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Our cohesive team of Machine Learning assignment experts consists of:

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How You Can Learn Computer Vision Algorithms

Get a hold over the language to be used for implementation – practice the basics till you are comfortable.
Understand and implement one algorithm at a time. You may not understand it completely in the beginning. Give it time. Do not get stuck at one place. Try something else and come back later. Get the intuition of what is going on behind the few lines of code written to implement it. With practice things will keep getting clearer. Keep reading about it from multiple sources.
Go through the quick learning videos from YouTube or other online resources.
Join or purchase any online courses that available in reasonable price, or get materials from our team. We are also providing good materials that help to learn basic to advance data science. You can contact us at realcode4you@gmail.com or call us at given number(Contact details on website menu).
Make extensive notes when trying to understand / watching videos – it helps with internalizing the information as well as with the review.
Understand limitations of each algorithm, if any.
Understand the usual application areas of each of the algorithms and why are they used there.
Try understanding how these algorithms differ from each other. Using a single problem statement and solving it using different applicable algorithms should help here.
Remember, the algorithms are just tools to solve problems. Don’t lose sight of the main problem statement during implementation.
Many times, simple implementations are good enough. Build a simple solution first quickly and then iterate - you may want to try different features, tuning the parameters and hyperparameters, different algorithms, stacking different algorithms together and so on. Make sure you try one thing at a time and not everything together since you would want to know what change made the algorithm(s) better or worse.
Explain what you have done to one person who knows about the algorithms in technical terms and to another who does not know the algorithms per se but can follow the problem and its solution logically. Gaps in understanding are best understood when explaining to others.
Learning is an iterative process – your first implementation may not be the best. It can be made better over time. Please be patient.

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AI vs ML vs DL

What is Artificial Intelligence?
What is Machine Learning?
What is Deep Learning?

Hire Artificial intelligence Experts

The term was first introduced in 1956 in a conference where researchers wanted to digitized how human brain works

AI is the science and engineering of making computers behave in ways that until recently, we thought required human intelligence, Andrew Moore
AI is a moving target based on the capabilities that human possessed but machines do not, e.g., emotion – AI encompasses technology advances in different fields such as Machine Learning, Human Computer Interaction, etc
Example of AI: DeepBlue, and to some extent: Google Home, Siri and Alexa

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Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience ~ Tom Mitchell

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 ~ Tom Mitchell
The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful.

Deep Learning

It is a class of machine learning algorithms inspired by the structure of a human brain.
Deep learning algorithms use complex multi-layered neural networks, where the level of abstraction increases gradually by non-linear transformations of input data.

How can a machine learn?

Dataset

– The samples need to be representative

– The samples can include numbers, images, text, etc

Features

– Important piece of data that work as the key to the solution of the task

– Tell the machine/program what to pay attention to

Algorithm

– The same task can be solved using different algorithm

– The accuracy or speed of getting results can be different

If the dataset quality is high, the features were chosen right, an ML-powered system can be better than human for a given task

Machine Learning Problem

Many real-world problems are complex. Inventing specialized algorithms to solve them perfectly every time is not practical
Some example

– How can we predict future traffic pattern at an intersection?

– Is it cancer?

– What is the market value of this house five years from now?

– Which of these candidates are the perfect one for the job?

– Which of these people can be my best friend/partner?

– Will a certain people like this movie or not?

Machine Learning Algorithms In Which You Can Get Help

Generally divided into supervised and unsupervised learning, also reinforced learning, based on whether the they are trained with human supervision, and whether the training data is labeled or not

Whether or not they can learn incrementally on the fly (online versus batch learning)
Whether they work by simply comparing new data points to known data points, or instead detect patterns in the training data and build a predictive model, much like scientists do (instance-based versus model-based learning)

Supervised Learning

The training data fed to the algorithm includes the desired solutions, called labels
It models the relationship between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships learned from past data
The goal is to develop a finely tuned predictor function h(x) (sometimes called the “hypothesis”) so that, given input data x about a certain domain, e.g., square footage of a house), it will predict interesting value h(x), e.g., the market price of the house.
Two major categories are regression and classification

Some of the most important supervised learning algorithms include:

– k-Nearest Neighbors

– Linear Regression

– Logistic Regression

– Support Vector Machines (SVMs)

– Decision Trees and Random Forests

– Neural networks

Unsupervised Learning

In unsupervised learning, the training data is unlabeled
The unsupervised machine learning is typically tasked with finding relationships and correlation within data.

- Used mostly for pattern detection and descriptive modeling

Some of the most important unsupervised learning algorithms include:

– Clustering

– Visualization and dimensionality reduction

– Association rule learning

Exploratory Data Analysis (EDA) Help

1. The initial process in any machine learning implementation

2. The purpose is to understand the data, interpret the hidden information, visualizing and engineering the feature to be used by the machine learning

3. A few things to consider:

– What questions do you want to answer or prove true/wrong?

– What kind of data do you have? Numeric, Categorical, Text, Image? How are you going to treat them.

– Do you have any missing values, wrong format, etc.

– How the data is spread? Do you have any outliers? How are you going to deal with them?

– Which features are important?

– Can we add or remove features to get more from the data?

4. Data Wrangling

– Understand the data

– Getting basic summary statistics

– Handling missing values

– Handling outliers

– Typecasting and transformation

5. Data Visualization

– Univariate Analysis: histogram, distribution (distplot, boxplot, violin)

– Multivariate Analysis: scatter plot, pair plot, etc

Get Help In Feature Engineering

1. Why Feature Engineering?

– Better representation of data

– Better performing models

– Essential for model building and evaluation

– More flexibility on data types

– Emphasis on the business and domain

2. Types of data for feature engineering ranges from numerical, categorical, text, temporal, and image

– Numerical Data

– Categorical Data

– Text

– Temporal

– Image

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Get help In Computer Vision Homework

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Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it.

Image processing basically includes the following three steps:

Importing the image via image acquisition tools;
Analysing and manipulating the image;
Output in which result can be altered image or report that is based on image analysis.

Here some important image processing libraries in which you can also get help:

OpenCV

Functionality:

Basic data structures
Image processing algorithms
Basic algorithms for computer vision
Input and output of images and videos
Human face detection
And more others

TensorFlow

Functionality:

Work on multiple parallel processors
Calculation through multidimensional data arrays – tensors
Optimization for tensor processors
Immediate model iteration

And other like PyTorch, Caffe, etc.

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The classification is supported by a specific algorithm named classifier which can be implemented by using different theoretic principles, including: machine learning, statistical approaches, mathematical approaches, geometrical approaches and exhaustive computations.

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Image recognition refers to technologies that identify places, logos, people, objects, buildings, and several other variables in digital images. It may be very easy for humans like you and me to recognise different images, such as images of animals. We can easily recognise the image of a cat and differentiate it from an image of a horse. But it may not be so simple for a computer.

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Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments. The goal of object detection is to replicate this intelligence using a computer.

If you need any help in object detection in computer vision assignment then our expert ready to help you. We are group of more than 15+ machine learning expert and professionals.

Some Important Areas Where Machine Learning and Data Science Is Applied

Facebook: Machine learning and data science used in Facebook to quick search and identify and remove misleading stories from Facebook Feed. It used to improve our products and services. We develop and advance algorithms that rank feeds

Business intelligence: Today, machine learning used in business intelligence for business growth. It used to extract meaningful patterns from huge amounts of data. There are many business intelligence tools that are used to analyse the data.

Customer relationship management: CRM provide many functionalities to make the customer happy. Below are some features that makes CRM better:

Contact management
Track interactions
Scheduling/reminders
Pipeline
Sales Automation
Central Database
Email Marketing
Customization
Reporting/Analytics
Integration
Lead Generation

Self-driving cars: Now a day’s top auto and vehicles companies used the concept of machine learning and AI to design self-driving cars. It easily identifies a visible object and direction and inform the driver.

Machine learning algorithms are also used in semi-autonomous vehicles to identify a partially visible object and inform the driver.

Virtual assistants: Machine learning models are used to understand natural speech and supply context. Now a day there are many applications used the virtual assistant’s concept like; Alexa.

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Knowledge of basic algorithms.
Learn Machine learning algorithms and libraries
Basic knowledge of Data modeling and evaluation
Basic knowledge of Knowledge of statistics and probability
At least one machine learning area certifications.
Have a knowledge of programming OOPs.
At least one programming knowledge from Python, R or MATLAB

Get Help In Probabilistic Graphical Modeling

It al known as a graphical model or probabilistic graphical model (PGM) or structured probabilistic model. It is the probabilistic model for which a graph expresses the conditional dependence structure between random variables.

Probabilistic Graphical models (PGMs) are statistical models that encode complex joint multivariate probability distributions using graphs. Basically

PGMs divided into two categories:

Directed Graphical Models (DGMs), otherwise known as Bayesian Networks (BNs) and
Undirected Graphical Models (UGMs) or Markov Random Fields (MRFs).

Object Segmentation

In machine learning there are different types of segmentation are used. Segmentation, the technique of splitting customers into separate groups depending on their attributes or behavior, makes this possible.

Below the some important segmentation techniques that are used in machine learning:

Thresholding Segmentation.
Edge-Based Segmentation.
Region-Based Segmentation.
Watershed Segmentation.
Clustering-Based Segmentation Algorithms.
Neural Networks for Segmentation.

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This is the process of altering data. We we select specific features hat can help to increase the models efficiency. There are some steps which are used to implement machien learning task;

Gathering data.
Cleaning data.
Feature engineering.
Defining model.
Training, testing model and predicting the output.

Some words that are written by experienced machine learning experts; Much of the success of machine learning is actually success in engineering features that a learner can understand.

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

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Now a days data is increases day by day so that data scientist face the problem to run the large size data which need to implement using Spark. Data bricks is a data engineering tool that are used by companies to process and transform by large quantity of data.

Databricks includes a variety of datasets within the Workspace that you can use to learn Spark or test out algorithms. You’ll see these throughout the getting started guide. The datasets are available in the /databricks-datasets folder

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Google Colab Research Paper Implementation Help.jpg

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requirements.

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Analyze Big Data Using Cloudera Quick Start VM.jpg

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Here you can see some sample questions that are related to statistics and machine learning:

a) A vote with outcome for or against follows a Bernoulli distribution where P(vote = "for") = 0.27. Represent the proportion of “for” and “against” in this single Bernoulli trial using a graphics and a percentage. Can an expectation be calculated? Justify your answer.

b) The number of meteorites falling on an ocean in a given year can be modelled by a Poisson distribution with an expectation of λ = 37. Explain why a Poisson distribution is a natural candidate for this phenomenon. Give a graphic showing the probability of one, two, three...

meteorites falling (until the probability is less than 0.5%). Calculate the median and variance and show them graphically on this graphic.

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Master's Dissertation Coding and Writing Services

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Basic Structure to write Dissertation Report

ABSTRACT
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Realcode4you Machine Learning Engineer Skills

1. NLP Specialist

In today daily life we encountered with many NLP related problems like; ask our smartphones to take direction or play the music in ratio, automated call centers to give reply when call by customers etc. There are many NLP Applications that makes it important for Machine Learning Experts.

Application of NLP:

Automatic summarization is the process of creating a short summary of a longer piece of text that captures the most relevant information. Think of the abstracts or executive summaries found at the beginnings of research papers and longer reports. This can be achieved by extracting key sentences and combining them into a concise paragraph, or by generating an original summary from keywords and phrases.
Natural Language Generation (NLG) combines data analysis and text generation to take data and turn it into language that humans can understand. While it’s been used to create jokes and poems, it’s also being used to generate news articles based on stock market events and weather reports based on meteorological data.
Speech processing is the specific technology that allows virtual assistants to translate verbal commands into discrete actions for the computer to perform. This technology allows Amazon Echo to translate your request to hear some dance music into a specific Pandora search, or Siri to turn your question about local hot spots into a Yelp search for dinner recommendations.
Topic segmentation and information retrieval refer (respectively) to the process of dividing text into meaningful units and identifying meaningful pieces of information based on a search query. You enjoy the benefits of this technology every time you execute a Google search. Taken together, these two techniques are also being used by several legal tech companies to create searchable databases of legal opinions, allowing lawyers to more efficiently find relevant case law without having to scour briefs for hours on end.
Biomedical text mining is a subset of text mining used by biomedical researchers to glean insights from massive databases of specialized research. Some of its applications include identifying relationships between different proteins and genes, as well as assisting in the creation of new hypotheses.
Sentiment analysis is routinely used by social analytics companies to put numbers behind the feelings expressed on social media or the web in order to generate actionable insights. Marketers use sentiment analysis to inform brand strategies, while customer service and product departments can use it to identify bugs, product enhancements, and possible new features.

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2. Computer Vision Engineers

Realcode4you is the marketplace for top rated computer vision expert, developers, engineers, programmers, coders and professionals. Why Realcode4you is the better compare to other online services? Realcode4you.com is the better than other services; (1) We are group of 5+ year of experience experts, (2) Completed Many research papers without any Plagiarism issue, (3) Highly educated experts and professionals which also have experience of freelancing work, (4) Affordable price of student, (5) Attractive discount on First Order, (5) Capable to handle short deadline projects, (6) Hire computer vision expert in less than 1 hour.

3. Image Processing Specialist

At Realcode4you.com, you will find top rated image processing expert, coders, programmers, professionals, tutors and consultants. We are currently focused on cutting-edge technologies such as TensorFlow, Keras, deep learning, and most of the Python data science stack.

Here some image processing topics in which you can get help:

Image enhancement
Image restoration
Image color processing
Multi-resolution and processing wavelets
Image compression
Image morphology processing
Image segmentation
Object recognition
Image transmission over communication channels
Image acquisition

Important Image Processing Development tools in which you can also get help:

OpenCV
Tensorflow
Pytorch

4. Deep Learning Engineers

At Realcode4you.com you can get top freelance image recognition experts with expertise in machine learning, writing, and more. Realcode4you is the world's largest platform for hiring trusted freelance Experts and consultants. Realcode4you experts has expertise in research paper implementation and report writing.

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Build and train deep neural networks,
Identify key architecture parameters, implement vectorized neural networks and deep learning to applications
Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

5. R Programming Specialist

I have experienced machine learning and data science experts that providing data analysis services related to Python and R. We have providing the services in both industrial and academic contexts. We also have expertise in quickly understand the problems. We have Experience in analyzing data and developing systems for transitioning to, or improving, data-driven decision-making models.

6. Data Visualization Experts

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7. Tableau Expert and Engineers

This is most popular visualization tool which use by top rated Data Analysis firms and industries. Our experts created multiple dashboards from scratch. We have 5+ years of experience in interpreting and analyzing scientific research data and am proficient in data wrangling, modeling & visualization using Tableau. I have a good understanding of different univariate and multivariate statistical techniques along with various analytic tools for effective data analyses.

8. Power BI Experts and Engineers

Hire Realcode4you.com experienced, disciplined, and highly-motivated IT Professional - Data Analyst who has worked on a wide range of projects. Realcode4you.com expert has the good knowledge in other visualization tools like Tableau, R and Rapid miner.

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Power BI development and administration.
Building Analysis Services reporting models.
Developing visual reports, dashboards.
Connecting to data sources, importing data and transforming data for Business Intelligence.
Advance level calculations on the data set.
Design methodology and project documentation.
Able to properly understand the business requirements and develop data models accordingly by taking care of the resources.
Able to integrate Power BI reports into other applications using embedded analytics like Power BI service (SaaS), or by API automation

9. Big data engineers

Big data is the collection of complex and large dataset. Now a days companies hire the professionals to analyze his big data for future prediction. Big Data Engineers are professionals who work on collecting, storing, processing and analyzing huge sets of data. If face the issue to handle big data you face the issue to handle big data then don't worry, our realcode4you.com expert handle any types of big data related task easily.

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Hire Big Data Map Reduce Expert

A programming model for large-scale data processing.

Map

Iterate over a large number ofrecords
Extract something of interest from each

- Shuffle and sort intermediate results

Reduce

Aggregate intermediate results
Generate final output

10. Algorithms Engineers

Realcode4you.com expert has the deep knowledge in all machine learning related algorithms. We are also have expertise in statistics related algorithms. If you are looking to hire expert that can help you do implement your machine learning algorithms then Realcode4you.com is the right choice. Here you get quality code with an affordable price compare to other online services.

Machine Learning SQL Engineers
Recommender System Engineers
Machine Learning AWS Engineers
AI Specialist

WE ARE EXPERTISE IN BELOW TYPES OF VISUALIZATIONS

Related to Computer Vision

Below the list of python machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning

Below the list of machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning:

Parallel Coordinates chart: Parallel coordinates is a visualization technique used to plot individual data elements across many performance measures. Each of the measures corresponds to a vertical axis and each data element is displayed as a series of connected points along the measure/axes.
Density Plot: Density Plot is a type of data visualization tool. It is a variation of the histogram that uses ‘kernel smoothing’ while plotting the values. It is a continuous and smooth version of a histogram inferred from a data.
Column Chart: A column chart is a data visualization where each category is represented by a rectangle, with the height of the rectangle being proportional to the values being plotted. Column charts are also known as vertical bar charts.
Bar Graph: A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically
Stacked Bar Graph: A stacked bar chart is also known as a stacked bar graph. It is a graph that is used to compare parts of a whole. In a stacked bar chart each bar represents the whole, and the segments or parts in the bar represent categories of that whole
Grouped Bar Chart: A grouped barplot is used when you have several groups, and subgroups of these groups. The example in this post shows how to build a grouped barplor using the bar() function of matplotlib library.
Area Chart: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.
Dual Axis Chart: A dual axis chart (also called a multiple axes chart) uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement. The relationship between two variables is referred to as correlation.
Line Graph: A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.
Candle set chart: A typical candlestick chart is composed of a series of bars, known as candles, which vary in height and color. Candlestick charts are one of the most popular chart types for day traders. Learn how to read these charts and apply them to your trading
Box and whisker plot: A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles.
Mekko Chart: A Mekko chart (sometimes also called marimekko chart) is a two-dimensional stacked chart. In addition to the varying segment heights of a regular stacked chart, a Mekko chart also has varying column widths. Column widths are scaled such that the total width matches the desired chart width.
Pie Chart: A pie chart, sometimes called a circle chart, is a way of summarizing a set of nominal data or displaying the different values of a given variable (e.g. percentage distribution).
Bubble Chart: Bubble chart displaying the relationship between poverty and violent and property crime rates by state. Larger bubbles indicate higher percentage of state residents at or below the poverty level.
Scatter Plot Chart: A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point.
Grouped Scatter Chart: Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. In this we group more than two variable that called group scatter chart.
Scatter Plot Matrix: A scatter plot matrix is a grid (or matrix) of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships to be explored in one chart.
Radar Chart: A radar chart is a way of showing multiple data points and the variation between them. They are often useful for comparing the points of two or more different data sets.
Radial Bar Chart: A Radial/Circular bar chart is a bar chart displayed on a polar coordinate system. The difference between radial column chart is that base axis of series is y axis of a radar chart making columns circular. You can easily adjust start/end angles of a chart by setting startAngle and endAngle of your RadarChart component.
Donut chart: A donut chart is essentially a Pie Chart with an area of the centre cut out. A donut chart (also spelled doughnut) is functionally identical to a pie chart, with the exception of a blank center and the ability to support multiple statistics at once.
Bullet Graph: A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures
Funnel Chart: A funnel chart is a specialized chart type that demonstrates the flow of users through a business or sales process. Funnel charts show values across multiple stages in a process. For example, you could use a funnel chart to show the number of sales prospects at each stage.
TreeMap: A treemap chart provides a hierarchical view of your data and makes it easy to spot patterns, such as which items are a store's best sellers. Treemapping is a data visualization technique that is used to display hierarchical data using nested rectangles;
Dendo gram: A dendrogram (or tree diagram) is a network structure. It is constituted of a root node that gives birth to several nodes connected by edges or branches.
Heat Map: A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.
Violin Chart: A violin plot is a method of plotting numeric data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.
Area graph: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.
stacked Area graph: Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series.

Best Machine Learning Sample Projects 2023

Project 1(OpenCV): Domain- Entertainment

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

OpenCV Assignment Help, Face Detection Using OpenCV.jpg

Project 2: Statistical Analysis to Reducing Gender Inequality in Wages and Employment

Germany’s government is interested in reducing gender inequality, especially gender wage gaps and gender gaps in employment. They are considering the introduction of a set of
policies that incentivize firms to shrink gender inequality in working conditions. First, the policy forces firms to internally publish salaries of all workers, so discrepancies in salaries can be detected by workers themselves. Second, firms are incentivized to encourage salary negotiations. Third, firms are incentivized to offer childcare where needed for their employees to fulfill their duties.
The government has been made aware that, in parts of the United States, exactly these policies have been introduced and now asks you to evaluate the effectiveness of these policies in reducing gender inequality in wages and employment. The dataset genderinequality (provided in RData and csv formats) contains data on individuals in the U.S., some working in firms to which the new policies apply (these are the treated workers) and some working at firms to which the new policies do not apply (these are the untreated workers). The policies have been introduced in 2007 and we have panel data on workers for the years 2005 and 2010, i.e. before and after the introduction of the new policies.

Statistical Analysis to Reducing Gender Inequality in Wages and Employment.jpg

Project 3: ReneWind

Renewable energy sources play an important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases. Wind energy is one of the most developed technologies worldwide and the U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices. Predictive maintenance means failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors.

Hire Machine Learning Expert, Get Help In Machine Learning Assignment, Best Machine learni

Project 4: TRAIN&AHEAD PROJECT

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group.