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Their are many programming languages which are used for machine learning and data science. Here some of important and used by many tech firms and researchers. Python and R is important and widely used for data science and machine learning task. Both programming languages support large number of packages that makes the Implementation easy and fast.

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How to Learn ML and Data Science 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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Hire Machine Learning and Deep Learning Experts

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.

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

Get help In Feature Sampling

Using a sampling algorithm can help us reduce the size of the dataset to a point where we can use a better, but more expensive, machine learning algorithm. The key principle here is that the sampling should be done in such a manner that the sample generated should have approximately the same properties as the original dataset. There are mainly 2 types of Sampling in which you can get help

Sampling without replacement
Sampling with replacement

Dimensionality Reduction Assignment Help

Most real world datasets have a large number of features. For example, consider an fraud detection problem, we might have to deal with a lot of features, also called as dimensions. As the name suggests, dimensionality reduction aims to reduce the number of features - but not simply by selecting a sample of features from the feature-set. Data Analysis algorithms work better if the dimensionality of the dataset is lower. This is mainly because irrelevant features and noise have now been eliminated. The models which are built on top of lower-dimensional data are more understandable and explainable.

Get Help In Feature Encoding

Feature encoding is basically performing transformations on the data such that it can be easily accepted as input for machine learning algorithms while still retaining its original meaning. As we all know that better encoding leads to a better model and most algorithms cannot handle the categorical variables unless they are converted into a numerical value. Types of categorical features In Which You Can Get Help

Binary(yes/no and True/false)
Ordinal(Low, Medium, high / cold, hot, lava hot etc.)
Nominal(Cat, dog, tiger / pizza, burger, coke)

Get Help In Label Encoding

Label encoding algorithm is quite simple and it considers an order for encoding. Hence can be used for encoding ordinal data. LabelEncoder is present in scikit-learn library

Code

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

df['ord_2'] = le.fit_transform(df['ord_2'])

sns.countplot(df['ord_2'])

Get Help In One Hot Encoding

To overcome the Disadvantage of Label Encoding as it considers some hierarchy in the columns which can be misleading to nominal features present in the data, we can use the One-Hot Encoding strategy.

It is done in the following 2 steps:

1.Splitting of categories into different columns.

2.Put ‘0 for others and ‘1’ as an indicator for the appropriate column.

Code:

from sklearn.preprocessing import OneHotEncoder

enc = OneHotEncoder() enc = enc.fit_transform(df[['nom_0']]).toarray()

encoded_colm = pd.DataFrame(enc)

df = pd.concat([df, encoded_colm], axis=1)

df = df.drop(['nom_0'], axis=1)

df.head(10)

Get Help In Frequency Encoding

We can also encode considering the frequency distribution. This method can be effective at times for nominal features.

Code:

# grouping by frequency

fq = df.groupby('nom_0').size()/len(df)

# mapping values to dataframe

df.loc[:, "{}_freq_encode".format('nom_0')] = df['nom_0'].map(fq)

# drop original column.

df = df.drop(['nom_0'], axis=1)

fq.plot.bar(stacked=True)

df.head(10)

Get Help In Features Scaling

Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model. Scaling can make a difference between a weak machine learning model and a better one. The most common techniques of feature scaling are

Normalization and Standardization.

Normalization is used when we want to bound our values between two numbers, typically, between [0,1] or [-1,1]. While Standardization transforms the data to have zero mean and a variance of 1, they make our data unitless. Where to use feature scaling

KNN
K-means
Principal Component Analysis
Gradient Descent

Data Compression

Data compression is an essential phase in training a network. The idea is to compress the data so that the same amount of information can be represented by fewer bits. This also helps with the problem of the curse of dimensionality as discussed earlier. A dataset with many attributes is different to train with because it tends to overfit the model. Hence dimensionality reduction techniques need to be applied before the dataset can be used for training.

Techniques: Data Compression

This is where the Autoencoder (AE) and Variational Autoencoder (VAE) come into play. They are end-to-end networks that are used to compress the input data. Both Autoencoder and Variational Autoencoder are used to transform the data from a higher to lower-dimensional space, essentially achieving compression.

Get Help In AutoEncoder (AE)

Autoencoder is used to learn efficient embeddings of unlabeled data for a given network configuration. The autoencoder consists of two parts, an encoder, and a decoder. The encoder compresses the data from a higher-dimensional space to a lower-dimensional space (also called the latent space), while the decoder does the opposite i.e., convert the latent space back to higher-dimensional space.

Get Help In Generative adversarial networks(GANs)

Generative adversarial nets are alternative framework for training generative models in order to avoid the difficulty of approximating many intractable probabilistic computations. Generative adversarial networks (GANs) are one class of models that have been successfully used to model complex and high dimensional distributions. The main advantage in adversarial nets is the Markov chains are never needed, and gradients can be obtained using only back-propagation. Also during learning no inference is required and a extensive variety of interactions and factors can easily be incorporated into the model.

Types: GANs

Conditional GANs

GANs can be extended to a conditional model. In conditional GAN generator and discriminator are conditioned on extra information. This extra information can be class labels or other data. This Conditioning can be done by feeding this extra information into discriminator and generator as as additional input layer. The generator combines the extra information and noise together as a joint representation which is hidden. The adversarial training framework has shown considerable flexibility in how this hidden representation is collected.

Machine Learning and Data Science Engineer – Scope of Work In Future

Realcode4you Machine Learning Experts and Data Scientists can help develop the best ML models by creating a winning AI strategy for your company. Below the description of Machine Learning engineer jobs include various tasks and responsibilities.

Design the solution architectures for ML Applications
Research and implementation of ML algorithms and thesis without any plagiarism
Develop Machine Learning applications as per customer need
Data Analysis with right and clean visuals
Identify and fix the issues
Help to deploy machine Learning models

Cloudera QuickStart VM Big Data Assignment Help

This is simple Cloudera’s Big Data platform. If you need help in any big data which is related to Cloudera QuickStart VM Big Data Map-Reduce or any Big Data Related .

These are free for personal use, but do require you to register your details on the
Cloudera website prior to download. Remember to check your system meets the minimum
requirements.

Below the basic requirement to install the cloudera in machine:

A virtual machine such as Oracle Virtual Box or VMWare
RAM of 12+ GB. That is 4+ GB for the operating system and 8+ GB for Cloudera
80GB hard disk

Complete Installation guide you can get from below link:

https://ugc.futurelearn.com/uploads/files/3c/c9/3cc92360-1155-4eee-8d59-4c7c0e3d192c/Instructions_Installing_Cloudera.pdf

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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.

What Specifications Machine Learning Experts Should Have

If you want to become a machine learning experts then need to have some specifications. If you are beginners and not know anything related machine learning and data science, then don’t worry. Realcode4you.com expert team help you to became a machine learning experts. Below the some specifications that are required to became the ML Expert.

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.

Important Deep Learning Tools

There are different types of tools that is used in deep learning:

Neural Designer
H2O.ai
DeepLearningKit
Microsoft Cognitive Toolkit
Keras
ConvNetJS
PyTorch
TensorFlow
Caffe
MxNet
Theano

Master's Dissertation Coding and Writing Services

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Basic Structure to write Dissertation Report

ABSTRACT
INTRODUCTION
LITERATURE REVIEW
Project Topics Description
METHODOLOGY
Model Description and Evaluation
Conclusion

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Get Help In Applied AI

Realcode4you.com top rated expert and professional team provide all AI related problem solution with an affordable price. If you are looking any help in Applied AI then contact us.

Applied AI Topics In Which You Can Get Help:

1. Feedforward neural nets with hyperparameter optimisation: this project will implement a feedforward neural network for a dataset of choice and experiment systematically with a number of hyperparameter configurations, e.g. the learning rate, batch size, number of hidden units, layers, etc. The project will need to explore systematic approaches for hyperparameter optimisation such as random or grid optimisation or genetic algorithms. Note that the specifics will not be taught directly, you’ll need to research and find out how to implement these in Python yourself. You may use any software libraries available, as long as referenced. The approaches named above e.g. come with sk_learn and do not need to be implemented from scratch. The neural network’s performance should be evaluated in different settings and compared against other approaches, such as decision trees, Naive Bayes or other classifiers. Results should be supported with visualisations, such as graphs.

2. Text classification: this project will implement a deep learning system for text classification (e.g. using the news dataset from the lab, or any other you can find). You can choose what classes you want to learn (i.e. classify) from your dataset. You will need to make an informed choice of neural network (such as recurrent or transformer) and implement it using a deep learning library. This part can be based on a lab session we did together. The project should also include at least one additional component, e.g. a specific hypothesis you want to investigate, a comparison against another technique, or a data augmentation technique, such as language modelling (i.e. embed features into vector space using one or more techniques e.g. Word2Vec, GloVe, BERT, GPT-2, etc.). Regardless of what you choose to do concretely, make sure that you use baselines in your project, i.e. choose the system you want to “pitch” and make sure you compare it another setup. Results should be supported with visualisations, such as graphs.

3. Sentiment analysis from text and/or images: this project will implement a deep learning based system for sentiment analysis. You will need to choose a dataset (e.g. not the one used in our sentiment analysis lab please) and make an informed choice of architecture. Then implement it using a deep learning library. You can then either focus on sentiment analysis from text (as we’ve done before) or image analysis, e.g. predicting sentiment from images. In either case, please make sure to benchmark your results against an alternative setting, e.g. experimenting with more than one neural network architecture, or experimenting substantially with your chosen architecture itself, e.g. using hyperparameter optimisation. Results should be supported with visualisations, such as graphs.

Deep Learning Topics In Which You Can Get Help

Below the some important topics in which you can get help:

Single-layer neural networks: The perceptron algorithm

Mathematical and computational foundations

Linear algebra and calculus for deep learning
Parameter optimization with gradient descent
Automatic differentiation with PyTorch
Cluster and cloud computing resources

Introduction to neural networks

Multinomial logistic regression
Multilayer perceptrons and backpropration
Regularization to avoid overfitting
Input normalization and weight initialization
Learning rates and advanced optimization algorithms
Project proposal (student submission)

Deep learning for computer vision and language modeling

Introduction to convolutional neural networks
Convolutional neural networks architectures
Introduction to recurrent neural networks

Deep generative models

Autoencoders
Variational autoencoders
Introduction to generative adversarial networks
Evaluating generative adversarial networks
Recurrent neural networks for seq-to-seq modeling
Self-attention and transformer networks

Research Paper Writing Help: Format that followed by Experts

Abstract—These instructions give you guidelines for preparing papers for your research project. Use this document as a template if you are using Microsoft Word or later. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Full names of authors are preferred in the author field. Put a space between authors’ initials. Do not cite references in the abstract. Do not delete the blank line immediately above the abstract; it sets the footnote at the bottom of this column.
Keywords—Enter key words or phrases in alphabetical order, separated by commas.

I. INTRODUCTION

Your should place your introduction in this section.

II. LITERATURE REVIEW

Type your literature review in this section. Please define abbreviations and acronyms the first time they are used in the text, even after they have already been defined in the abstract.

III. METHODOLOGY

Type your methodology here.

IV. EMPIRICAL RESULTS AND ANALYSIS

Present and analyze your empirical results here.

V. CONCLUSION

A conclusion section is required. Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions.

APPENDIX

Appendixes, if needed, appear before the acknowledgment.

ACKNOWLEDGMENT

Here you can give special thanks to those who assisted you.

REFERENCES

All references should be cited in text and listed below. Please use the Harvard, Chicago or APA referencing styles.

BUILD YOUR VISUALIZATION DASHBOARD

WE ARE EXPERTISE IN BELOW TYPES OF VISUALIZATIONS

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

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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.

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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.

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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.