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Introduction

Working as an ML engineer, it is common to be in situations where you spend hours to build a great model with desired metrics after carrying out multiple iterations and hyperparameter tuning but cannot get back to the same results with the same model only because you missed to record one small hyperparameter.

What could save one from such situations is keeping a track of the experiments you carry out in the process of solving an ML problem.

If you have worked in any ML project, you would know that the most challenging part is to arrive at good performance – which makes it necessary to carry out several experiments tweaking different parameters and tracking each of those.

You don’t want to waste time looking for that one good model you got in the past – a repo of all the experiments you carried out in the past makes it hassle-free.

Just a small change in alpha and the model accuracy touches the roof – capturing the small changes we make in our model & their associated metrics saves a lot of time.

All your experiments under one roof – experiment tracking helps in comparing all the different runs you carry out by bringing all the information under one roof.

Should we just track the machine learning model parameters?

Well, No. When you run any ML experiment, you should ideally track multiple numbers of things to enable reproducing experiments and arriving at an optimized model:

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Code: Code that is used for running the experiments

Data: Saving versions of the data used for training and evaluation

Environment: Saving the environment configuration files like  ‘Dockerfile’,’requirements.txt’ etc.

Parameters: Saving the various hyperparameters used for the model.

Metrics: Logging training and validation metrics for all experimental runs.

Whether you are a beginner or an expert in data science, you would know how tedious the process of building an ML model is with so many things going on simultaneously like multiple versions of the data, different model hyperparameters, numerous notebook versions, etc. which make it unfeasible to go for manual recording.

Fortunately, there are many tools available to help you. Neptune is one such tool that can help us track all our ML experiments within a project.

Let’s see it in action!

Install Neptune in Python

In order to install Neptune, we could run the following command:

pip install neptune-client

For importing the Neptune client, we could use the following line:

import chúng tôi as Neptune

Does it need credentials?

We need to pass our credentials to the neptune.init() method to enable logging metadata to Neptune.

run = neptune.init(project='',api_token='') Logging the parameters in Neptune

We use the iris dataset here and apply a random forest classifier to the dataset. We consequently log the parameters of the models, metrics using Neptune.

from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score from joblib import dump data = load_iris() X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.4, random_state=1234) params = {'n_estimators': 10, 'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 3, } clf = RandomForestClassifier(**params) clf.fit(X_train, y_train) y_train_pred = clf.predict_proba(X_train) y_test_pred = clf.predict_proba(X_test) train_f1 = f1_score(y_train, y_train_pred.argmax(axis=1), average='macro') test_f1 = f1_score(y_test, y_test_pred.argmax(axis=1), average='macro')

To log the parameters of the above model, we could use the run object that we initiated before as below:

run['parameters'] = params

Neptune also allows code and environment tracking while creating the run object as follows:

run = neptune.init(project=' stateasy005/iris',api_token='', source_files=['*.py', 'requirements.txt'])

Can I log the metrics as well?

The training & evaluation metrics can be logged again using the run object we created:

run['train/f1'] = train_f1 run['test/f1'] = test_f1   Shortcut to log everything at once?

We can create a summary of our classifier model that will by itself capture different parameters of the model, diagnostics charts, a test folder with the actual predictions, prediction probabilities, and different scores for all the classes like precision, recall, support, etc.

This summary can be obtained using the following code:

import neptune.new.integrations.sklearn as npt_utils run["cls_summary "] = npt_utils.create_classifier_summary(clf, X_train, X_test, y_train, y_test)

folders on the Neptune UI as shown below:

What’s inside the Folders? 

The ‘diagnostic charts’ folder comes in handy as one can assess their experiments using multiple metrics just with one line of code on the classifier summary.

The ‘all_params’ folder comprises the different hyperparameters of the model. These hyperparameters help one to compare how the model performs at a set of values and post tuning them by some levels. The tracking of the hyperparameters additionally helps one to go back to the exact same model (with the same values of hyperparameters) when one needs to.

The trained model also gets saved in the form of a ‘.pkl’ file which can be fetched later to use. The ‘test’ folder contains the predictions, prediction probabilities, and the scores on the test dataset.

We can get a similar summary if we have a regression model using the following lines:

import neptune.new.integrations.sklearn as npt_utils run['rfr_summary'] = npt_utils.create_regressor_summary(rfr, X_train, X_test, y_train, y_test)

Similarly, for clustering as well, we can create a summary with the help of the following lines of code:

import neptune.new.integrations.sklearn as npt_utils run['kmeans_summary'] = npt_utils.create_kmeans_summary(km, X, n_clusters=5)

Here, km is the name of the k-means model.

How do I upload my data on Neptune?

We can also log csv files to a run and see them on the Neptune UI using the following lines of code:

run['test/preds'].upload('path/to/test_preds.csv') Uploading Artifacts to Neptune

Any figure that one plot using libraries like matplotlib, plotly etc. can be logged as well to Neptune.

import matplotlib.pyplot as plt plt.plot(data) run["dataset/distribution"].log(plt.gcf())

In order to download the same files later programmatically, we can use the download method of ‘run’ object using the following line of code:

run['artifacts/images'].download()

Final Thoughts

In this article, I tried to cover why experiment tracking is crucial and how Neptune can help facilitate that consequently leading to an increase in productivity while conducting different ML experiments for your projects. This article was focused on ML experiment tracking but we can carry out code versioning, notebook versioning, data versioning, environment versioning as well with Neptune.

There are of course many similar libraries available online for tracking the runs which I would try to cover in my next articles.

About Author

Nibedita Dutta

Nibedita is a master’s in Chemical Engineering from IIT Kharagpur and currently working as a Senior Consultant at AbsolutData Analytics. In her current capacity, she works on building AI/ML-based solutions for clients from an array of industries.

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Choose Best Python Compiler For Your Machine Learning Project – Detailed Overview

This article was published as a part of the Data Science Blogathon.

Introduction

programming language and has different execution environments. It has a wide range of compilers to execute the python programs eg. PyCharm, PyDev, Jupyter Notebook, Visual Studio Code, and many more. The compiler is a special program that is written in a specific programming language to convert the human-readable language i.e. high-level language to machine-readable language i.e. low-level language.

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So in this blog, I am going to cover my personal favorite top 6 python compilers that are useful for Python developers and data scientists. So let’s get started!

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List of Python Compilers

Here is a wide range of compilers to execute the python programs.

PyCharm

Spyder

Visual Studio Code

PyDev

Jupyter Notebook

Sublime Text

PyCharm

It is created by Jet Brains and it is one of the best and broadly utilized Integrated Development Environment (IDE). Developers utilize this IDE for creating gainful Python and creates perfect and viable code. The PyCharm IDE assists engineers with making greater profitability and gives savvy help to the developers. It helps developers to write good quality code correctly. It saves the developers time by performing the fast compilation.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Jet Brains

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Features of PyCharm

It supports more than 1100 plugins

Provides an option to write own plugin

It has a code navigator, code editor, and fast & safe refactoring

It provides developers with an option to detect errors, fast fix errors and to complete auto code, etc.

It can be easily integrated with an IPython notebook.

It provides functionality to integrate debugging, deployments, testing, etc

Pros

It is very easy to use

Installation is very easy

Very helpful and supportive community

Cons

In the case of large data, it becomes slow

Not beginners friendly

Check the official page here: PyCharm

Spyder

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Pierre Raybaut

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Features

Provides auto-code completion and syntax highlighting feature

It supports multiple IPython consoles

With the help of GUI, it can edit and explore the variables

It provides a debugger to check the step by step execution

User can see the command history in the console

Pros

It is open-source and free

To improve the functionalities, it supports additional plugins

Provide support for strong debugger

Cons

The very old style interface

Difficult to find the terminal in this compiler

Check the official page here: Spyder

Visual Studio Code

This IDE is developed by Microsoft in 2024. It is free and open-source. It is lightweight and very powerful. It provides features such as unit testing, debugging, fast code completion, and more. It has a large number of extensions for different uses, for example, if you want to use C++, then install C++ extension, similarly install the different extension for different programming languages.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Microsoft

Features

It has an inbuilt Command Line Interface

It has an integrated Git that allows users to commit, add, pull and push changes to a remote Git repository utilizing a straightforward GUI.

It has an API for debugging

Visual Studio Code Live Share is an element that empowers you to share your VS Code case, and permit somebody distant to control and run different things like debuggers.

Pros

It supports multiple programming languages eg. Python, C/C++, Java etc

Provides auto-code feature

It has built-in plugins

Cons

Sometimes, it crashes and shutdowns

The interface isn’t all that great and it required some time to begin

Check the official page here: Visual Studio Code

PyDev

PyDev is free and open-source, people can introduce it from the web and begin utilizing it. It is perhaps the most usable IDE and liked by a large portion of developers.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Appcelerator

Features

It provides functionalities such as debugging, code analysis, refactoring, etc

Provides error parsing, folding of code, and syntax for highlighting code.

It supports black formatted, virtual environment, PyLint, etc

Pros

It supports Jython, Django Framework, etc

It offers supports for different programming languages like Python, Java, C/C++, etc

Provides auto-code completion and syntax highlighting feature

Cons

When multiple plugins are installed, the performance of PyDev diminishes

Check the official page here: PyDev

Jupyter Notebook

It is one of the most widely used python IDE for data science and machine learning environments. It is an open-source and web-based interactive environment. It permits us to create and share documents that have mathematical equations, plots, visuals, live code, and readable text. It supports many languages such as Python, R, Julia, etc but it is mostly used for Python.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Brian Granger, Fernando Perez

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Features

Easy collaboration

Provides the option to download jupyter notebook in many formats like PDF, HTML file etc

It provides presentation mode

Provides easy editing

Provides cell level and selection code execution that is helpful for data science

Pros

It is beginners friendly and perfect for data science newbies.

It supports multiple languages like Python, R, Julia, and many more

With the help of data visualization libraries such as matpotlib and seaborn, we can visualize graphs within the IDE

It has a browser-based interface

Cons

It doesn’t provide a good security

It doesn’t provide code correction

Not effective in real-world projects – use only for dummy projects

Check the official page here: Jupyter Notebook

Sublime Text

Sublime Text is an IDE that comes in two renditions for example free and paid. The paid variant contains additional highlights features. It has different plugins and is kept up under free software licenses. It upholds numerous other programming languages, for instance, Java, C/C++, and so on not just Python.

Sublime Text is very quick when contrasted with other text compilers. One can likewise introduce different bundles like debugger, code linting, and code completion.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Jon Skinner

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Features

Provides option for customization

Instant switch between different projects

It provides split editing

It has a Goto Anything option, that allows user to jump the cursor wherever they want

It supports multiple languages such as Python, java, C/C++

It provides Command Palette

It has a distraction-free mode too

Pros

Very interactive interface – very handy for beginners

Provide plugin which is very helpful in debugging and tet highlighting.

Provide time to time suggestion for accurate syntax

It provides a free version

Working on different projects are possible at the same time

Cons

Not wors well in case of large documents

One of the most annoying things is, it doesn’t save documents automatically.

At some time, plugins are difficult to handle.

Check the official page here: Sublime Text

Frequently Asked Questions

Q1. Which compiler is best for Python?

A. Python is an interpreted language, and it does not require compilation like traditional compiled languages. However, popular Python interpreters include CPython (the reference implementation), PyPy (a just-in-time compiler), and Anaconda (a distribution that includes the conda package manager and various scientific computing libraries).

Q2. What is a Python compiler?

A. In the context of Python, a compiler is a software tool that translates Python code written in high-level human-readable form into low-level machine code or bytecode that can be executed directly by a computer. The compiled code is typically more efficient and faster to execute than the original Python source code. Python compilers can optimize the code, perform static type checking, and generate standalone executable files or bytecode files that can be run on a specific platform or within a virtual machine. Examples of Python compilers include Cython, Nuitka, and Shed Skin.

Conclusion

So in this article, we have covered the top 6 Python Compilers For Data Scientists in 2023. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

You can check my articles here: Articles

Connect me on LinkedIn: LinkedIn

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Tracking The Evolution Of The Project Management

A project manager plays an important role in managing human resources and handling all aspects of a project efficiently. They are mainly associated with the construction and engineering industry, but with the increasing popularity of effective project management, the demand for a manager has been growing exponentially across various industries.

A project manager is known for their ability to complete and deliver all types and complexities of projects in a timely manner. They provide leadership and direction to their teams, helping them navigate the tricky parts of projects efficiently. For the past three decades, the PMP exam has been considered the main qualification for a project manager.

These technologies work with an extensive range of software applications to help managers achieve their project goals while staying within budget and timeline and without wasting resources. Today, the goals of a manager go beyond the basic management process. It’s more about achieving stakeholder satisfaction and ensuring that each process is executed according to the client’s instructions with minimal risk of errors.

Evolution of PMBOK Guide

PMBOK (Project Management Body of Knowledge) is a guide for aspiring project managers that are planning to appear for the PMP exam. Although the content in the guide has changed over the past few years, the primary goal remains the same. PMBOK is one of the most crucial elements of project management. It helps you learn the basics of project management, the emerging trends in this industry, and the latest technological changes and the impact they have on your projects.

The first PMBOK was released in 1996 as a guide that teaches aspiring project managers the fundamentals of management, challenges a manager faces, decision−making, and so on. This guide covers the entire syllabus of the PMP exam.

Four years later, the second version of the PMBOK was released. The guide was filled with more valuable and informative content for the project manager’s growth. It also included practices and techniques which proved beneficial for managers. Then came the third version that included the latest and revised project management processes, improving the project’s lifecycle and making the management part more efficient. This version covered integration, cost, risk, time quality, procurement, and scope management.

The fourth version of PMBOK is one of the most popular editions, as it introduced stakeholders as key players in any project. The guide focuses on how to improve a project’s efficiency and ensure the successful completion of a given project while satisfying the needs of stakeholders and keeping them up−to−date with the project’s status. The PMBOK’s fifth version continued the focus on stakeholders.

Latest Editions of PMBOK

You may have heard of different project management methodologies and their role in improving management and bringing efficiency to all organizational processes. Well, to gain knowledge about project management, you need to check the 6th and 7th editions of PMBOK that introduced agile project management methodologies.

In addition to that, you can notice changes in the techniques, management processes, and other aspects of project management after going over the 6th edition of PMBOK. Among other concepts, it discusses Agile methodology applications. Agile methodology is about dividing the project into several sprints, which can be accomplished more efficiently than traditional management. The 6th edition has also highlighted the role of strategic thinking in project management and how it plays a crucial role in driving business growth.

The latest edition of PMBOK, i.e., the 7th part, was released recently in 2023. This latest version talks about the drastic change brought to the PMP format. The focus has now been shifted to reducing those lengthy guides into shorter management procedures. Now, the section is expanded to cover a wide range of project management techniques, which are efficient, manageable, and generate better results than the previous models.

The syllabus meets the demand for the changes introduced recently in the PMP exam. For example, the most common change in the 7th edition is the increased focus on principles instead of management processes. If you have read all 6 versions, you may have noticed how the focus had always been on the individual tasks. In the last few editions, managers shared techniques and tools that can help you achieve success in different tasks. The latest version, however, focuses more on the project and the final delivery.

What does Project Management Look Like Today?

Today, project management has a broader scope than in previous decades. The latest challenge that nearly all project managers have faced is shifting to a remote and hybrid work environment. As most businesses had shut down during the pandemic, people had to move work to the cloud. Businesses allowed employees to operate work from home so that the work could be continued.

Although the pandemic ended and operations returned to normal, the work−from−home trend hasn’t changed. This has presented new challenges for project management. Communication, for instance, has become the biggest challenge for project managers handling remote teams or a combination of remote and hybrid workers.

Fortunately, we have several tools, like Zoom and Skype, that makes communication easier and allows managers to conduct face−to−face interactions. The role of a project manager isn’t limited to overseeing different tasks, but it’s equally important that you know how to resolve conflicts, give and take feedback, conduct regular meetings, and divide your projects into smaller and achievable tasks. Note that finishing a project requires dedication, hard work, and the right management strategies.

This was all about the evolution of project management and how PMBOK has come a long way in making project management an effective, better, and more cost−efficient process. If you are planning to become a project manager, check the 6th and 7th editions of PMBOK to prepare for the PMP exam.

Build And Automate Machine Learning

Intel keeps on eating up new businesses to work out its machine learning and AI tasks.

In the most recent move, TechCrunch has discovered that the chip goliath has obtained chúng tôi an Israeli organization that has manufactured and works a platform for information scientists to assemble and run machine learning models, which can be utilized to prepare and follow various models and run examinations on them, construct proposals and the sky is the limit from there.

Intel affirmed the securing to us with a short note. “We can affirm that we have procured Cnvrg,” a representative said.

Also read: Top 10 Trending Technologies You should know about it for Future Days

Intel isn’t uncovering any monetary terms of the arrangement, nor who from the startup will join Intel.

Cnvrg, helped to establish by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from financial specialists that incorporate Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook gauges that it was esteemed at around $17 million in its last round.

It was just seven days back that Intel made another procurement to help its AI business, additionally in the region of machine getting the hang of demonstrating: it got SigOpt, which had built up a streamlining platform to run machine picking up displaying and reenactments.

Also read: Top 10 Programming Languages for Kids to learn

Cnvrg.io’s platform works across on-reason, cloud and half and half conditions and it comes in paid and complementary plans (we covered the dispatch of the free help, marked Core, a year ago).

It contends with any semblance of Databricks, Sagemaker and Dataiku, just as more modest activities like chúng tôi that are based on open-source structures.

Cnvrg’s reason is that it gives an easy to use platform to information scientists so they can focus on formulating calculations and estimating how they work, not fabricating or keeping up the platform they run on.

While Intel isn’t saying much regarding the arrangement, it appears to be that a portion of a similar rationale behind a week ago’s SigOpt procurement applies here also: Intel has been pulling together its business around cutting edge chips to more readily contend with any semblance of Nvidia and more modest players like GraphCore.

So it bodes well to likewise give/put resources into AI apparatuses for clients, explicitly administrations to help with the process stacks that they will be running on those chips.

It’s prominent that in our article about the Core complementary plan a year ago, Frederic noticed that those utilizing the platform in the cloud can do as such with Nvidia-improved holders that sudden spike in demand for a Kubernetes bunch.

Also read: Best 12 Vocabulary Building Apps for Adults 2023?

Intel’s attention on the up and coming age of figuring expects to balance decreases in its inheritance activities. In the last quarter, Intel announced a 3% decrease in its incomes, driven by a drop in its server farm business.

It said that it’s anticipating the AI silicon market to be greater than $25 billion by 2024, with AI silicon in the server farm to be more prominent than $10 billion in that period.

Understand Machine Learning And Its End

This article was published as a part of the Data Science Blogathon.

What is Machine Learning?

Machine Learning: Machine Learning (ML) is a highly iterative process and ML models are learned from past experiences and also to analyze the historical data. On top, ML models are able to identify the patterns in order to make predictions about the future of the given dataset.

Why is Machine Learning Important?

Since 5V’s are dominating the current digital world (Volume, Variety, Variation Visibility, and Value), so most of the industries are developing various models for analyzing their presence and opportunities in the market, based on this outcome they are delivering the best products, services to their customers on vast scales.

What are the major Machine Learning applications?

Machine learning (ML) is widely applicable in many industries and its processes implementation and improvements. Currently, ML has been used in multiple fields and industries with no boundaries. The figure below represents the area where ML is playing a vital role.

Where is Machine Learning in the AI space?

Just have a look at the Venn Diagram, we could understand where the ML in the AI space and how it is related to other AI components.

As we know the Jargons flying around us, let’s quickly look at what exactly each component talks about.

How Data Science and ML are related?

Machine Learning Process, is the first step in ML process to take the data from multiple sources and followed by a fine-tuned process of data, this data would be the feed for ML algorithms based on the problem statement, like predictive, classification and other models which are available in the space of ML world. Let us discuss each process one by one here.

Machine Learning – Stages: We can split ML process stages into 5 as below mentioned in the flow diagram.

Collection of Data

Data Wrangling

Model Building

Model Evaluation

Model Deployment

Identifying the Business Problems, before we go to the above stages. So, we must be clear about the objective of the purpose of ML implementation. To find the solution for the given/identified problem. we must collect the data and follow up the below stages appropriately.

Data collection from different sources could be internal and/or external to satisfy the business requirements/problems. Data could be in any format. CSV, XML.JSON, etc., here Big Data is playing a vital role to make sure the right data is in the expected format and structure.

Data Wrangling and Data Processing: The main objective of this stage and focus are as below.

Data Processing (EDA):

Understanding the given dataset and helping clean up the given dataset.

It gives you a better understanding of the features and the relationships between them

Extracting essential variables and leaving behind/removing non-essential variables.

Handling Missing values or human error.

Identifying outliers.

The EDA process would be maximizing insights of a dataset.

Handling missing values in the variables

Convert categorical into numerical since most algorithms need numerical features.

Need to correct not Gaussian(normal). linear models assume the variables have Gaussian distribution.

Finding Outliers are present in the data, so we either truncate the data above a threshold or transform the data using log transformation.

Scale the features. This is required to give equal importance to all the features, and not more to the one whose value is larger.

Feature engineering is an expensive and time-consuming process.

Feature engineering can be a manual process, it can be automated

Training and Testing:

the efficiency of the algorithm which is used to train the machine.

Test data is used to see how well the machine can predict new answers based on its training.

used to train the model.

Training

Training data is the data set on which you train the model.

Train data from which the model has learned the experiences.

Training sets are used to fit and tune your models.

Testing

learnt good enough from the experiences it got in the train data set.

are “unseen” data to evaluate your models.

Test data: After the training the model, test data is used to test its efficiency and performance of the model

The purpose of the random state in train test split: Random state ensures that the splits that you generate are reproducible. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.

Data Split into Training/Testing Set

We used to split a dataset into training data and test data in the machine learning space.

The split range is usually 20%-80% between testing and training stages from the given data set.

A major amount of data would be spent on to train your model

The rest of the amount can be spent to evaluate your test model.

But you cannot mix/reuse the same data for both Train and Test purposes

If you evaluate your model on the same data you used to train it, your model could be very overfitted. Then there is a question of whether models can predict new data.

Therefore, you should have separate training and test subsets of your dataset.

MODEL EVALUATION: Each model has its own model evaluation mythology, some of the best evaluations are here.  

Evaluating the Regression Model.

Sum of Squared Error (SSE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

Coefficient of Determination (R2)

Adjusted R2

Evaluating Classification Model.

Confusion Matrix.

Accuracy Score.

Deployment of an ML-model simply means the integration of the finalized model into a production environment and getting results to make business decisions.

So, Hope you are able to understand the Machine Learning end-to-end process flow and I believe it would be useful for you, Thanks for your time.

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Machine Learning Is Revolutionizing Stock Predictions

Stock predictions made by machine learning are being deployed by a select group of hedge funds that are betting that the technology used to make facial recognition systems can also beat human investors in the market.

Computers have been used in the stock market for decades to outrun human traders because of their ability to make thousands of trades a second. More recently, algorithmic trading has programmed computers to buy or sell stocks the instant certain criteria is met, such as when a stock suddenly becomes cheaper in one market than in another — a trade known as arbitrage.

Software That Learns to Improve Itself

Machine learning, an offshoot of studies into artificial intelligence, takes the stock trading process a giant step forward. Pouring over millions of data points from newspapers to TV shows, these AI programs actually learn and improve their stock predictions without human interaction.

According to Live Science, one recent academic study said it was now possible for computers to accurately predict whether stock prices will rise or fall based solely on whether there’s an increase in Google searches for financial terms such as “debt.” The idea is that investors get nervous before selling stocks and increase their Google searches of financial topics as a result.

These complex software packages, which were developed to help translate foreign languages and recognize faces in photographs, now are capable of searching for weather reports, car traffic in cities and tweets about pop music to help decide whether to buy or sell certain stocks.

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Mimicking Evolution and the Brain’s Neural Networks

A number of hedge funds have been set up that use only technology to make their trades. They include Sentient Technologies, a Silicon Valley-based fund headed by AI scientisk Babak Hodjat; Aidiya, a Hong Kong-based hedge fund headed by machine learning pioneer Ben Goertzel; and a fund still in “stealth mode” headed by Shaunak Khire, whose Emma computer system demonstrated that it could write financial news almost as well as seasoned journalists.

Although these funds closely guard their proprietary methods of trading, they involve two well-established facets of artificial intelligence: genetic programs and deep learning. Genetic software tries to mimic human evolution, but on a vastly faster scale, simulating millions of strategies using historic stock price data to test the theory, constantly refining the winner in a Darwinian competition for the best. While human evolution took two million years, these software giants accomplish the same evolutionary “mutations” in a matter of seconds.

Deep learning, on the other hand, is based on recent research into how the human brain works, employing many layers of neural networks to make connections with each other. A recent research study from the University of Freiburg, for example, found that deep learning could predict stock prices after a company issues a press release on financial information with about 5 percent more accuracy than the market.

Hurdles the Prediction Software Faces

None of the hedge funds using the new technology have released their results to the public, so it’s impossible to know whether these strategies work yet. One problem they face is that stock trading is not what economists call frictionless: There is a cost every time a stock is traded, and stocks don’t have one fixed price to buyers and sellers, but rather a spread between bid and offer, which can make multiple buy-and-sell orders expensive. Additionally, once it’s known that a particular program is successful, others would rush to duplicate it, rendering such trades unprofitable.

Another potential problem is the possible effects of so-called “black swan” events, or rare financial events that are completely unforeseen, such as the 2008 financial crisis. In the past, these types of events have derailed some leading hedge funds that relied heavily on algorithmic trading. Traders recall that the immensely profitable Long-Term Capital Management, which had two Nobel Prize-winning economists on its board, lost $4 billion in a matter of weeks in 1998 when Russia unexpectedly defaulted on its debt.

Some of the hedge funds say they have a human trader overseeing the computers who has the ability to halt trading if the programs go haywire, but others don’t.

The technology is still being refined and slowly integrated into the investing process at a number of firms. While the software can think for itself, humans still need to set the proper parameters to guide the machines toward a profitable outcome.

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