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Machine Learning Course in Delhi, Ncr


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Machine Learning Course in Delhi
  • Upgrade Your Skills For a High Paying Corporate Job
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  • Develop Real World Projects
  • Work on Multiple Case Studies

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Upgrade Your Skill In Machine Learning Course

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Machine Learning Course in Delhi


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Machine Learning Course in Delhi
Machine Learning Institute in Delhi

Machine Learning Institute in Delhi India's No-1 Machine Learning Course Institute With Most Advanced Course Curriculum

Machine Learning Institute in DelhiAcademic Partner - Hewlett Packard



What Is Machine Learning Course & Why It Is The Most Demanding Skill Now a Days?

  • Searching the answers for "what is machine learning?" in the Google search bar shows up "a pandora's box" of forums, educational research, and sometimes bogus information.

    The sole purpose of this article is to abridge the definition and understanding of machine learning for you.

    What is machine learning?

    A computing algorithm is a set of rules/instructions that a computing language programmer specifies for a computer to process.

    Tom M. Mitchell describes machine learning as "Machine learning is the study of computer algorithms that allow computer programs to improve through experience automatically."

    It is a sub-domain of artificial intelligence (AI) that trains computer systems to learn and improve from experience without being unambiguously programmed automatically.

    Machine learning technology aims to optimize a system's performance when handling new data through user-defined programming logic for a given environment.

Importance of machine learning in 2020

The machine learning domain is continuously evolving. This is leading to a rise in demand and importance. There is one crucial reason why data scientists call for machine learning.

The reason is 'High-value predictions that can guide better decisions and smart actions in real-time without human intervention.'


  • The technology helps analyze large datasets, easing data scientists' responsibilities in an automated method and gaining eminence and recognition.

  • It facilitates the ability to automatically and rapidly apply numerical calculations to big data.

  • New techniques in the field are evolving swiftly and expanding the application of Machine learning to nearly boundless possibilities.

  • ML tools enable organizations to identify profitable opportunities and potential risks more quickly.

Why is it the most high-demand skill? Good news for ML enthusiasts

The use of ML & AI by businesses has allowed people to work from home in this pandemic. This shift has also stimulated many businesses, both small-scale and large-scale, to reassess their functioning.

The focus on Machine Learning is a vault to increase considerably in the upcoming years.

According to the latest AI talent report, over the past 3 years alone, the number of AI-related job postings on Indeed has increased by 119 %.

According to a report by Research and Markets, the global machine learning market anticipated growing from $1.4B in 2017 to $8.8B by 2022. In India, job openings for analytics professionals have the highest share at 33.7 percent, followed by machine learning at 20.4 percent and cybersecurity at 15.4 percent.

machine learning course in delhi

Acquire a highly-paid machine learning job post

Now, as you have chosen a machine learning career path, it is crucial to get the theoretical and practical knowledge with hands-on experience.

Many institutes provide machine learning courses, but post-COVID situations will change drastically. That's why it's essential to learn the updated course from industry experts aware of industry requirements.

Madrid Software Trainings provide a machine learning course in Delhi where the trainers are industry professionals. It is the best machine learning institute in Delhi that provides 100% placement assistance.



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Machine Learning Classes in Delhi
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Course Outline

Top Machine Learning institute in Delh

Our Machine Learning Course Is Designed By Industry Experts That Gives The Candidate an Edge In The Market



  •   Introduction To Python

    •    Case Python Concepts

    •   Python Numpy Concepts

    •   Python Pandas Concepts

    •   Fundamentals of Machine Learning

    •   artificial intelligence vs machine learning

    •   Supervised learning

    •   Unsupervised learning

    •   Reinforcement Learning

    •   Scikit Learn Library

    •   Advance Supervised learning concepts like random forest , neural network etc.

    •   Predictive Modelling Concepts

    •   Different Phases of Predictive Modelling

    •   Case Studies



Job Profile And Salaries In Machine Learning



machine learning engineer salary in india

(Source , upgrad)

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26-Oct-2024
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  • Course Curriculum Design by Industry Experts
  • Real Time Assignments Case Study & Projects
  • Got Better Salary Hike and Promotion
  • Industry recognized certificates
  • Mock tests and Mock interview
  • Dedicated placement coordinator assigned to every
        candidate

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Machine Learning Course Interview Q & A


1.Mention 1 advantage and disadvantage of neural networks.

Advantages: Neural networks have led to performance breakthroughs for unstructured datasets such as imagery, audio, and video. Their implausible flexibility allows them to study patterns.

Disadvantages:/ However, they need a large amount of training data to congregate. It's also tricky to pick the accurate architecture, and the internal "hidden" layers are perplexing.

2. what are the differences between supervised learning and unsupervised learning?

In supervised learning, we train a model to find out the relationship between input data and output data. We require labelled data to be able to do supervised learning. With unsupervised learning, we only have unlabeled data. The model learns a representation of the data. Unsupervised learning is chronically used to initialize the parameters of the model when we have a lot of unlabeled data and a little bit of labelled data. We first train an unsupervised model and, afterwards, we use the weights of the model to train a supervised model.

3.What is data augmentation? Give some examples.

Data augmentation is a method for synthesizing new data by modifying existing data in such a way that the target is not changed, or it is changed in a known way. Computer vision is one of the fields where data augmentation is advantageous. There are many modifications that we can do to images:

  • Resize
  • Horizontal or vertical flip
  • Rotate
  • Add noise
  • Deform
  • Modify colours

Each problem requires a customized data augmentation pipeline. For example, on OCR, doing flips changes the text and won't be beneficial; however, resizes and small rotations may help.

4. Explain ensemble learning.

In ensemble learning, many base models like classifiers and regressors are generated and united so that they give better results. It is used when we construct component classifiers that are accurate and independent. There are sequential over and above parallel ensemble methods.

5. Explain the difference between a random forest and gradient boosting algorithm.

Random forest uses bagging techniques, whereas GBM uses boosting techniques. Random forests mainly attempt to diminish variance and GBM reduces both bias and variance of a model.

6.What is the use of Box-Cox transformation?

The Box-Cox transformation is a generalized "power transformation" that transforms data to make the distribution more normal.

For example, when its lambda parameter is 0, it's equivalent to the log-transformation.

It's used to stabilize the variance (eliminate heteroskedasticity) and normalize the distribution.

7. What is the ROC curve, and what is the AUC?

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs False Positive Rate (x- Axis).

AUC is an area under the ROC curve, and it's a standard performance metric for evaluating binary classification models.

It's equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.

8.Define Bagging.

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling. Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models. Bagging is performed in parallel.

9.Why do we require a validation set and test set?

The validation dataset is used to measure how well the model does on examples that weren't part of the training dataset. The metrics computed on the validation data can be used to tune the hyperparameters of the model. However, every time we evaluate the validation data, and we make decisions based on those scores, we are leaking information from the validation data into our model. The more evaluations, the more information is leaked. So we can end up overfitting to the validation data, and once again, the validation score won't be reliable for predicting the behaviour of the model in the real world. The test dataset is used to measure how well the model does on previously unseen examples. It should only be used once we have tuned the parameters using the validation set.

10.What is the advantage of Naive Bayes?

A Naive Bayes classifier converges very quickly as compared to other models like logistic regression. As a result, we need less training data in case of naive Bayes classifiers.

FAQ


1. Who should do a machine learning course?

With the rising demand for AI in a wide range of industries, The machine learning course is for any professional or beginner, though it's best-suited for:

  • Developers aspiring to be a Machine Learning Engineer
  • Analytics Managers leading a team of analysts
  • Analytics experts who would like to work in machine learning
  • Graduates looking to build a career in machine learning
  • Experienced professionals who would like to harness Machine learning in their fields to get more insight

2. Is this useful for non-IT professionals?

All the concepts discussed have been crafted from a basic level to an advanced level with practical implementation at every stage of the course allowing every course applicant to master the skills irrespective of their background.

3. What is the average salary of a machine learning engineer?

According to indeed.com, the average pay a machine learning engineer gets is $142,858. However, it may vary from company to company.

4. What are the top algorithm that every machine learning engineer must know?

Naïve Bayes Classifier Algorithm, K Means Clustering Algorithm, Support Vector Machine Algorithm, Linear Regression Algorithm, Logistic Regression Algorithm, Decision Trees Algorithm, and Random Forests Algorithm are some of the top machine learning algorithms that every machine learning engineer must know.

5. Which is the most widely used programming language in machine learning?

If we look at the overall popularity of machine learning languages, Python leads the pack, with 57 percent of data scientists and machine learning developers using it and 33 percent prioritizing it for development.

6. What are the companies in India to work for after completing the machine learning course?

Many organizations in India hire top experts for Machine Learning. This field attracts jobs from multiple sectors such as recruitment, finance, e-commerce, education, etc. Some of the best companies that recruit for Machine Learning experts include ABB, Accenture, Dell, Juniper Network.

7. Do we get placement support after completing the course?

Yes, Madrid Software Trainings provides placement support to the students who successfully complete the course. We will help you build your resume as per the job profile, take mock interview sessions, and get you interviews in various companies.

8.Do we get online training also machine learning from Madrid Software Trainings?

Yes, since the pandemic arrived, we provide online training with the same quality course curriculum and training environment.

9. what if I miss a class?

All the lectures will be available for the students to view in the Madrid Software Trainings education platform after the class is over. All our live classes are recorded for self-study purposes and future reference. Hence, if you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question at the beginning of the subsequent class.

10.Will I get a completion certificate of machine learning course from Madrid Software Trainings?

Yes, we will provide you with a completion certificate. Students get weekly assignments and module-wise case studies in our course. Once all your submissions are received and evaluated, the certificate shall be awarded. Then the certificate will be issued.

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