This document provides an overview of machine learning in Python using key Python libraries. It discusses popular Python libraries for machine learning like NumPy, SciPy, Pandas, Matplotlib and scikit-learn. It outlines the typical steps in a machine learning project including defining the problem, preparing and summarizing data, evaluating algorithms, and presenting results. It also introduces the Iris dataset as a sample classification dataset and discusses loading, handling and visualizing sample data for a machine learning project in Python.
Python is a basic term for the programming language which anyone can generally work. in this blog, you briefly learn about benefits & Advantages and disadvantages of python
Python is a basic term for the programming language which anyone can generally work. in this blog, you briefly learn about benefits & Advantages and disadvantages of python
PYTHON CURRENT TREND APPLICATIONS- AN OVERVIEWEditorIJAERD
Python is a powerful high-level, interpreted, interactive, and object-oriented scripting language created by
Guido Van Rossum in late 1980’s. Python is a very suitable language for the beginner level programmers and supports
the development of a wide range of applications from simple text processing to www browsers to games developments.
One of the biggest reasons for Python’s rapid growth is the simplicity of its syntax. The language reads almost like plain
English, making it easy to write complex programs. In this paper we first analyze you to Python programming language
popularity and features. Moreover, this paper specifying applications areas where python can be applied and specially
analyzing web application frameworks which are using in Python programming language
WHY
WHERE
HOW
WHEN
WHO
FOR WHAT
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
this presentation will walk you through basic introduction to python, major features of python, how python runs on our system and some important commands used in python.
This power point slides best describes the contents taught to us during the internship on Python taken by us in the college. It is totally a practical learning session and we learnt a lot about practical use of Python. So, I think to share it.
These are the slides I was using when delivering the Python Crash Course (https://www.meetup.com/life-michael/events/247984087/). The crash course was delivered in Hebrew. More info about the Python Programming course I deliver can be found at python.course.lifemichael.com.
Boost your career with Python Programming Language at SSDN Technologies in Gurgaon. In this training you learn about Python basic to advance concept by industry expert. Register Now !!
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
PYTHON CURRENT TREND APPLICATIONS- AN OVERVIEWEditorIJAERD
Python is a powerful high-level, interpreted, interactive, and object-oriented scripting language created by
Guido Van Rossum in late 1980’s. Python is a very suitable language for the beginner level programmers and supports
the development of a wide range of applications from simple text processing to www browsers to games developments.
One of the biggest reasons for Python’s rapid growth is the simplicity of its syntax. The language reads almost like plain
English, making it easy to write complex programs. In this paper we first analyze you to Python programming language
popularity and features. Moreover, this paper specifying applications areas where python can be applied and specially
analyzing web application frameworks which are using in Python programming language
WHY
WHERE
HOW
WHEN
WHO
FOR WHAT
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
this presentation will walk you through basic introduction to python, major features of python, how python runs on our system and some important commands used in python.
This power point slides best describes the contents taught to us during the internship on Python taken by us in the college. It is totally a practical learning session and we learnt a lot about practical use of Python. So, I think to share it.
These are the slides I was using when delivering the Python Crash Course (https://www.meetup.com/life-michael/events/247984087/). The crash course was delivered in Hebrew. More info about the Python Programming course I deliver can be found at python.course.lifemichael.com.
Boost your career with Python Programming Language at SSDN Technologies in Gurgaon. In this training you learn about Python basic to advance concept by industry expert. Register Now !!
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Afternoons with Azure - Azure Machine Learning CCG
Journey through programming languages such as R, and Python that can be used for Machine Learning. Next, explore Azure Machine Learning Studio see the interconnectivity.
For more information about Microsoft Azure, call (813) 265-3239 or visit www.ccganalytics.com/solutions
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
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.
Python for Data Science: A Comprehensive Guidepriyanka rajput
Python’s popularity in data science is undeniable, to sum up. It is the best option for data analysts and scientists because of its simplicity, extensive library environment, and community support. The essential Python tools and best practices have been highlighted in this thorough book, enabling data aficionados to succeed in this fast-paced industry.
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi | Automating Machine Learning, Artificial Intelligence, and Data Science | Guided Analytics
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
1. Python and Data AnalyticsPython and Data Analytics
•Understand the problem By Understanding the Data
•Predictive Model Building: Balancing Performance, Complexity,
and theBig Data
4. Predictive model buildingPredictive model building
The process of building a predictive model is called
training.
Attributes: the variables being used to make predictions is known as:
◦ Predictors.
◦ Features
◦ Independent variables
◦ Input
Labels are also known as,
◦ Outcomes
◦ Targets
◦ Dependent variables
◦ Responses
5. A machine learning project may not be
linear, but it has a number of well known
steps:
Define Problem.
Prepare Data.
Evaluate Algorithms.
Improve Results.
Present Results.
6. the iris dataset has followingthe iris dataset has following
structurestructure
Attributes are numeric so you have to figure out
how to load and handle data.
It is a classification problem, allowing you to
practice with perhaps an easier type of supervised
learning algorithm.
It is a multi-class classification problem (multi-
nominal) that may require some specialized
handling.
It only has 4 attributes and 150 rows, meaning it is
small and easily fits into memory.
All of the numeric attributes are in the same units
and the same scale, not requiring any special scaling
or transforms to get started.
7. Machine Learning in Python:Machine Learning in Python:
Step-By-StepStep-By-Step
Installing the Python and SciPy
platform.
Loading the dataset.
Summarizing the dataset.
Visualizing the dataset.
Evaluating some algorithms.
Making some predictions.
8. Basic library in pythonBasic library in python
NumPy‘s array type augments the Python language
with an efficient data structure useful for numerical
work, e.g., manipulating matrices. NumPy also
provides basic numerical routines, such as tools for
finding eigenvectors.
SciPy contains additional routines needed in
scientific work: for example, routines for computing
integrals numerically, solving differential equations,
optimization, and sparse matrices.
The matplotlib module produces high quality plots.
With it you can turn your data or your models into
figures for presentations or articles. No need to do
the numerical work in one program, save the data,
and plot it with another program.
9. The Pandas module is a massive collaboration of many
modules along with some unique features to make a very
powerful module.
Pandas is great for data manipulation, data analysis, and data
visualization.
The Pandas modules uses objects to allow for data analysis
at a fairly high performance rate in comparison to typical
Python procedures. With it, we can easily read and write
from and to CSV files, or even databases.
From there, we can manipulate the data by columns, create
new columns, and even base the new columns on other
column data.
The scikit library used for
Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable
10. NumPy: Base n-dimensional array
package
SciPy: Fundamental library for scientific
computing
Matplotlib: Comprehensive 2D/3D
plotting
IPython: Enhanced interactive console
Sympy: Symbolic mathematics
Pandas: Data structures and analysis
11. 1. Downloading, Installing and Starting
Python SciPy
1.1 Install SciPy Libraries
There are 5 key libraries that you will need to
install. Below is a list of the Python SciPy
libraries required for this tutorial:
scipy
numpy
matplotlib
pandas
sklearn