This document provides an introduction to machine learning concepts including supervised and unsupervised learning, regression, classification, features, weights and bias, and linear regression. It defines machine learning as computers learning without being explicitly programmed and discusses common machine learning applications. Key machine learning types are outlined including supervised learning using labeled data for predictions, unsupervised learning with unlabeled data, deep learning using neural networks, and reinforcement learning using rewards. Regression is described as determining relationships among variables to predict quantities, using housing price prediction as an example. Linear regression for fitting a linear model to data is covered in more detail, discussing loss functions, gradient descent, and using Python code examples.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
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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
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
Feature Importance Analysis with XGBoost in Tax auditMichael BENESTY
Presentation of a real use case at TAJ law firm (Deloitte Paris) of applying Machine learning on accounting to help clients to prepare their tax audit.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
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
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
Feature Importance Analysis with XGBoost in Tax auditMichael BENESTY
Presentation of a real use case at TAJ law firm (Deloitte Paris) of applying Machine learning on accounting to help clients to prepare their tax audit.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
What is machine learning? Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Top 40 Data Science Interview Questions and Answers 2022.pdfSuraj Kumar
1 – What is F1 score?
F1 score is a measure of the accuracy of a model. It is defined as the harmonic mean of precision and recall.
F1 score is one of the most popular metrics for assessing how well a machine learning algorithm performs on predicting a target variable. F1 score ranges from 0 to 1, with higher values indicating better performance.
The F1 score is used to evaluate the performance of a machine learning algorithm by considering how many times it has classified correctly and how many times it has misclassified.
The higher the F1 score, the better the performance of an algorithm.
2 – What is pickling and unpickling?
Pickling is the process of converting an object into a string representation. It can be used to store the object in a file, send it over a network, or save it to disk.
Unpickling is the inverse process of pickling. It converts an object from its string representation back into an object.
Pickling and unpickling can be done with machine learning by using an algorithm that converts the input to the output.
3 – Difference between likelihood and probability?
Probability is a measure of the likelihood of an event happening under certain conditions. The event can be a machine learning algorithm predicting the probability that a person will buy a product or not.
Likelihood is the probability that an event will happen, based on evidence and knowledge about the world. For example, if you see someone who looks like they are going to rob you and you know that they have robbed other people in the past, your likelihood of being robbed is high.
4 – Which machine learning algorithm known as a lazy learner?
KNN is a machine learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesn’t learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorizes the training dataset instead.
5 – How to fix multicollinearity?
Multicollinearity is a statistical problem that arises when two or more independent variables are highly correlated.
One way to fix multicollinearity is to use a different variable that has less correlation with the other variables. If there are not any other variables available, one can use a transformation on the original variable and then re-run the regression.
6 – Significance of gamma and Regularization in SVM?
The significance of gamma and regularization in SVM is that they are used to control the trade-off between the training error and the generalization error. In other words, these two parameters are used to balance the bias-variance trade-off.
Regularization is a technique to reduce overfitting by penalizing models with more complexity than necessary. The goal of regularization is to find a model that has good generalization performance, which means it can correctly predict new data points with high accuracy. On the other hand, gamma is a parameter that controls how much weight should be given to each training ex
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets.
Arrive early, dress formally and be confident, advices T Muralidharan.
Research the company beforehand.
Arrive early at the venue.
Be neatly groomed and dress formally.
Be confident.
Maintain a composed body posture.
Answer to the point.
Say, 'I don't know', if you don't know the answer.
Personality development refers to how the organized patterns of behavior that make up each person's unique personality emerge over time. Many factors go into influencing personality, including genetics, environment, parenting, and societal variables.
Understand the job requirements. ...
Develop job-related interview questions. ...
Establish a system to evaluate candidates. ...
Ensure a comfortable interviewing environment. ...
Help the candidate relax. ...
Avoid unlawful or discriminatory questions. ...
Document the interview. ...
Allow the candidate to ask questions
classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. ... An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.
Regression takes a group of random variables, thought to be predicting Y, and tries to find a mathematical relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all the individual data points.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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
6. What is Machine Learning ?
Machine learning is a specific field of AI where a system learns to find
patterns in examples in order to make predictions.
It can be understood as Computers learning how to do a task without
'being explicitly programmed' to do so.
7. What is Machine Learning ?
Machine Learning Algorithms are those that can tell you something
interesting about the data (patterns !), without you having to write any
custom code specific to the problem.
Instead of writing code explicitly, we feed data to these ML algorithms and
they build their own logic based on the data and its patterns.
8. What is Machine Learning ?
Hence, ML is the “Art of Seeking Information and Meaning from Data”
18. Types of Machine Learning System
Machine
Learning
Supervised
Machine
Learning
Unsupervised
Machine
Learning
Deep Learning
Reinforcement
Learning
19. Types of Machine Learning System
Unsupervised
Unsupervised learning is when we are dealing with data that has not been labeled or categorized.
Supervised
Supervised learning algorithm takes labeled data and creates a model that can
make predictions given new data.
Deep Learning
Deep learning utilizes neural networks which, just like the human brain, contain interconnected
neurons that can be activated or deactivated.
Reinforcement
Reinforcement learning uses a reward system and trial-and-error in order to maximize the long-
term reward.
22. Classification vs. Regression !
CLASSIFICATION: In a classification problem, there might be test data consisting of
photos of animals, each one labeled with its corresponding name. The model would
be trained on this test data and then the model would be used to classify unlabeled
animal photos with the correct name.
REGRESSION: In a regression problem, there is a relationship trying to be
determined among many different variables. Usually, this takes place in the form of
historical data being used to predict future quantities. An example of this would be
predicting the future price of a stock based on past prices movements.
23. What are Features ?
Features are the variables which distinguish one example from another. They tell
the machine learning model what parts of the data to look for patterns for
achieving the goal.
Lots of data is crucial to a machine learning system but it needs to be helpful
and relevant data. Though you never know until you experiment to see what
variables truly make an impact.
24. An Example
Consider the problem, "Predicting the Price of a House"
What features should we use ?
25. Features :
Location
Number of bedrooms
No of floors
Size of property
Number of light switches?
Colour of house?
Parking Availability?
26. Weights & Bias:
Weights and biases (commonly referred to as w and b or Ѳ {theta} notation) are
the learnable parameters of a machine learning model.
Weights control the signal (or the strength of the connection) between two
neurons. In other words, a weight decides how much influence the input will
have on the output.
Biases, which are constant, are an additional input into the next layer that will
always have the value of 1.
27. Regression
(by fitting a curve / an equation to observed data).
For example, a modeler might want to relate the weights of individuals
to their heights using a linear regression model.