Machine Learning Course in Chennai

Learn Machine Learning Course in Chennai as the demand for machine learning professionals are high to pave the future of the technical world. It is the exact time for you to equip and train yourself on the most happening skill of Artificial intelligence. Join our best Machine Learning Training Institute in Chennai Velachery, OMR and take a career leap in Machine Learning, Data Analytics, Artificial Intelligence and much more.

About Machine Learning Course

Machine learning is a part of Artificial Intelligence that allows the systems to learn automatically and work better from experience without being programmed. Machine learning algorithms simply focus on computer applications such as detection of network intruders, email filtering and computer vision where it is inapplicable to develop an algorithm of certain instructions for performing the task. It is related to computational statistics that focus on making a prediction using computers. For example, you post a photo and immediately you are given suggestions on whom to tag in the photo.And this easing out most of the day to day activities, Now-a-days, Machine Learning is one of the greatest in-demand technologies budding in the computer industry. You can refer the detailed Machine Learning Course Content below and also can reach us to know more about the Machine Learning course.
On successfully completing our machine learning course, You will be a master in it with the below skills,
  • Master in Supervised and Unsupervised learning concepts and Modeling.
  • Gain knowledge of the mathematical and aspects of machine learning.
  • Understand the operations of Support vector machines, linear regression, decision tree, K-nearest neighbors and K means clustering.
  • Attain practical mastery over the Principles, algorithms and machine learning application.
Machine learning plays a major role in today's world, A pictorial representation of few machine learning applications given below that we use in our everyday lives. Application of Machine learning | Credo Systemz
The graphical representation of Machine Learning benefits given below, Benefits of Machine learning

Key Features

Training from
Industrial Experts

24 x 7
Expert Support

Hands on
Practicals/ Projects

Certification
of Completion

100% Placement
Assistance

Free
Live Demo

MACHINE LEARNING TRAINING COURSE CONTENT

Learning Outcomes of our Machine Learning Course

  • Strong Knowledge of the mathematical and heuristic aspects of Machine Learning.
  • Master in the concepts of supervised, unsupervised, reinforcement modeling and machine learning concepts.
  • Automate data analysis using Python.
  • Good knowledge in theoretically and as well as how to relate with the practical aspects of Machine Learning.
  • Working with a wide variety model of robust machine learning algorithms which including clustering, deep learning and recommendation systems.
  • Be able to analyze the data in various scenarios using Machine learning algorithm.
  • Expertise to handle the business in the future.

    • Machine learning Course Highlights

    • During the course, candidates are inculpated in assignments to provide the real-time exposure, to support them to acquire the confidence to work in real-world industry project.
    • Learn Machine learning training from our Experts and become a Certified Machine Learning Professional.
    • Machine learning course curriculum which matches the current industry standards.
    • Hands-on practical assignments which can be showcased to recruiters.
    • Each and every algorithm is handled with real time scenario.
    • 24/7 support for the candidates in Whatsapp.
    • After Machine learning course completion, your skills will be examines equal to six months of an experienced employee.

    Machine Learning Certification in Chennai

    Credo Systemz is named as the best institute for Machine Learning in Chennai because of our professional training approach towards every individual. Our Machine learning course starts from the basic scratch and includes all the important topics which come under Supervised and Unsupervised Learning. In this machine learning training program, you will effectively learn about the tips and tricks of Machine learning techniques. Our Machine Learning Center in Chennai is ranked as the No.1 Training Institute for Artificial Intelligence and Machine Learning. As an individual, after completing your Machine Learning certification at Credo Systemz you will learn to automate your systems to perform on its own. To know about our machine learning course fees and to book a free demo session please fill up the quick enquiry form.

Course Features

  • Duration60 hours
  • Skill levelAll level
  • Batch Strength15
  • AssessmentsYes
  • Mock InterviewsYes
  • Resume BuildingYes
  • PlacementsYes
  • Flexible TimingYes
  • Fee InstallmentsYes
  • LanguageTamil/English
Section 1: Introduction to ML
  • What is ML?
  • Why ML?
  • Opportunities in ML
  • What is ML models?
  • Why R and Python is popular?
Section 2: ML Model Overview
  • Introduction to ML Model.
  • Data Handling
  • Data Pre-processing
  • Types of ML Model.
  • Supervised and Unsupervised.
  • How to test your Data?
  • Cross validation techniques
Section 3: Linear Regression
  • What is Linear Regression?
  • Gradient Descent overview.
  • Gradient Descent Calculations.
  • R and Python Overview.
  • How to improve your model?
Section 4: Overfitting
  • Overfitting Overview
  • How to use Linear Regression for Overfitting?
  • How to avoid Overfitting?
  • Bias-Variance Tradeoff.
  • Regularization - Ridge, LASSO
  • ANOVA, F tests overview.
  • What is Logistic Regression?
  • Classification with Logistic Regression.
  • Maximum Likelihood Estimation.
  • Build an end to end model with Logistic Regression using scikit Learn.
  • How to build a model in the Industry?
Section 5: Decision Trees
  • Why Decision Tree?
  • Entropy, Gini Impurity overview
  • Implement Overfitting.
  • How to improve the Decision Tree model without Overfitting?
  • Bagging, Boosting
  • Random Forest
  • AdaBoost, Gradient Boost
Section 6: k-NN
  • Distance based model with kNN.
  • Value of k - overview.
Section 7: Support Vector Machines(SVM)
  • Power of SVM overview.
  • Why SVM?
  • What is Kernel Functions?
  • What are the Kernel Functions available?
  • How to Build an OCR(Optical Character Reader) with the help of SVM and Kernel functions?
  • Neural Networks overview.
  • Why Neural Networks?
  • What is Neural Network Architecture?
  • How to build AND, OR, NOT, XOR, XNOR Logic Gates with Neural Network?
  • What is Forward & Backward Propagation?
  • List of Activation Functions.
  • Vanishing Gradient problem
Section 8: Deep Neural Networks
  • Optimization methods overview.
  • Gradient Descent with Momentum, RMSProp, ADAM.
  • Learning Rate Decay.
  • Xavier Initialization.
  • Introduction to Keras and Tensorflow(TF)
  • Deep Learning in Keras with TensorFlow as the backend.
Section 9: Unsupervised Learning
  • Clustering overview.
  • k-means Clustering.
  • Hierarchical clustering.
Section 10: PCA
  • Principal Component Analysis(PCA).
  • Maths behind PCA.
  • Engine Recommendation.
  • Content and Collaborative Filtering.
  • Market Basket Analysis
  • What is Apriori Rule?
Section 11: Computer Vision
  • Image Detection, Image Classification, Localization.
  • Convolutional Neural Networks(CNN) overview.
  • Strides, Padding methods
  • Convolutional, Padding and Fully Connected layers
  • Sliding Window
  • Edge Detection
Section 12: Advanced Computer Vision
  • YOLO ALgorithm - You Only Look Once
  • Introduction to classical networks like LeNet5
  • IoU
  • Introduction to Natural Language Processing(NLP)
  • Text Preprocessing
  • Lemmatization, Stemming
  • Syntactical Parsing, Entity Parsing
  • Develop a chatbot with the above concepts of NLP and Neural Networks

You will be going through detailed 2 to 3 months of Machine Learning Hands-on training

  • Detailed instructor led sessions to help you become a proficient Expert in Machine Learning.
  • Build a Machine Learning professional portfolio by working on hands on assignments and projects.
  • Personalised mentorship from professionals working in leading companies.
  • Lifetime access to downloadable Machine Learning course materials, interview questions and project resources.
Credo Systemz Velachery

Credo Systemz - Velachery, Chennai
Call Us +91 9884412301

Credo Systemz OMR

Credo Systemz - OMR, Chennai
Call Us +91 9600112302

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    Machine Learning with various languages

    Credo Systemz offering Machine Learning course with below programming languages,
    • Machine Learning with Python
    • Machine Learning with Tensorflow
    • Machine Learning with JavaScript
    • Machine Learning with Java
    • Machine Learning with R
    • Machine Learning with AI
    Contact us for know more details about it.

    Top MNC Interview Questions

    1. What’s the trade-off between bias and variance?
    2. What is the difference between supervised and unsupervised machine learning?
    3. How is KNN different from k-means clustering?
    4. Explain how a ROC curve works.
    5. What is Bayes’ Theorem? How is it useful in a machine learning context?
    6. Why is “Naive” Bayes naive?
    7. Explain the difference between L1 and L2 regularization
    8. What’s your favorite algorithm, and can you explain it to me in less than a minute?
    9. What’s the difference between Type I and Type II error?
    10. What’s the difference between probability and likelihood?
    11. What is deep learning, and how does it contrast with other machine learning algorithms?
    12. What cross-validation technique would you use on a time series dataset?
    13. How is a decision tree pruned?
    14. Which is more important to you– model accuracy, or model performance?
    1. How do you handle missing or corrupted data in a dataset?
    2. Do you have experience with Spark or big data tools for machine learning?
    3. Pick an algorithm. Write the psuedo-code for a parallel implementation.
    4. What are some differences between a linked list and an array?
    5. Describe a hash table.
    6. Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
    7. How can we use your machine learning skills to generate revenue?
    8. What do you think of our current data process?
    9. What are the last machine learning papers you’ve read?
    10. Do you have research experience in machine learning?
    11. What are your favorite use cases of machine learning models?
    12. How would you approach the “Netflix Prize” competition?
    13. How do you think Google is training data for self-driving cars?
    1. How would you explain Machine Learning to a school-going kid?
    2. How does Deep Learning differ from Machine Learning?
    3. Explain Classification and Regression
    4. What do you understand by selection bias?
    5. What do you understand by Precision and Recall?
    6. What is a Confusion Matrix?
    7. What is the difference between inductive and deductive learning?
    8. How is KNN different from K-means clustering?
    9. What is ROC curve and what does it represent?
    10. What’s the difference between Type I and Type II error?
    11. Is it better to have too many false positives or too many false negatives? Explain.
    12. Which is more important to you – model accuracy or model performance?
    13. What is the difference between Entropy and Information Gain?
    14. Explain Ensemble learning technique in Machine Learning.
    15. What is bagging and boosting in Machine Learning?
    16. What are collinearity and multicollinearity?
    1. What is A/B Testing?
    2. What is Cluster Sampling?
    3. Name a few libraries in Python used for Data Analysis and Scientific Computations.
    4. How are NumPy and SciPy related?
    5. What is the main difference between a Pandas series and a single-column DataFrame in Python?
    6. How can you handle duplicate values in a dataset for a variable in Python?
    7. How do you map nicknames (Pete, Andy, Nick, Rob, etc) to real names?
    8. Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?
    9. Why is naive Bayes so ‘naive’ ?
    10. How is kNN different from kmeans clustering?
    11. How is True Positive Rate and Recall related? Write the equation.
    12. When is Ridge regression favorable over Lasso regression?
    13. While working on a data set, how do you select important variables? Explain your methods.
    14. What is the difference between covariance and correlation?
    1. What is the difference between covariance and correlation?
    2. What is convex hull ?
    3. What cross validation technique would you use on time series data set? Is it k-fold or LOOCV?
    4. What do you understand by Type I vs Type II error ?
    5. When does regularization becomes necessary in Machine Learning?
    6. What do you understand by Bias Variance trade off?
    7. What Is Bias and Variance in a Machine Learning Model?
    8. What Is the Trade-off Between Bias and Variance?
    9. Define Precision and Recall.
    10. What Is Decision Tree Classification?
    11. What Is Pruning in Decision Trees, and How Is It Done?
    12. Briefly Explain Logistic Regression.
    13. Explain the K Nearest Neighbor Algorithm.
    14. What Is a Recommendation System?
    15. What Is Kernel SVM?
    1. What Are the Different Types of Machine Learning?
    2. What Is Overfitting, and How Can You Avoid It?
    3. What Is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
    4. How Do You Handle Missing or Corrupted Data in a Dataset?
    5. How Can You Choose a Classifier Based on a Training Set Data Size?
    6. Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
    7. What Is a False Positive and False Negative and How Are They Significant?
    8. What Are the Three Stages of Building a Model in Machine Learning?
    9. What Is Deep Learning?
    10. What Are the Differences Between Machine Learning and Deep Learning?
    11. What Are the Applications of Supervised Machine Learning in Modern Businesses?
    12. What Is Semi-supervised Machine Learning?
    13. What Are Unsupervised Machine Learning Techniques?
    14. What Is the Difference Between Supervised and Unsupervised Machine Learning?
    15. Compare K-means and KNN Algorithms.
    1. What Are Unsupervised Machine Learning Techniques?
    2. What Is the Difference Between Supervised and Unsupervised Machine Learning?
    3. What Is the Difference Between Inductive Machine Learning and Deductive Machine Learning?
    4. Compare K-means and KNN Algorithms.
    5. What Is ‘naive’ in the Naive Bayes Classifier?
    6. Explain How a System Can Play a Game of Chess Using Reinforcement Learning.
    7. How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
    8. When Will You Use Classification over Regression?
    9. How Do You Design an Email Spam Filter?
    10. What Is a Random Forest?
    11. Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use?
    12. What Is Bias and Variance in a Machine Learning Model?
    13. What Is the Trade-off Between Bias and Variance?
    14. Define Precision and Recall.
    15. What Is Decision Tree Classification?
    1. Explain the difference between supervised and unsupervised machine learning?
    2. Explain the difference between KNN and k.means clustering?
    3. What is the difference between classification and regression?
    4. How to ensure that your model is not overfitting?
    5. What is meant by ‘Training set’ and ‘Test Set’?
    6. List the main advantage of Navie Bayes?
    7. Explain Ensemble learning.
    8. Explain dimension reduction in machine learning.
    9. What should you do when your model is suffering from low bias and high variance? Explain differences between random forest and gradient boosting algorithm.
    10. What is the "Curse of Dimensionality?"
    11. Explain the Bias-Variance Tradeoff.
    1. What is Machine learning?
    2. Mention the difference between Data Mining and Machine learning?
    3. What is ‘Overfitting’ in Machine learning?
    4. Why overfitting happens?
    5. How can you avoid overfitting ?
    6. What is inductive machine learning?
    7. What are the five popular algorithms of Machine Learning?
    8. What are the different Algorithm techniques in Machine Learning?
    9. What are the three stages to build the hypotheses or model in machine learning?
    10. What is the standard approach to supervised learning?
    11. What is ‘Training set’ and ‘Test set’?
    12. List down various approaches for machine learning?
    13. Explain what is the function of ‘Unsupervised Learning’?
    14. Explain what is the function of ‘Supervised Learning’?
    15. What is algorithm independent machine learning?
    16. What is the difference between artificial learning and machine learning?
    1. What is classifier in machine learning?
    2. What are the advantages of Naive Bayes?
    3. In what areas Pattern Recognition is used?
    4. What is Genetic Programming?
    5. What is Inductive Logic Programming in Machine Learning?
    6. What is Model Selection in Machine Learning?
    7. What are the two methods used for the calibration in Supervised Learning?
    8. Which method is frequently used to prevent overfitting?
    9. What is the difference between heuristic for rule learning and heuristics for decision trees?
    10. What is Perceptron in Machine Learning?
    11. Explain the two components of Bayesian logic program?
    12. What are Bayesian Networks (BN) ?
    13. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?
    14. What are the two classification methods that SVM ( Support Vector Machine) can handle?
    15. What is ensemble learning?
    16. Why ensemble learning is used?
    1. What is Perceptron in Machine Learning?
    2. Explain the two components of Bayesian logic program?
    3. What are Bayesian Networks (BN) ?
    4. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?
    5. What are the two classification methods that SVM ( Support Vector Machine) can handle?
    6. What is ensemble learning?
    7. Why ensemble learning is used?
    8. What is dimension reduction in Machine Learning?
    9. What are the different methods for Sequential Supervised Learning?
    10. What is inductive machine learning?
    11. What are the five popular algorithms of Machine Learning?
    12. What are the different Algorithm techniques in Machine Learning?
    13. What are the three stages to build the hypotheses or model in machine learning?
    14. What is ‘Training set’ and ‘Test set’?
    1. List down various approaches for machine learning?
    2. What is Inductive Logic Programming in Machine Learning?
    3. What is Model Selection in Machine Learning?
    4. What is Perceptron in Machine Learning?
    5. What are the different categories you can categorized the sequence learning process?
    6. What is sequence learning?
    7. What are two techniques of Machine Learning ?
    8. What is the difference between Bias and Variance?
    9. What is the difference between supervised and unsupervised machine learning?
    10. How is KNN different from K-means clustering?
    11. Comparision between Machine Learning and Big Data
    12. Explain what is precision and Recall?
    13. What is your favorite algorithm and also explain the algorithm in briefly in a minute?
    14. What is the difference between Type 1 and Type 2 errors?
    1. What is deep learning?
    2. How to handle or missing data in a dataset?
    3. What is your favorite use case for machine learning models?
    4. What is the difference between Machine learning and Data Mining?
    5. What is inductive machine learning?
    6. Please state few popular Machine Learning algorithms?
    7. What are the different types of algorithm techniques are available in machine learning?
    8. What are the three stages to build the model in machine learning:
    9. Explain How We Can Capture The Correlation Between Continuous And Categorical Variable?
    10. How To Handle Or Missing Data In A Dataset?
    11. Define A Hash Table?
    12. Mention Any One Of The Data Visualization Tools That You Are Familiar With?
    13. What Is The Difference Between Bias And Variance?
    14. What do you understand by Machine learning?
    15. How is KNN different from k-means?
    1. What do you understand by Machine learning?
    2. Differentiate between inductive learning and deductive learning?
    3. What is the difference between Data Mining and Machine Learning?
    4. What is the meaning of Overfitting in Machine learning?
    5. Differentiate supervised and unsupervised machine learning.
    6. How does Machine Learning differ from Deep Learning?
    7. How is KNN different from k-means?
    8. What are the different types of Algorithm methods in Machine Learning?
    9. What do you understand by Reinforcement Learning technique?
    10. What do you mean by ensemble learning?
    11. What is a model selection in Machine Learning?
    12. Describe 'Training set' and 'training Test'.
    13. What do you understand by ILP?
    14. What are the functions of Supervised Learning?
    1. Describe the classifier in machine learning.
    2. What is Bagging and Boosting?
    3. What is the difference between supervised and unsupervised machine learning?
    4. What’s the trade-off between bias and variance?
    5. How KNN is different from k-means clustering?
    6. What is Bayes’ Theorem? How it is useful?
    7. What is the difference between L1 and L2 regularization?
    8. What Deep Learning is exactly?
    9. What is Bias error in ML algorithms?
    10. What is the meaning of Variance Error in ML algorithms?
    11. What is the importance of Bayes’ theorem in ML algorithms?
    12. What is the difference between deep learning and machine learning?
    13. What is machine learning?
    14. How is data mining different from machine learning?
    15. What are the different types of machine learning?
    16. Define overfitting in machine learning.
    17. Name the five most popular machine learning algorithms.
    18. What are the different approaches for machine learning?
    Get Answer for all the above questions and place in your dream company

    Upcoming Batch Details

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    Machine Learning Training – Online & Classroom
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    Chennai
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    Trainer Profile of Machine Learning Training in Chennai

    • Credo systemz Machine Learning trainers assist in developing the skill set required for Machine learning experts with theoretical and practical knowledge which covers all the important Machine Learning concepts to the students.
    • Our Machine Learning Trainers are Expert in the Machine Learning platform with 10+ years of experience and part of the top MNC’s in IT field who are passionate and dedicated towards training the aspirants using our Machine learning Course in Chennai.
    • Our Trainers enhance the knowledge of the aspirants by providing the entire Machine learning concepts, neural networks, Machine Learning Algorithms with python and R programming up to industrial standard.
    • Our trainers are friendly, readily available by providing individual attention to each aspirant and encouraging to eliminate the difficulty in coding.
    • Our Machine learning trainers perform assessments, code reviews, and help in completing projects by developing all the necessary machine learning. So our Machine learning Course in Chennai is preferred by all.
    • Our Trainers share their industrial experience, recent interview questions, and answers and also helps in professional resume building and improve the confidence that helps to crack the interview.

    Join the Best Machine Learning Training Institute in Chennai

    Why join Credo Systemz for Machine Learning ?
    • Credo Systemz is ranked as the No.1 Machine Learning Training institute in Chennai based on the positive reviews from our students with a successful training record of more than 1500+ IT professionals.
    • Our Machine Learning course in Chennai stands out based on the Detailed Course Plan to help both Fresher & Experienced Professionals which is prepared by our expert trainer team.
    • Each training session includes both Theoretical & mandatory practical programs development as well to experience the real environment.
    • Skilled & expert trainers with 10+ years of industry experience with flexible training pattern, allowing candidates to choose between week day or week end sessions.
    • Our Machine Learning training in Chennai covers all the important aspects of machine learning, neural networks, algorithms and much more.
    • Our trainers demonstrate every concept through hands-on approach, which helps to apply machine learning techniques to solve real world problems easily.
    • Complete Hands-on Machine Learning training in Chennai with 100% Placement Assistance.
    • To cater to any special needs of the candidates, we offer an Online Machine Learning Training program with live trainers as well.
    • Our Machine Learning training in Chennai provides Professional teams to assist with Career guidance, Interview preparation, Mock Interviews, Placement Counselling, and Certification Assistance, Resume writing and Job updates.
    • Ranked as Best Machine Learning Training Institute in Chennai providing best Artificial Intelligence and Machine Learning training in the city with nominal course fees. Above all, attend the 1st session for free!!
    • Credo Systemz is the Best Institute to learn Machine Learning in Chennai.

    To know more about our Machine Learning course, feel free to attend our free Machine Learning workshop or demo sessions and discuss with our consultant to know more about the topics, case studies and live Machine Learning projects that are included in this Machine Learning training program.

    Career Opportunities of Machine Learning

    In recent times, Machine learning is the most in demand and rapidly growing career option in India and throughout the world. It is because most of the companies are incorporating Artificial intelligence and Machine learning to transform their systems to be smarter and efficient. Machine learning is the skill of the present and future because of this transition which leads to exponential career growth. It has many best options to move like Data science, big data and much more with the right set of skills. Machine learning jobs are the highly promising positions that are growing upwards.

    As the demand for machine learning professionals is high, the companies are looking for people with the important skills who enhance the organisations overall productivity using timely analysis, right time predictions and transforming industries with valuable insights.In Credo Systemz, Our Machine learning training in Chennai makes sure that our aspirants acquire all the necessary skills to grab their dream job.

    Job Roles

    In machine learning, many career options are available to work on real challenges and few of them are,
    • Software Engineer/Developer
    • Data Scientist
    • Artificial Intelligence Engineer
    • Designer in Human-Centred Machine Learning
    • Machine Learning and Automation Expert
    • NLP Engineer

    Salary details in India

    The salary of any machine learning professional depends on many factors like skill set, experience, company and its location. The basic salary of a machine learning engineer in India is about Rs.700000 per annum. Start your career with the help of our Machine learning training in Chennai.

    FAQ

    This being the most frequently asked question by our joiners enquiring on our Machine Learning training in Chennai, we have an answer …
    • Basic understanding of any one programming language
    • Good knowledge on mathematics and statistics concepts
    Machine Learning Classroom Training in Chennai:
    • Regular classroom based training: It takes 45 hours of machine learning course.
    • Fast Track (1-1 : Machine Learning crash course in Chennai ) : 15 days.
    • Choose your options for week day or week end classroom training.
    • Online training - please send us your request to info@credosystemz.com or call + 91 9884412301 / + 91 9600112302.
    Graphical representation of our course journey given below, Machine Learning Training in Chennai
    • Assessments - Our training pattern includes conducting frequent assessments to understand your technical competence & brief your areas of improvement, during the tenure of the course.
    • Interview Questionnaire - At Credo Systemz, a dedicated team is available to collate the frequently asked questions in the Top MNCs, and will share the questionnaire with our candidates.
    • Mock Interviews - At the end of the course, a team of highly qualified real-time IT technical experts from the industry assess your knowledge on the language, by conducting interviews, and share the feedback so you can crack the interview with confidence & at ease.
    • Resume Building Services – Expert trainers guide our candidates on the Resume preparation which will profoundly help you put your career on track.
    Check this Top 100 Machine Learning Interview Questions and Answers to crack the interview easily
    Credo Systemz offers Class room training, online training and Corporate Training. The training will be provided by expert trainers having more than 10+years IT experience currently working in the Industry.
    Book Your Free Demo Session: +91-9884412301
    NO HARD!

    Our Machine learning in Chennai starts with very basics which makes it easy for beginners into advanced levels. . In 45 Hours of course duration, you will become an Expert in Machine Learning.
    Yes, after the completion of Machine learning training, you will become a certified Machine learning professional who is an expert in Statistics , algorithms development using Python & R to complete Real world Projects without anyone’s guidance/support and to be the part of top MNC’s
    SIMPLE..!

    Just give a call to + 91 9884412301 / + 91 9600112302 or fill up the form in the sidebar to get a call from our admin team, they will provide you with the required details about our next batch and registration process for our Machine learning training in Chennai.
    Of course. It’s easy! You can contact us anytime to attend a full live classroom session or live online session and interact with our trainer. You can clarify all our doubts without paying anything. Feel free to call us to get a clear idea about our Machine learning training in Chennai.

    To book:

    Velachery + 91 9884412301

    OMR + 91 9600112302

    (Also available in WhatsApp)
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    Credo Systemz allows you to select your preferred payment via Cash, Card, Cheque and UPI services.
    To know about our exciting offers, concessions and group discounts. Call us now: + 91 9884412301 / + 91 9600112302
    You can attend your missed sessions with upcoming Machine learning Course batches. Our admin team arranges a compensation session within the batch or the next available batch. Also, we provide a recorded video of our live session for your reference.
    It’s a big YES!

    Our live trainer uses online tools and techniques with effective online live presentation which improves online training experience. Students can view, interact, and clarify doubts arising during the presentation.

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    Nearby Access Areas
    Our Velachery and OMR branches are very nearby access to the below locations.
    Medavakkam, Adyar, Tambaram, Adambakkam, OMR, Anna Salai, Velachery, Ambattur, Ekkattuthangal, Ashok Nagar, Poonamallee, Aminjikarai, Perambur, Anna Nagar, Kodambakkam, Besant Nagar, Purasaiwakkam, Chromepet, Teynampet, Choolaimedu, Madipakkam, Guindy, Navalur, Egmore, Triplicane, K.K. Nagar, Nandanam, Koyambedu, Valasaravakkam, Kilpauk, T.Nagar, Meenambakkam, Thiruvanmiyur, Nungambakkam, Thoraipakkam, Nanganallur, St.Thomas Mount, Mylapore, Pallikaranai, Pallavaram, Porur, Saidapet, Virugambakkam, Siruseri, Perungudi, Vadapalani, Villivakkam, West Mambalam, Sholinganallur.
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