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PG Program in Machine Learning
in collaboration with IBM

Become an industry-ready Certified Machine Learning professional through immersive learning of everything from scratch to machine learning with this Job Assistance Program

Become an industry-ready Certified Machine Learning professional through immersive learning of everything from scratch to machine learning with this Job Assistance Program

In Collaboration With
  • Silver
    Business
    Partner
  • IBM

30 Sep, 2022

Next Batch
starts on

6 Months

Recommended
18-20 hrs/week

Online

Learning
Format

10,000

Career
Transformed

400+

Hiring
Partners

Program Overview

Best online Machine Learning program in India and across the globe. Get trained with highly in-demand tools, techniques, and technologies.

Key Highlights

  • One-on-One with industry mentorsOne-on-One with industry mentors
  • 100% Placement Assistance100% Placement Assistance
  • 360 Degree Career Support360 Degree Career Support
  • Instant Doubt ResolutionInstant Doubt Resolution
  • Ideal for both Working Professionals and Fresh GraduatesIdeal for both Working Professionals and Fresh Graduates
  • Interactive LearningInteractive Learning

PG Program In Machine Learning In Collaboration With IBM

  1. $ 1,000
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    16000+ learners
Features
  • 200+ hours of learning
  • Practice Test Included
  • Certificate of completion
  • Instant Doubt Resolution

Languages and Tools covered

  • Excel in Data Science program - online excel course
  • python in Data Science Program - online python course
  • tableau in Data Science program - online tableau course
  • NLP in Data Science - online NLP course
  • SQL in Data science course - online SQL course

6 Months PG Program in Machine Learning in Collaboration with IBM

Get elligible for 3 world-class certificates thus adding that extra edge to your resume

  • Learning paths and certification from IBM
  • Course completion certificate from DataTrained Education
  • Project completion certificate from DataTrained Education

What’s the focus of this course?

In this course we will cover everything from the scratch to the advanced levels of machine learning

6 Unique Specializations- data science programs near me

Learn Anything, Anytime, Anywhere

Learn through our HD online videos by world class faculties and industry experts

Dedicated Career Assistance- data science program institute

Dedicated Career Assistance

Receive 1:1 career counseling sessions & mock interviews with hiring managers. Exhilarate your career with our 400+ hiring partners.

Student Support -  data science online training

Student Support

Chat support for Quick Doubt Resolution is available from 06 AM to 11 PM IST. Program Managers are available on call, chat and ticket during business hours.

Instructors

Learn essential skill from the industry's top leaders by enrolling in DataTrained – IBM-certified curriculum.

Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture

Shankargouda Tegginmani

Data Scientist, Accenture

Shankar is a Data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Sanket Maheshwari - Data Scientist, Faasos

Sanket Maheshwari

Data Scientist, Faasos

Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.

Polong Lin - Business Analyst, IBM

Polong Lin

Business Analyst, IBM

Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU.

Jay Rajasekharan- Data Scientist, IBM

Jay Rajasekharan

Data Scientist, IBM

Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.

Mahdi Noorian- Data Scientist, IBM

Mahdi Noorian

Data Scientist, IBM

Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick.

Macine Learning Course Syllabus

Best-in-class content in the form of pre-recorded HD videos, live sessions, case studies, projects, assignments, and industry webinars from leading faculty and industry experts.

best data science courses online

Detailed Syllabus of Macine Learning Course

  • 100+ Hours of Content - data science program institute
  • 200+

    Hours of Content

  • 100+ Live Sessions - data science online training
  • 100+

    Live Sessions

  • 100+ Live Sessions - data science online training
  • 15

    Tools and Software

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, IBM, and Expert Industry Professionals.

100+ Hours of Content - data science program institute
200+

Hours of Content

100+ Live Sessions - data science online training
100+

Live Sessions

15 Tools and Software - best tools for data science
15

Tools and Software

Module 1

Foundations

The Foundations bundle comprises 2 courses where you will learn to tackle Statistics and Coding head-on. These 2 courses create a strong base for us to go through the rest of the tour with ease.

This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.

Python Basics

Python is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.

Anaconda Installation - Jupyter notebook operation

Using Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.

Python functions, packages and other modules

For code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.

NumPy, Pandas, Visualization tools

In this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.

Working with various data structures in Python, Pandas, Numpy

Understanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.

In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.

Descriptive Statistics

Descriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.

Inferential Statistics

In this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.

Probability & Conditional Probability

Probability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.

Hypothesis Testing

With this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.

Module 2

Machine Learning

Machine Learning is a part of artificial intelligence that allows software programs to boost their prediction accuracy without simply being expressly designed to do so. You will learn all the Machine Learning methods from fundamental to advanced, and the most frequently used Classical ML algorithms that fall into all of the categories.

With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.

Linear Regression - Simple, Multiple regression

Linear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.

Logistic regression

Logistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.

K-NN classification

k-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?

Support vector machines

Support Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.

We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.

Decision Trees

The Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.

Random Forests

Random Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.

Bagging and Boosting

When the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.

Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.

In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.

K-means clustering

In Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.

Hierarchical clustering

Hierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.

Principal Component Analysis

PCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.

Module 3

Advanced Techniques

EDA - Part1

Exploratory Data Analysis (EDA) is a procedure of analyzing the data using different tools and techniques. You will learn data standardization and represent the data through different graphs to assess and make decisions for several business use cases. You will also learn all the essential encoding techniques.

EDA - Part2

You will also get a opportunity to use null values, dealing with various data and outliers preprocessing techniques to create a machine learning model.

Feature Engineering

Feature Engineering is the process of extracting features from an organization's raw data by using domain expertise. A feature is a property shared by independent units that can be used for prediction or analysis. With this module, you will learn how this works.

Feature Selection

Feature selection is also called attribute selection, variable selection, or variable subset selection. It is the process of selecting a subset of relevant features for use in model development. You can learn many techniques to do the feature selection.

Model building techniques

Here you will learn different model-building techniques using different tools

Model Tuning techniques

In this module, you can learn how to enhance model performance using advanced techniques as GridSearch CV, Randomized Search CV, cross-validation strategies, etc.

Building Pipeline

What is Modeling Pipeline and how does it work? Well, it is a set of data preparation steps, modeling functions, and prediction transform routines organized in a logical order. It allows you to specify, evaluate, and use a series of measures as an atomic unit.

Module 4

Time Series Analysis

Introduction

A time series is a set of data points that appear in a specific order over a specific time. A time series in investing records the movement of selected data points, like the cost of security, with a set period of time, with data points collected at regular intervals.

Time Series Components

In this module, you will learn about different components that are necessary to analyze and forecast future outcomes.

Stationarity

You will learn what is stationarity and the importance of learning stationarity.

Time Series Models

In this module, you will learn common Time series models as AR, MA, ARIMA, etc.

Model Evaluation

When you build models, you will use different evaluation methods to gauge the product performance or even accuracy. Yes, In this module, you will learn model evaluation methods.

Use Case and Assignment

You will also get a chance to work on assignments and feel at ease while working on the use case scenarios.

Projects

Also, we are providing a few more extra projects for practice, you can assemble and compare your solutions with the ones we provide.

Module 5

Recommendation Engine

Introduction

In the introduction module, you will learn why recommendation systems are used, their requirement, and their applications.

Understanding the relationship

In this module, you will learn on what basis recommendation engine works and their association rules.

Types of Data in RS

In this module, you will learn all the types of data used in the Recommendation Engine.

Ratings in RS

In this module, you will learn just how the ratings are drawn in the Recommendation Engine.

Similarity and Its Measures

Recommendation systems work on the basis of similarity between the product and the consumers who view it. There are many ways for determining how similar 2 products are. This similarity matrix is used by recommendation systems to recommend the next most comparable product to the customer.

Types of Recommendation Engine

In this module, you will learn different types of Recommendation Engines.

Evaluation Metrics in Recommendation

Once you build the models, you require metrics to evaluate how effective is your model. You will learn various evaluation tools in RE.

Use cases

You will also get an opportunity to focus on additional use cases. Later, you can compare your solution with the SME-provided solution.

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

  • Data science program Engage in collaborative projects and learn from peers
  • Data science programMentoring by industry experts to learn and apply better
  • Data science programPersonalized subjective feedback on your submissions to facilitate improvement
Smartphone and Smartwatch Activity - data science training in india

Smartphone and Smartwatch Activity

The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.

Recommendation System - best online data science programs

Recommendation System

In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.

Air Quality Study - data science course near me

Air Quality Study

Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country

Why DataTrained for Machine Learning Program in India?

The best most exclusive Machine Learning program in India is the Post Graduate Program in Machine Learning. The program is developed with Data scientists from IBM and industry experts working in the data science domain for decades and according to the international industry standards. The course duration is 5 months including a well-balanced curve of practical and theoretical learning’s covering everything from the basics to the advanced levels of Machine Learning program in India and across the globe.

Enroll now to benefit from the best Machine Learning program online

Resume pepration by DataTrained

Partnered with IIMJobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.

Interview pepration DataTrained

Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps learners to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters.

100% granted placement

We generate the Ability Score of every individual which is then sent to our more than 400+ recruitment partner organizations. At last, we organize campus placements quarterly in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai to place our students.

Career Impact

DataTrained in collaboration with IBM presents the best online Machine Learning Program in India. With 10,000+ careers transformed.

DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the Algorithms with the real industry datasets.DataTrained brings the power of online learning along with dedicated Mentorship, Counselling, Live Sessions and 6 months Internship.

Aaruni Khare-- Data Scientist
Aruni Khare Data Scientist, RBS

I saw an ad from DataTrained on facebook and I contacted them straight away and enquired about their Data Science online course. Their counselor took me through the complete journey of what they offer and what is data science all about. After continuous conversation for a few weeks, I was pretty sure about the course and now I knew where I need to invest my money and hard work.

Rakshit Jain- Data Scientist, Optum
Rakshit Jain Data Scientist, Optum

The program is a well-balanced mix of pre-recorded classes, live sessions on weekends and printed reading materials they sent to my address. My mentor was Amit Kaushik and he helped me in getting that confidence and completing my assignments on time.I have almost completed the course and have been able to crack Glenmark interview.Thank you so much DataTrained.

Rupam Kumar Chaurasia-- Head Sales, Glenmark
Rupam Kumar Chaurasia Head Sales, Glenmark

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After my graduation, I didn't want to pursue MBA since everyone is doing it I wanted to do something different but I was confused. I opted for the PG Program in Data Science by Data Trained Education and I had an amazing journey with them, the trainers were top-notch, the course content was perfect.

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I can certainly say the content they are offering is really good. Assignments are relatable. Completing the assignments helps in a better understanding of the module. In a nutshell, I would recommend this course to anyone interested in Data Science.

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Admission Process

There are 3 simple steps in the Admission Process that are detailed below

Step 1: Fill in a Query Form

Fill up the Query Form and one of our counselors will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.

Step 3: Block your Seat & Begin the Prep Course

Block your seat with a payment of INR 10,000 to enroll in the program. Begin with your Prep course and start your Machine Learning journey!

Machine Learning Course Fee

$ 1,000

No Cost EMI options are also available. *

I’m interested in this program

What's included in the price

Placements

Access to real-life projects

Access to domain specific mentorship

Access to career assist by IIMJobs

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Frequently Ask Questions

Each of us brings a unique set of skills, expertise, and experience to the table. There is no one-size-fits-all approach to learning machine learning. Based on my personal experience, there are two distinct sorts of learners:

  • Those who have the opportunity (and resources, particularly time and money) to devote themselves entirely to study. For such students, this usually entails earning a degree and studying for several years.
  • Those who do not have the financial means to pursue a degree. They could be people who already have jobs and families and, understandably, are unable to pursue a full-time education. This category also covers those who refuse to pursue a degree because they cannot afford to pay for it.

Most machine learning engineering positions require a bachelor's degree in a relevant discipline, such as computer science, mathematics, or statistics, with some requiring a master's degree or Ph. D. in machine learning, computer vision, neural networks, deep learning, or a similar topic. Certifications in machine learning, artificial intelligence, or data science are beneficial outside of higher education since they offer relevant skills.

Let's look at the eligibility criteria for an Machine Learning Engineer at Amazon to get an understanding of what it takes to be an ML Engineer in a huge company:

  • A master's degree in computer science or a related discipline, or equivalent experience.
  • Experience with machine learning/artificial intelligence, fairness, data quality, data science, or information integration research
  • A Professional ML Engineer exam from Google can also be used to examine your abilities to formulate ML challenges, construct ML models, architect ML solutions, and prepare and process data.

The PG Program In Machine Learning offered by DataTrained is one of the best courses available in the market. This program will help you to pursue a career in this domain. This course includes 5 months duration content that will cover each and every topic from scratch to advanced level. They also provide certificates after the completion of this course that will definately give you an extra edge over other candidates and increase the employment opportunities. So what are you waiting for to enroll now? Just go for it. It's totally worth it. This course only costs Rs.82,600 only(GST included) which is again a reasonable amount for such great content. They provide 100% placement assistance after the completion of this course.

The syllabus for the Machine Learning course by DataTrained is one of the best courses in the market. Which covers Major concepts & topics some of them are:

  • Module 1- Foundations, Python for Artificial Intelligence & Machine Learning, Applied statistics
  • Module 2- Machine Learning, Supervised Learning, Ensemble Techniques, Unsupervised Machine Learning
  • Module 3- Advanced Techniques
  • Module 4- Time Series Analysis
  • Module 5- Recommendation Engine

And so much more. You can just click here to get all the information you need for this course

Machine learning is a rapidly growing topic that is attracting a lot of interest, yet finding machine learning jobs remains a challenge. To get a job as an engineer at a big corporation, you'll need to know not only Data Science, but also programming and system architecture. When it comes to applying for a new job, there is usually a lot of study and learning involved. DataTrained PG program in machine learning is one of the best machine learning courses which will not only help you to learn everything from scratch to advanced level but they will also provide you 100% Guaranteed placement assistance to help you secure a better job of your choice.

Not only will machine learning usher in a new period of civilization, but it will also usher in a new stage in the evolution of life on Earth."

Machine learning (ML) has been a game-changer, paving the way for the fourth industrial revolution. Forward-thinking merchants, automakers, financial services firms, game developers, and researchers, among others, have flocked to AI like ducks to water. According to research, the worldwide machine learning market will be worth Rs 543 billion by 2023. Nasscom has created a graph depicting the demand and supply mismatch in AI and Big Data Analytics talent. Machine learning engineers are known by the titles of Research Scientist and Research Engineer. You should investigate new data approaches and algorithms, such as supervised, unsupervised, and deep learning techniques, utilised in adaptive systems, if you want to pursue a career in machine learning engineering.

During the period 2018-2021, employment opportunities surged by more than 64%, but the supply gap worsened by nearly 125 percent — the numbers speak for themselves.(Source)

Machine learning algorithms can be trained in a variety of ways, each with its own set of benefits and drawbacks. To comprehend the benefits and drawbacks of each sort of machine learning, we must first consider the type of data they consume.

There are two types of data in machine learning: labelled data and unlabeled data. There are some sorts of machine learning algorithms that are utilised in very specific use-cases, but today there are three basic ways.

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

A machine learning engineer's average annual income is ₹671,548. Machine learning engineers with less than a year of experience earn roughly ₹5,00,000 per year, making them one of India's highest entry-level wages. Obviously, the rate for early-stage machine learning engineers varies depending on their skill set, region, and demand.

A mid-level engineer earns an average of ₹11,73,074 per year. If they think that's wonderful, you'll find the income of senior-level engineers (those with more than 10 years of experience) motivating, since they make over 2 million rupees per year.

Not only has technology advanced numerous industrial and professional procedures, but it has also improved ordinary life. But, first and foremost, what is machine learning? It's a branch of artificial intelligence that focuses on employing statistical approaches to create intelligent computer systems that can learn from databases. Machine learning is currently applied in a variety of sectors and industries. Medical diagnosis, image processing, prediction, classification, learning association, regression, and so on are only a few examples.

Intelligent systems based on machine learning algorithms can learn from previous experience or historical data. Machine learning programmes generate outcomes based on previous experience. We'll look at ten real-world instances of how machine learning is assisting in the development of better technology to power today's ideas in this post.

Machine learning tools are algorithmic applications of artificial intelligence that allow systems to learn and develop without a lot of human input; data mining and predictive modelling are similar concepts. They allow software to improve its accuracy in anticipating outcomes without having to programme it directly.

There is a large variety of Machine Learning Software available in the market. The most popular among them are given below. To know about more tools click here.

Platform Cost Written in language Algorithms or Features
Scikit Learn Linux, Mac OS, Windows Free Python, Cython, C, C++ Classification
Regression
Clustering
Preprocessing
Model Selection
Dimensionality reduction
PyTorch Linux, Mac OS, Windows Free Python, C++, CUDA Autograd Module
Optim Module
nn Module
TensorFlow Linux, Mac OS, Windows Free Python, C++, CUDA Provides a library for dataflow programming
Weka Linux, Mac OS, Windows Free Java Data preparation
Classification
Regression
Clustering
Visualization
Association rules mining
KNIME Linux, Mac OS, Windows Free Java Can work with large data volumes.
Supports text mining & image mining through plugins
Colab Cloud Services Free Java Supports libraries of PyTorch, Keras, TensorFlow, and OpenCV
Apache Mahout Cross-platform Free Java, Scala Preprocessors
Regression
Clustering
Recommenders
Distributed Linear Algebra
Accors.Net Cross-platform Free C# Classification
Regression
Distribution
Clustering
Hypothesis Tests & Kernel Methods
Image, Audio & Signal. & Vision