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Tag: Machine Learning Online Training

Enabling a Smarter Technology with Machine Learning Courses

Posted on by Imarticus Learning

Enabling a Smarter Technology with Machine Learning Courses

Machine learning has become more prevalent in enabling smarter technology in a technology-driven era. Many renowned companies worldwide heavily invest in machine learning technologies for an Artificial Intelligence (AI)-powered manufacturing process.

For instance, AI and machine learning have reduced labor costs and downtime with the industrial IoT. In addition, many companies have experienced better quality control with the advancement in machine learning. 

Today, you can grab many opportunities by polishing your skills in this field. According to O’Reilly’s Machine learning Adoption Survey, job titles related to machine learning are widely used in organizations: data scientist (81%), machine learning engineer (39%), and deep learning engineer (20%). Whether you opt for a post-graduation in machine learning or seek deep learning training, let us become more familiar with the industry. 

5 Ways Machine Learning Courses Help You Enable Smarter Technology

Here are a few examples of machine learning implementations that enable smarter technology.

Product Development

Planning and designing new products and updates usually requires extensive research and information, which can be time-consuming, thereby affecting the final product. In this phase, machine learning is chiefly implemented and helps you yield potential results. 

It helps gather customer data, analyze it to meet customer demands, and find business opportunities. Moreover, machine learning can help execute a flawless evaluation of the risk of developing new products. 

Smart Manufacturing

Another significant impact of machine learning is the daily improvement of processes throughout the manufacturing cycle. This technology has helped manufacturers detect their weak points, such as bottlenecks, unprofitable product methods, etc.

With better data analysis and IoT, manufacturers could develop efficient production methodologies and take an in-depth look into inventory, assets, logistics, and supply chain management. Thus, a data analytics course with placements can help you understand how the analysis works and supports the overall production line.

Quality Control

Machine learning helps improve quality control in two ways. First, it can easily find defects in the final product and packaging. Second, one can use machine learning to increase the quality of the manufacturing process, thereby laying a solid foundation considering the quality aspect. With this automated testing process, manual processes and errors can be avoided, requirements can be predicted, and fast and reliable answers can be obtained.

Cybersecurity

With the increasing cybersecurity concern of the digital world, machine learning has come to the rescue. This is because most apps and devices are dependent on on-premise platforms, operating systems, cloud, networks, and applications. Fortunately, with the Zero Trust Security (ZTS) plan, you can easily regulate and control users’ access to data and information.

Robots

Eventually, companies are using their best machine learning techniques to approach the innovative technology of robots. Robots are quite helpful as they can handle dangerous tasks that humans cannot. With a fast pace of innovation, robots nowadays use their machine learning technologies and capabilities to efficiently tackle complicated and complex processes, quickly surpassing the outdated thresholds. Furthermore, industrial AI-powered robots are more aware of people and surroundings.

Final Thoughts

With excellent solutions and practical applications in today’s world, machine learning plays a crucial role in enabling smarter and better technology. If you are planning to boost your technology career by pursuing machine learning courses, a post-graduation in machine learning can help you.

We at Imarticus Learning provide professional courses that make you industry-ready. Choose from a range of technology, finance, data analytics, and marketing courses that offer top-tier collaborations, qualified faculty, and more! 

Contact us today or visit your nearest training center in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

6 Trends Shaping the Future of Data Science

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6 Trends Shaping the Future of Data Science

Introduction

The data science industry is rapidly evolving. The field is changing from the types of data collected to the tools and techniques used to analyze it. More and more companies are using these insights as part of their business strategies. As the world becomes more digitally adept, data scientists are in high demand to help businesses make sense of the information they collect.

At Imarticus, we offer data science courses as we are always on the lookout for what’s next in this rapidly changing future of data science. 

Here are six predictions for trends shaping the future of data science:

1. Data Collection Becomes More Ubiquitous

As companies become more comfortable with data to improve their business performance, they will likely collect more data about their customers and employees. In particular, we expect to see an increase in the amount of location-based information that companies collect about their customers’ movements (and even their emotions).

We are still in the early stages of understanding how to use data to make better decisions, but we are beginning to understand which best practices are most effective. For example, there’s a growing consensus that it’s essential to train your models on as much data as possible—not just large datasets but a variety of datasets representing different data types and problem areas.

2. Data Scientists Become More Valuable

As companies start collecting more data types, they’ll need to hire people who can help them make sense of it all. They will be willing to pay top dollar for those people because they know how important it is to access insights from every corner of their organization. There will also be an increased demand for people training in applied statistics or machine learning to apply those skills broadly across all areas. 

Data democratization: Data scientists are not just going to be working in corporations anymore—anyone with an internet connection can harness the power of data science.

3. The Internet of Things 

IoT is already changing/defining how we interact with our environment, and it will continue to change how we interact with data. As our physical world becomes increasingly connected, we can analyze our surroundings better and understand what they mean.

4. Machine learning

ML is becoming more accessible than ever before. Thanks to cloud computing and powerful open-source tools like TensorFlow and Keras, even non-coders can create powerful models without needing a Ph.D. in mathematics or computer science.

Additionally, there is a growing awareness regarding the importance of machine learning algorithms that can handle complex tasks with no human-defined solution. It means creating systems that can learn from their users’ behavior over time and use this information to solve new problems. It is similar to how Google Search knows what you want when you type in “tacos” or “puppies” while providing recommendations based on your previous searches.

5. Deep learning

Deep learning helps us understand language at a deeper level than ever before. By analyzing a text at various levels—from individual words up to sentences, paragraphs, and entire documents—we can extract information that would otherwise be impossible to find using traditional keyword search or keyword matching algorithms.

6. The growth of Big Data

As more people start using personal data to make discoveries, we’re going to see a lot more information about human behavior emerging—and as it becomes easier for people everywhere to collect this information and share it with others, we’ll see even more discoveries made through crowdsourcing efforts than ever before.

The future of data science will also be shaped by developments in automation technology, including AI assistants like Siri or Alexa. These technologies allow us to interact with computers in new ways. For example, they can understand natural language input like commands or questions and provide answers quickly without requiring us to learn programming languages.

Conclusion

The future of data science is an exciting one. We’ve already seen some incredible advancements and more to come. Now is the best time ever to enrol in data science courses and build a career for a digital future.

Imarticus learning offers a Certificate Program in Data Science and Machine Learning to guide and train you with the best resources to prepare you for this data journey.

Get in touch with us and find a detailed analysis of how this program can potentially revamp your career. Contact us through chat support or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon for more information.

Hello new world of “Supervised Learning”

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We have entered an era of machine learning called “supervised learning.” In this world, computers can learn from data that humans have labeled. It is a considerable shift from the old world of machine learning, where computers were only able to learn from data pre-programmed by humans.

This new world of machine learning is opening up many possibilities for businesses and organizations that want to harness the power of artificial intelligence. This blog post will discuss supervised learning and how you can improve your business!

A report from Forrester Research predicts that the market for artificial intelligence will grow $37 billion globally by 2025. Advancements are driving this growth in supervised learning algorithms and the increasing amounts of data available to train machines.

An overview of supervised learning

Supervised learning allows computers to learn from data that humans have labeled. Supervised learning aims to accurately train the computer to predict the desired results for new input data.

Supervised learning algorithms operate by finding a mathematical function that best fits the training data. Using this function to predict the desired outputs for new input data. The process of finding this mathematical function is known as “training the model.”

There are many different supervised learning algorithms, each with advantages and disadvantages. The most popular supervised learning algorithms include:

  • Linear regression
  • Logistic regression
  • Support for vector machines
  • Decision trees
  • Neural networks

How Can Supervised Learning Be Used to Improve Business?

Supervised learning can improve business in many different ways. Some of the most popular applications of supervised learning include:

  • Improving customer support: It builds chatbots that provide automated customer support.
  • Enhancing marketing campaigns: Supervised learning can improve marketing campaigns’ targeting by building models that predict which customers are most likely to respond positively to a given offer.
  • Optimizing supply chains: Supervised learning can build models that predict product demand and optimize supply chains accordingly.
  • Improving fraud detection: Supervised learning builds models that detect fraudulent activity such as credit card fraud or insurance fraud.
  • Enhancing security: Supervised learning can build models that identify unusual behavior patterns that indicate security threats.
  • Predicting consumer behavior: Supervised learning builds models that predict how consumers are likely to behave in the future. You can use it to optimize product offerings and marketing campaigns.

The possibilities for using supervised learning to improve business are endless!

Discover AIML certification with Imarticus Learning

This Machine Learning certification course provides students a solid foundation in data science’s day-to-day applications by teaching them how to apply these skills to real-world issues. This training is for graduates and early career professionals who want to advance their fields in Data Science and Analytics, one of the most in-demand skill sets. 

best artificial intelligence courses by E&ICT Academy, IIT GuwahatiCourse Benefit For Learner: 

  • Students can now learn Machine Learning by participating in 25 real-world projects and case studies with industry partners to learn practical machine learning skills. 
  • Students learn how to apply machine learning to solve data-intensive problems. This course will teach students about data analytics and basic machine learning concepts, as well as some of today’s most popular tools. 
  •  Impress employers and showcase skills with the IIT AI course recognized by India’s prestigious academic collaborations.

Top 5 machine learning skills desired by employers

Posted on by Imarticus Learning

AIML or Artificial Intelligence and Machine Learning are some of the leading subject matters in the tech industry. ML is a branch of AI and has a wide range of applications in our daily lives, ranging from traffic predictions, face and voice recognition, product recommendations, virtual personal assistant, fraud detection, automatic language translation, and many more.

Therefore, big ventures like Google and Facebook are exclusively employing AIML in their products and services. Machine learning is the process by which computer scientists and engineers attempt to impart intelligent behavior into machines, to make them think and respond like human-mind in real-time situations.

For example, Google Assistant, Cortana, and Siri are entirely powered by machine learning algorithms that recognize speech.

AIML works in a complex way to make predictions and decisions based on past data, eventually refining its accuracy. A machine learning course can definitely help someone study and get training in machine learning data and algorithms.

What are the top Machine Learning Skills?

To get a desirable job related to machine learning – data engineer, machine learning engineer or machine learning scientist – you need to have knowledge and training in both software engineering and data science.

Following are the top 5 machine learning skills desired by employers:

  1. Computer Science Fundamentals and Programming

If you are getting into a technical world, then you need to have knowledge of CS fundamentals like data structures (graphs, stacks, queues, etc), algorithms (optimizing, dynamic programming, etc), computability, and complexity (NP problems, P vs NP, etc).

Having experience in different programming languages, like Python and Java, will make it easy for you to implement these fundamentals for better results.

  1. Applied Mathematics

Within applied mathematics, probability and statistics go hand in hand. Many machine learning algorithms employ probability and its techniques, like Markov Decision Process and Bayes Net, to approach uncertainties and deal with them.

You should also be well-versed in statistics to be able to build algorithms from observed data through the application of various measures, analysis methods, and distributions.

  1. Data Modelling and Evaluation

Data modeling is the process of understanding the underlying structure of a dataset, in order to find complex patterns. Furthermore, you will have to evaluate the data to be able to choose an effective accuracy/error measure like regression, clustering, and classification.

The kind of evaluation strategy that you will apply, whether it’s training-testing split or sequential vs randomized cross-validation, depends on your knowledge of data modeling and its different measures.

  1. Machine Learning Algorithms

ML algorithms are broadly characterized into three categories – supervised, unsupervised and reinforcement machine learning algorithms. You can effectively choose a machine learning algorithm if you are aware of the learning procedures and hyperparameters that affect the learning.

Some of the common algorithms are K Means Clustering, Naïve Bayes Classifier, Support Vector Machine and Linear Regression. Having appropriate knowledge of the advantages and disadvantages of these algorithms is essential to machine learning.

  1. Natural Language Processing (NLP)

NLP is the bedrock of machine learning. It is a learning model through which a computer is made to understand and interpret the human language. Many libraries across the world provide the foundation of NLP and help computers understand human language by decoding the text or speech according to its syntax.

Natural Language Toolkit is one of the most popular libraries to build NLP applications. Without the basic skill of using NLP, it can become fairly difficult to get into machine learning.

 Conclusion

All these skills come under one roof with the Artificial Intelligence and Machine Learning course offered by Imarticus. A PG in Data Analytics and Machine Learning will definitely polish these top skills and help you understand related concepts such as Deep Learning and Artificial Neural networks.

Robot pizza delivery: The tale of AI and smartest self-driving engineers

Posted on by Imarticus Learning

The demand for AI certification courses is increasing day by day. People are interested in understanding this technology and learning how they can utilize this knowledge for their growth and how they can come up with unique innovations to help make their lives easier. One such innovation is the Robot pizza delivery system!

The tale of self-driven pizza delivery ‘robots’

When you order pizza online, you will see options- takeaway and delivery. Now, people in Houston, Texas have an additional option for their Domino’s Pizza delivery, NURO 2. It is a smart robot that delivers your pizza to your doorstep. Customers can track these self-driven robotic vehicles on GPS. On delivery, they need to enter the PIN to get access to their orders. 

NURO is a startup founded by Dave Ferguson and Jiajun Zhu. This AI-based company added robotics into the picture to produce the spawn of their original project Google’s self-driven cars. The founders were the leads of the projects that included Machine Learning, computer vision, prediction, for the former and the latter handling the perception and stimulators.  

After quitting their jobs with Google, they founded this company and now have thousands of employees and are making hundreds of self-driving robotic vehicles for deliveries. 

What it means is that their core technologies involve AI and Machine learning which helps achieve this feat. It also shows that even a startup by someone having expertise in the technology can achieve much more and faster than the giants in the field. 

Learning AI and ML

These days one can easily find a course to learn AI and ML. But what matters more is what exactly you learn and from where you get the expertise. When you choose a course, it’s better to be from one of the leading institutions such as the IIT. 

You can enroll for the IIT AI ML course, the Certification In Artificial Intelligence & Machine Learning, By E&ICT Academy, IIT Guwahati. It is a 36-week course with a 3-day on-campus immersion at the IIT. The curriculum covers all the latest technologies associated with AI to help you get a strong base of the technology. 

Participants will be getting to work on the ML and Deep learning projects to shape them into experts in this field and prepare for the in-demand job profiles. 

More about the AI ML course

This Artificial Intelligence certification is not for novices but for those with a degree in related subjects such as Computer, engineering, science, maths, economics, or statistics or having at least 2 years of experience in related fields. 

The classes will be online-mode except for the 3 days on the campus. The live training classes will be conducted by experts in the industry and by the professors at the academy. 

There will be a Capstone project towards the end of the course which will be from various industries such as real-estate, security and surveillance, mobile manufacturing, hospitality, airline, marketing, healthcare, advertisement, education, e-commerce, etc. You can choose your project as per your interest. 

Apart from the project the participants will be getting mentorship assistance for motivations, tips, and encouragement for the progress, and will also get long-term assistance and connections, even after the course. 

Conclusion

The certification in AI is available for beginners and experts and one can choose the most suitable one according to the industry and interests. The prospects of using AI in day-to-today lives is increasing exponentially. Those who have creative visions must surely learn more about this technology. Who knows, maybe you can also come up with such innovations as the robot pizza delivery!

The best research and investment tools for a machine learning course

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As machine learning becomes more popular, many people look to get into the field. But what are the best research and investment tools for a machine learning course in 2022?

This post will discuss the critical tools you will need to succeed in a machine learning course. So, if you are pursuing a career in machine learning, make sure to read this blog post!

Why are research and investment tools necessary?

Research and investment tools are essential because they allow you to research and invest in new technologies. In a machine learning course, you will need to complete a lot of research to keep up with the developments in the field. Additionally, you will need to invest in new technologies to improve your skillset. Thus, research and investment tools are essential for any machine learning course.

What are some of the best research and investment tools?

Many different options are available for research and investment tools for machine learning. Each has its benefits and weaknesses, so choosing the right tool for your needs is crucial.

Here are some tools for machine learning course in 2022:

#01: Python

Python is one of the most popular programming languages for machine learning. It has a large community, and there are many open-source libraries available. Additionally, it is easy to learn, and you can use Python in your research projects because it is an interpreted language with dynamic typing and garbage collection.

#02: TensorFlow

TensorFlow is a popular open-source library for machine learning. Google developed it, allowing you to perform complex mathematical operations on data. TensorFlow is also widely used in the industry, so it is a great tool to learn if you want to pursue a machine learning career.

#03: Keras

Keras is an open-source neural network library written in Python. François Chollet developed it, and it allows you to design quickly and train deep learning models using a few lines of code.

#04: PyTorch

PyTorch is another popular machine learning framework based on Torch, an open-source machine learning library. PyTorch is for deep learning, and it allows you to develop and test your models quickly.

These are just a few research and investment tools available for machine learning courses in 2022. Make sure to explore all different options before choosing the right tool for your needs.

Discover Artificial Intelligence And Machine Learning Course with Imarticus Learning

This IIT AIML course gives students the skills they’ll need for positions in today’s digital workplace. This intensive Artificial Intelligence certification will prepare the student as a data scientist, analyst, or engineer-a professional who can use AI tools from machine learning through reinforcement algorithms and deep neural networks while developing their understanding of how these technologies work under different circumstances.

Course Benefits For Learners:

  • The Expert Mentorship program provides AIML expertise through practical experience for those who want to learn more about this exciting field of study, leading them to careers as artificial intelligence professionals or experts!

  • This course will help students gain access to attractive professional prospects in Artificial Intelligence and Machine Learning.

  • Academic professors will help students construct Data Science concepts, while industry specialists will teach students how to utilize Machine Learning, Deep Learning, and AI approaches in real-world applications.

How a machine learning course will transform your resume in 2022?

Posted on by Imarticus Learning

An artificial intelligence (AI) technology that trains computers to learn and better itself based on experience without being explicitly designed is termed Machine learning (ML). It is a set of computer programs trained to retrieve and use data. Machine learning enables computers to observe the data and provide a result without any human intervention or observation.

Machine Learning with Python

AI is the machine intelligence that leads to the practical solution to the problem, and machine learning takes AI technologies a step further by employing algorithms to examine data, learn, and make intelligent conclusions. 

For AIML, the program developers use the programming language python because it has many libraries and frameworks to make coding easy, and it also saves time.

Thus, machine learning is all about application, and if you know python, you can grasp machine learning fast. To implement anything, you should know how to code it.

Machine Learning Course

At Imarticus, we offer you an extensive program to become a data scientist, data analyst, machine learning engineer, or AI engineer, and, by becoming analytics, you can build machines and systems that will react as humans do.

In the Data analytics certification, we will teach the technique to create a machine learning model that will accurately work to give suitable and best outcomes. We will develop your analytical abilities to choose the correct algorithm as per the model compatibility and your requirement.

The first requirement of a machine learning model is data collection and its interpretation. Therefore, at Imarticus, we give you the knowledge of data manipulation, analysis, and visualization. 

As analytics, you learn to extract ideas from your team, choose proper tools, use a machine learning framework, and stay up to date with the latest development. 

The key responsibilities of analytics are:

  • Collect data, study, and then convert it into data science prototypes
  • Research for the appropriate machine learning tools and algorithm
  • Build a machine learning application that will meet the industry requirement
  • Choose the correct data and the visualization methods
  • Perform machine learning tests
  • Execute statistical analysis from the test results.
  • Set the model for accurate results

Machine Learning Resume

Your resume is your introduction and first impression for recruiters, but writing perfect codes and preparing a good model may not get you your dream job. You have to delve deeper.

Furthermore, if you want to survive in the job market, you should not only have the skills, but you should also know how to endorse these skills to your name. Furthermore, you should have an exceptional and organized resume. Hence, you must include the following points in your resume:

  • You are a certified machine learning engineer
  • Briefly mention your projects and your contribution
  • Describe your work experience in one-liner points
  • List down every information in reverse chronological format
  • Prepare a summary of your resume while highlighting your contributions

 Machine learning has a promising future, and these professionals are high in demand. At Imarticus, we know this so, the expert mentors will give you a practical understanding of AIML. They will help you to develop skills to unlock lucrative career opportunities. 

Vectors are over, hashes are the future of artificial intelligence

Posted on by Imarticus Learning

AI (artificial intelligence) aims to have computers capable of thinking independently. We are getting closer to achieving that goal, but there are some obstacles in the way. One problem is how computers understand language and communicate with humans. This blog post will discuss how hashes are the future of Artificial Intelligence.

What are vectors and hashes, and how do they differ?

Vectors are a mathematical structure that represents multiple values as a single entity. You can use Vectors in artificial intelligence for matrix multiplication and deep learning tasks. On the other hand, Hashes are a data structure that can store an object’s key-value pairs. You can use hashes in computer science for caching and data mining tasks.

Vectors are better for tasks that require large amounts of data, while hashes are better for jobs that require a small amount of data. For example, vectors are used in deep learning because they can handle a lot of data. Hashes are used in data mining because they can take a small amount of data.

Why are hashes becoming more popular in the world of AI development?

You can use them to teach computers about the environment around them. It’s easy for machines to see what something looks like, but it is much more difficult for them to understand how that object will act in specific scenarios without prior experience. It means hashes can provide a foundation of knowledge that AI systems can understand.

Most importantly, hashes offer a way to understand how the world works without requiring large amounts of data. It is essential because it takes multiple datasets to train neural networks for AI development, and those can be difficult to obtain in some cases.

How can hashes be used to improve the accuracy and efficiency of AI systems?

One way hashes can improve the accuracy and efficiency of AI systems is by reducing the number of dimensions in a vector space. In other words, hashes can help reduce the complexity of data while still preserving its information content. Additionally, you can use hashes as a form of error detection and correction. Incorporating checksums into hash algorithms makes it possible to detect and correct data errors without recomputing the hash.

It can be beneficial for large datasets that are difficult to process in their entirety. Finally, you can use hashes as a form of compression. By representing data as a series of hashes, it is possible to reduce the size of the data while still retaining its information content.

Explore and Learn AI with Imarticus Learning

The Artificial Intelligence certification program collaborates with the E&ICT Academy, IIT Guwahati, and industry professionals to deliver the most satisfactory learning experience for aspiring Artificial Intelligence and Machine Learning students. This curriculum will prepare students for a data scientist, Data Analyst, Machine Learning Engineer, and AI Engineer.

Course Benefits For Learners:

  • This Artificial Intelligence course will help students improve their Artificial Intelligence basic abilities.
  • Students can now take advantage of an Expert Mentorship program to learn about Artificial Intelligence and Machine Learning in a practical setting.
  • This course will assist students in gaining access to attractive professional prospects in the disciplines of Artificial Intelligence and Machine Learning.

Here’s how to create your own plagiarism checker with the help of python and machine learning

Posted on by Imarticus Learning

Although plagiarism is not a legal concept, the general idea behind it is rather simple. It is about unethically taking credit for someone else’s work. However, plagiarism is considered dishonest and might lead to a penalty. 

It is possible for coders to build their plagiarism checker in Python with the help of Machine Learning. Thus, it is advisable to undertake a python course to get a comprehensive idea about this programming language. 

Here, you will get an idea of creating your own plagiarism checker. Once finished, individuals can check students’ assessments to compare them with each other.  

Python Is Perfect for AI and Machine Learning
Python Is Perfect for AI and Machine Learning

Pre-requisites

To develop this plagiarism checker, individuals will need knowledge in python and machine learning techniques like cosine similarity and word2vec.

Apart from these, developers must have sci-kit-learn installed on their devices. Hence, if anyone is not comfortable with these concepts, then they can opt for an artificial intelligence and machine learning course. 

Installation    

How to Analyse Text 

It is not unknown that computers only understand binary codes. So, before computation on textual data, converting text to numbers is mandatory. 

Embedding Words  

Word embedding is the process of converting texts into an array of numerical. Here, the in-built feature of sci-kit-learn will come into play. The conversion of textual data into an array of numbers follows algorithms, representing words as a position in space. 

How to recognize the similarities between the two documents? 

Here, the basic concept of dot product can be used to check the similarity between two texts by computing the cosine similarity between two vectors. 

Now, individuals need to use two sample text files to check the model. Make sure to keep these files in the same directory with the extension of .txt.

Here is a look at the project directory – 

Now, here is a look at how to build the plagiarism checker 

  • Firstly, import all necessary modules. 

Firstly, use OS Module for text files, in loading paths, and then use TfidfVectorizer for word embedding and cosine similarity to check plagiarism. 

  • Use List Comprehension for reading files. 

Here, use the idea of list comprehension for loading all path text files of the project directory as shown –

  • Use the Lambda function to compute stability and to vectorize. 

In this case, use two lambda functions, one for converting to array from text and the next one to compute the similarity between two texts. 

  • Now, vectorize textual data. 

Add this below line to vectorize files.

  • Create a function to compute similarity 

Below is the primary function to compute the similarities between two texts.

  • Final code

During compilations of the above concept, an individual will get this below script to detect plagiarism.

  • Output 

After running the above in app.py, the outcome will look as – 

But, before you create this plagiarism checker, you might need to enroll for a python course or an artificial intelligence and machine learning course, as this programming needs concepts from python and machine learning. 

But, if you are willing to take programming as a career, a machine learning certification might be ideal for you. Nevertheless, to create a plagiarism checker of your own, make sure to use the steps mentioned above to detect similarities between the two files. 

Level 1
Copyscape Premium Verification 100% passed
Grammarly Premium Score 95
Readability Score 41.5
Primary Keyword Usage Done
Secondary Keyword Usage Done
Highest Word Density  To – 5.17%
Data/Statistics Validation Date 15/12/21
Level 2
YOAST SEO Plugin Analysis 5 Green, 2 Red
Call-to-action Tone Integration NA
LSI Keyword Usage NA
Level 3
Google Featured Snippet Optimization NA
Content Camouflaging NA
Voice Search Optimization NA
Generic Text Filtration Done
Content Shelf-life NA

Tips and tricks in AI/ML with python to avoid data leakage

Posted on by Imarticus Learning

Data science has emerged as an essential field of work and study in recent times. Thus, a machine learning course can help interested candidates learn more and land lucrative jobs. However, it is also essential to protect data to ensure proper automation.

Now, beginner courses in machine learning and artificial intelligence only teach students to split data or feed the relevant training data to the classifier. But Imarticus Learning’s AI/ML program helps gain the necessary in-depth knowledge. 

Best Ways to Avoid Data Leakage when Using AI/ML with Python

A Python certification from a reputable institute can help one gain proper insight and learn the tricks of using AI or ML with Python. This will enable interested candidates to know about real-world data processing and help them prevent data leakage.

Following are some tips that advanced courses like an artificial intelligence course by E&ICT Academy, IIT Guwahati will teach students. 

  • No Data Preprocessing Before Train-Test Split

There will be a preprocessing method fitted on the complete dataset at times. But one should not use it before the train-test split. If this method transforms the train or test data, it can cause some problems. This will happen because the information obtained from the train set will move on to the test set after data preprocessing. 

  • Use Transform on Train and Test Sets

It is essential to understand where one can use Transform and where one needs to use fit_transform. While one can use Transform on both the train set and the test set, fit_transform cannot be used for a test set. Therefore, it is wise to choose to Transform for a test set and fit_transform for a train set. 

  • Use Pickle and Joblib Methods

The Python Pickle module serializes and deserializes an object structure. However, the Pickle module may not work if the structure is extensive with several numpy arrays. This is when one needs to use the Joblib method. The Joblib tools help to implement lightweight pipelining and transparent disk-caching. 

Following are a few more tricks that help in automation and accurate data analytics when using AI/ML with Python.

  • Utilize MAE score when working on any categorical data. It will help determine the algorithms’ efficiency as the most efficient one will have the lowest case score. 
  • Utilize available heat maps to understand which features can lead to leakage. 
  • When using a Support Vector Machine (SVM), it is crucial to scale the data and ensure that the kernel cache size is adequate. One can regularise and use shrinking parameters to avoid extended training times. 
  • With K-Means and K-Nearest Neighbour algorithms, one should use a good search engine and base all data points on similarities. The K-value should be chosen through the Elbow method, and it should be relevant. 

Learn AI/ML with Python 

A Python certification will be beneficial for those who wish to pursue a career in data science and analytics. However, it is best to choose a course that will offer advanced training. Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning includes various recent and relevant topics. Apart from using AI/ML with Python, students will also get to work on business projects and use AI Deep Learning methods.

The course curriculum is industry-oriented and developed by IIT Guwahati and the E&ICT Academy. Students can interact with industry leaders, build their skills in AI and Ml through this machine learning course. This course is ideal for understanding the real-world challenges in data science and how AI/ML with Python can help provide solutions. 

The IIT artificial intelligence course from Imarticus Learning helps students become data scientists who excel in their fields of interest. The course offers holistic education in data science through live lectures and real business projects. It is therefore crucial for a rewarding job in the industry.