Machine Learning Training using Python

Answer: Goal: Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools. Objectives: At the end of this Module, you should be able to: Define Data Science Discuss the era of Data Science Describe the Role of a Data Scientist Illustrate the Life cycle of Data Science List the Tools used in Data Science State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science Topics: What is Data Science? What does Data Science involve? Era of Data Science Business Intelligence vs Data Science Life cycle of Data Science Tools of Data Science Introduction to Python

Answer: Goal: Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format. Objectives: At the end of this Module, you should be able to: Discuss Data Acquisition techniques List the different types of Data Evaluate Input Data Explain the Data Wrangling techniques Discuss Data Exploration Topics: Data Analysis Pipeline What is Data Extraction Types of Data Raw and Processed Data Data Wrangling Exploratory Data Analysis Visualization of Data Hands-On/Demo: Loading different types of dataset in Python Arranging the data Plotting the graphs

Answer: Goal: In this module, you will learn the concept of Machine Learning and it’s types. Objective: At the end of this module, you should be able to: Essential Python Revision Necessary Machine Learning Python libraries Define Machine Learning Discuss Machine Learning Use cases List the categories of Machine Learning Illustrate Supervised Learning Algorithms Identify and recognize machine learning algorithms around us Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization. Topics: Python Revision (numpy, Pandas, scikit learn, matplotlib) What is Machine Learning? Machine Learning Use-Cases Machine Learning Process Flow Machine Learning Categories Linear regression Gradient descent Hands-On: Linear Regression – Using Boston Dataset

Answer: Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc. Objective: At the end of this module, you should be able to: Understand What is Supervised Learning? Illustrate Logistic Regression Define Classification Explain different Types of Classifiers such as Decision Tree and Random Forest Topics: What is Classification and its use cases? What is Decision Tree? Algorithm for Decision Tree Induction Creating a Perfect Decision Tree Confusion Matrix What is Random Forest? Hands-On: Implementation of Logistic regression, Decision tree, Random forest

Answer: Goal: In this module you will learn about impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model. Objective: At the end of this module, you should be able to: Define the importance of Dimensions Explore PCA and its implementation Discuss LDA and its implementation Topics: Introduction to Dimensionality Why Dimensionality Reduction PCA Factor Analysis Scaling dimensional model LDA Hands-On: PCA Scaling

Answer: Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc. Objective: At the end of this module, you should be able to: Understand What is Naïve Bayes Classifier How Naïve Bayes Classifier works? Understand Support Vector Machine Illustrate How Support Vector Machine works? Hyperparameter optimization Topics: What is Naïve Bayes? How Naïve Bayes works? Implementing Naïve Bayes Classifier What is Support Vector Machine? Illustrate how Support Vector Machine works? Hyperparameter optimization Grid Search vs Random Search Implementation of Support Vector Machine for Classification Hands-On: Implementation of Naïve Bayes, SVM

Answer: Goal: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. Objective: At the end of this module, you should be able to: Define Unsupervised Learning Discuss the following Cluster Analysis o K - means Clustering o C - means Clustering o Hierarchical Clustering Topics: What is Clustering & its Use Cases? What is K-means Clustering? How K-means algorithm works? How to do optimal clustering What is C-means Clustering? What is Hierarchical Clustering? How Hierarchical Clustering works? Hands-On: Implementing K-means Clustering Implementing Hierarchical Clustering

Answer: Goal: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm. Objective: At the end of this module, you should be able to: Define Association Rules Learn the backend of recommendation engines and develop your own using python Topics: What are Association Rules? Association Rule Parameters Calculating Association Rule Parameters Recommendation Engines How Recommendation Engines work? Collaborative Filtering Content Based Filtering Hands-On: Apriori Algorithm Market Basket Analysis

Answer: Goal: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent environment interaction. Objective: At the end of this module, you should be able to Explain the concept of Reinforcement Learning Generalize a problem using Reinforcement Learning Explain Markov’s Decision Process Demonstrate Q Learning Topics: What is Reinforcement Learning Why Reinforcement Learning Elements of Reinforcement Learning Exploration vs Exploitation dilemma Epsilon Greedy Algorithm Markov Decision Process (MDP) Q values and V values Q – Learning α values Hands-On: Calculating Reward Discounted Reward Calculating Optimal quantities Implementing Q Learning Setting up an Optimal Action

Answer: Goal: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time dependent data for forecasting. Objective: At the end of this module, you should be able to: Explain Time Series Analysis (TSA) Discuss the need of TSA Describe ARIMA modelling Forecast the time series model Topics: What is Time Series Analysis? Importance of TSA Components of TSA White Noise AR model MA model ARMA model ARIMA model Stationarity ACF & PACF Hands-On: Checking Stationarity Converting a non-stationary data to stationary Implementing Dickey Fuller Test Plot ACF and PACF Generating the ARIMA plot TSA Forecasting

Answer: Goal: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms to stronger ones. Objective: At the end of this module, you should be able to: Discuss Model Selection Define Boosting Express the need of Boosting Explain the working of Boosting algorithm Topics: What is Model Selection? Need of Model Selection Cross – Validation What is Boosting? How Boosting Algorithms work? Types of Boosting Algorithms Adaptive Boosting Hands-On: Cross Validation AdaBoost

Answer: Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session. Objectives: At the end of this module, you should be able to: How to approach a project Hands-On project implementation What Industry expects Industry insights for the Machine Learning domain QA and Doubt Clearing Session

Answer: CertAdda’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in the python programming language to enhance your learning experience.

Answer: Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

Answer: After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present

Answer: CertAdda’s Python Machine Learning Certification Course is a good fit for the below professionals: Developers aspiring to be a ‘Machine Learning Engineer' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'Python' professionals who want to design automatic predictive models

Answer: The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous. You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enroll for the course.

Answer: You don’t have to worry about the System Requirements as you will be doing your Practical on a Cloud LAB environment. This environment already contains all the necessary software that will be required to execute your practicals.

Answer: How will I execute the practicals?

Answer: This course comprises of 34 case studies that will enrich your learning experience. In addition, we also have 3 Projects that will enhance your implementation skills. Below are few case studies which are part of this course: Case Study 1: Maple Leaves Ltd is a start-up company which makes herbs from different types of plants and its leaves. Currently the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of plant family. They have asked us to automate this process and remove any manual intervention from this process. You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference. Case Study 2: BookRent is the largest online and offline book rental chain in India. Company charges a fixed fee per month plus rental per book. So, company makes more money when user rent more books. You as an ML expert and must model recommendation engine so that user gets recommendation of books based on behavior of similar users. This will ensure that users are renting books based on their individual taste. Company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User Based Vs Item Based Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with random forest classifier. Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy. Case Study 4: Principal component analysis using scikit learn. Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy. Using scikit learn perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that has gone wrong. For each of the wrong sample, plot the digit along with predicted and original label. Case Study 5: Read the datafile “letterCG.data” and set all the numerical attributes as features. Split the data in to train and test sets. Fit a sequence of AdaBoostClassifier with varying number of weak learners ranging from 1 to 16, keeping the max_depth as 1. Plot the accuracy on test set against the number of weak learners, using decision tree classifier as the base classifier.

Answer: Project #1: Industry: Social Media Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters. Actions to be performed: Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your goal. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest. Project #2: Industry: FMCG Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years. Actions to be performed: You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components which explain the max variance.

Answer: You will never miss a lecture at CertAdda You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

Answer: Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Answer: Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Answer: Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

Answer: More than 70% of Edureka Learners have reported change in job profile (promotion), work location (onsite), lateral transfers & new job offers. Edureka\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real-life projects you have delivered.

Answer: If you have seen any of our sample class recordings, you don\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'t need to look further. Enrollment is a commitment between you and us where you promise to be a good learner and we promise to provide you the best ecosystem possible for learning. Our sessions are a significant part of your learning, standing on the pillars of learned and helpful instructors, dedicated Personal Learning Managers and interactions with your peers. So experience complete learning instead of a demo session. In any case, you are covered by Edureka Guarantee, our No questions asked, 100% refund policy.

Answer: Learning pedagogy has evolved with the advent of technology over the years. Online training adds convenience and quality to the training module. With our 24x7 support system, our online learners will have someone to help them all the time even after the class ends. This is one of the driving factors to make sure that people achieve their end learning objective. We also provide life-time access of our updated course material to all our learners.

Answer: All the instructors at CertAdda are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

Answer: You can give us a CALL at +91 8178510474 / +91 9967920486 OR email at admin@certadda.com