These slides will help you to crack interviews for product-based companies if you are planning your career in Data Science, Artificial Intelligence, etc.
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
A quick introduction to linear and logistic regression using Python. Part of the Data Science Bootcamp held in Amman by the Jordan Open Source Association Dec/Jan 2015. Reference code can be found on Github https://github.com/jordanopensource/data-science-bootcamp/tree/master/MachineLearning/Session1
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
A quick introduction to linear and logistic regression using Python. Part of the Data Science Bootcamp held in Amman by the Jordan Open Source Association Dec/Jan 2015. Reference code can be found on Github https://github.com/jordanopensource/data-science-bootcamp/tree/master/MachineLearning/Session1
Introduction to Data Analytics starting with
OLS.
This is the first of a series of essays. I will share essays on unsupervised learning, dimensionality reduction and anomaly/outlier detection.
In this presentation, I explain the assignment problem:
basically, the assignment problem is a topic of operational research.
Here I cover the topic are:
What is an Assignment problem?
Rules for the Assignment problem.
How to solve the Assignment problem.
A New Approach of Right State Machine in Discrete Alphabets System.ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Introduction to Data Analytics starting with
OLS.
This is the first of a series of essays. I will share essays on unsupervised learning, dimensionality reduction and anomaly/outlier detection.
In this presentation, I explain the assignment problem:
basically, the assignment problem is a topic of operational research.
Here I cover the topic are:
What is an Assignment problem?
Rules for the Assignment problem.
How to solve the Assignment problem.
A New Approach of Right State Machine in Discrete Alphabets System.ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
linear regression is a linear approach for modelling a predictive relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables), which are measured without error. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. If the explanatory variables are measured with error then errors-in-variables models are required, also known as measurement error models.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.[4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
Linear regression has many practical uses. Most applications fall into one of the following two broad categories:
If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.
If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsDerek Kane
This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Interpretability in ML & Sparse Linear RegressionUnchitta Kan
The presentation, first given on January 8, 2019, introduces the concept of interpretability in machine learning, and why we might care about it. It also introduces an example of an interpretable, sparse model which is lasso regression.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
For more topics stay tuned with Learnbay.
This presentation educate you about how to create table using Python MySQL with example syntax and Creating a table in MySQL using python.
For more topics stay tuned with Learnbay.
This presentation educates you about Python MySQL - Create Database and Creating a database in MySQL using python with sample program.
For more topics stay tuned with Learnbay.
This presentation educates you about Python MySQL - Database Connection, Python MySQL - Database Connection, Establishing connection with MySQL using python with sample program.
For more topics stay tuned with Learnbay.
This presentation educates you about Python MySQL, Python Database, What is mysql-connector-python?, How do I Install mysql-connector-python? and Installing mysql-connector-python with sample program.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Issues and the types of issue, AI - Terminology with its list of frequently used terms in the domain of AI.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Working of ANNs, Machine Learning in ANNs, Back Propagation Algorithm, Bayesian Networks (BN), Building a Bayesian Network and Gather Relevant Information of Problem.
For more topics stay tuned with Learnbay.
This presentation educates you about AI- Neural Networks, Basic Structure of ANNs with a sample of ANN and Types of Artificial Neural Networks are Feedforward and Feedback.
For more topics stay tuned with Learnbay.
This presentation educates you about Artificial Intelligence - Robotics, What is Robotics?, Difference in Robot System and Other AI Program, Robot Locomotion, Components of a Robot and Applications of Robotics.
For more topics stay tuned with Learnbay.
This presentation educates you about Applications of Expert System, Expert System Technology, Development of Expert Systems: General Steps and Benefits of Expert Systems.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Components and Acquisition of Expert Systems and those are Knowledge Base, Knowledge Base and User Interface, AI - Expert Systems Limitation.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Expert Systems, Characteristics of Expert Systems, Capabilities of Expert Systems and Components of Expert Systems.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Popular Search Algorithms, Single Agent Pathfinding Problems, Search Terminology, Brute-Force Search Strategies, Breadth-First Search and Depth-First Search with example chart.
For more topics stay tuned with Learnbay.
This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
For more topics stay tuned with Learnbay.
This presentation educates you about Artificial Intelligence - Research Areas, Speech and Voice Recognition., Working of Speech and Voice Recognition Systems and Real Life Applications of Research Areas.
For more topics stay tuned with Learnbay.
This presentation educates you about Artificial intelligence composed and those are Reasoning, Learning, Problem Solving, Perception and Linguistic Intelligence.
For more topics stay tuned with Learnbay.
This presentation educates you about Artificial Intelligence - Intelligent Systems, Types of Intelligence, Linguistic intelligence, Musical intelligence, Logical-mathematical intelligence, Spatial intelligence, Bodily-Kinesthetic intelligence, Intra-personal intelligence and Interpersonal intelligence.
For more topics stay tuned with Learnbay.
This presentation educates you about Applications of Artificial Intelligence such as Intelligent Robots, Handwriting Recognition, Speech Recognition, Vision Systems and so more.
For more topics stay tuned with Learnbay.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
2. What is Linear Regression?
Linear regression is a linear approach to
modelling the relationship between a
scalar response (or dependent variable) and
one or more explanatory variables or
independent variables.
The case of one explanatory variable is
called simple linear regression, for more
than one explanatory variable, the process
is called multiple linear regression.
A linear regression line has an equation of
the form
Q
A
3. What are assumptions of Linear
Regression?
Short Trick: Assumptions can be
abbreviated as LINE in order to remember.
L : Linearity ( Relationship between x and y
is linear)
I : Independence (Observations are
independent of each other)
N : Normality (for any fix value of x, y is
normally distributed)
E : Equal Variance (homoscedasticity)
Q
A
4. What is Regularization? Explain
different types of Regularizations?
The L1 regularization (also called Lasso)
The L2 regularization (also called
Ridge)T
The L1/L2 regularization (also called
Elastic net)
Regularization is a technique which is used
to solve the overfitting problem of the
machine learning models.
The types of Regularization are as follows:
Q
A
5. How to choose the value of the
regularisation parameter (λ)?
Selecting the regularisation parameter is a
tricky business. If the value of λ is too high, it
will lead to extremely small values of the
regression coefficient β, which will lead to
the model underfitting (high bias – low
variance).
On the other hand, if the value of λ is 0 (very
small), the model will tend to overfit the
training data (low bias – high variance).
There is no proper way to select the value of
λ. What you can do is have a sub-sample of
data and run the algorithm multiple times
on different sets. Here, the person has to
decide how much variance can be tolerated.
Once the user is satisfied with the variance,
that value of λ can be chosen for the full
dataset.
Q
A
6. Explain gradient descent?
Gradient descent is an optimization
algorithm used to find the values of
parameters (coefficients) of a function (f)
that minimizes a cost function (cost).
When it is used: Gradient descent is best
used when the parameters cannot be
calculated analytically (e.g. using linear
algebra) and must be searched for by an
optimization algorithm.Details: The goal of
any Machine Learning Model to minimise
the cost function. To get the minima of the
cost function we use Gradient Descent
Algorithm.
Q
A