar groups of data in a data set is called clustering. Entities in each group are comparatively more similar to entities of that group than those of the other groups.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
3. Introduction
• Unsupervised learning is a type of machine
learning algorithm used to draw inferences from datasets
consisting of input data without labeled responses. The
most common unsupervised learning method is cluster
analysis, which is used for exploratory data analysis to
find hidden patterns or grouping in data.
3
4. What is clustering?
• A group of objects that are similar to other objects in the
cluster, and dissimilar to data points in other clusters.
4
5. Use of clustering
Clustering has been widely used across industries for
years:
• Biology - for genetic and species grouping;
• Medical imaging - for distinguishing between different
kinds of tissues;
• Market research - for differentiating groups of customers
based on some attributes
• Recommender systems - giving you better Amazon
purchase suggestions or Netflix movie matches.
5
6. Clustering algorithms
• Partition-based clustering
• Relatively efficient
• E.g. k-means
• Hierarchical clustering
• Produces trees of clusters
• E.g. Agglomerative, Divisive
• Density-based clustering
• Produces arbitrary shaped clusters
• E.g. DBSCAN
6
7. K-means clustering
• k-means is a partitioning clustering
• K-means divides the data into non-overlapping subsets
(clusters) without any cluster-internal structure
• Examples within a cluster are very similar
• Examples across different clusters are very different
7
11. How does k-means clustering works?
1. Randomly place k centroids, one for each cluster
2. Calculate the distance of each point from each centroid
3. Assign each data point(object) to the closest centroid,
creating a cluster
4. Recalculate the position of the k centroids
5. Repeat the steps 2-4, until the centroids no longer
move
11
14. • K-means is partitioning algorithm relatively efficient for
medium and large sized databases
• Produces sphere-like clusters
• Needs number of clusters (k)
14
15. Hierarchical clustering
• Hierarchical clustering algorithms build a hierarchy of
clusters where each node is a cluster consists of the
clusters of its daughter nodes.
• Hierarchical clustering strategies
• Divisive (top down)
• Agglomerative (bottom up)
15
16. Agglomerative algorithm
1. Create n clusters, one for each data point
2. Compute the proximity matrix
3. Repeat
1. Merge the two closest clusters
2. Update the proximity matrix
4. Until only a single cluster remains
16
18. Distance between clusters
• Single-Linkage clustering
• Minimum distance between clusters
• Complete-Linkage Clustering
• Maximum distance between clusters
• Average linkage clustering
• Average distance between clusters
• Centroid linkage clustering
• Distance between cluster centroids
18
19. • Advantages
• Doesn’t required number of clusters to be specified
• Easy to implement
• Produces a dendrogram, which helps with understanding the data
19
20. • Disadvantages
• Can never undo any previous steps throughout the algorithm
• Generally has long runtimes
• Sometimes difficult to identify the number of clusters by the
dendrogram
20
21. Hierarchical clustering Vs. K-means
K-means Hierarchical Clustering
Much more efficient Can be slow for large datasets
Requires the number of clusters to be
specified
Does not require the number of
clusters to run
Gives only one partitioning of the data
based on the predefined number of
clusters
Gives more than one partitioning
depending on the resolution
Potentially returns different clusters
each time it is run due to random
initialization of centroids
Always generates the same clusters
21
22. DBSCAN clustering
• When applied to tasks with arbitrary shaped clusters or
clusters within clusters, traditional techniques might not
be able to achieve good results
• Partitioning based algorithms has no notion of outliers that
is, all points are assigned to a cluster even if they do not
belong in any
• In contrast, density-based clustering locates regions
of high density that are separated from one another by
regions of low density. Density in this context is defined as
the number of points within a specified radius.
22
25. What is DBSCAN?
• DBSCAN (Density-Based Spatial Clustering of
Applications with Noise)
• Is one of the most common clustering algorithms
• Works based on density of objects
• R (Radius of neighborhood)
• Radius (R) that if includes enough number
of points within, we call it a dense area
• M (Min number of neighbors)
• The minimum number of data points
we want in a neighborhood to define a cluster
25