# Machine Learning Course in Chennai

Are you fascinated in learning Machine Learning Course? Your wait is over. Unite with FITA for **Machine Learning Course in Chennai**. Flexible Timings! Most Affordable Rate with 100% Placement! Stick with us for **Machine Learning Training in Chennai** and obtain Machine Learning Certification.

## What is Machine Learning?

Machine Learning is a type of Artificial Intelligence (AI) that authorize software applications to be more exact in predicting outcomes without programmed explicitly. Machine learning is a procedure of data analysis that automates analytical model building which is a branch of AI. The idea is based on which computers should adapt learn and gain experience. Machine Learning focuses on computer applications to access data and use it by themselves.

## Why do students prefer Machine Learning Course in Chennai?

You can attain an advanced level of Machine Learning Algorithm and application like clustering, classification, regression and prediction through **Machine learning Certification Training**. The training covers deep learning and Spark Machine Learning. The chief aim is to allow computers to automatically learn without intervention and balanced actions accordingly.

## Why FITA?

Numerous checklist is ticked for the process of selecting any institute before joining course. As an educational institute, we try to fulfill all those expectations of our students. It is very difficult to get a smart and approachable faculty, but we handpicked our trainers from the industry for the welfare of our students. Enroll yourself at **Machine Learning institute in Chennai** for classes.

Next comes the turn of infrastructure, we have made all the necessary facilities available at a very accessible location. Our faculties understand the importance of hands-on experience. Thus, equal importance is given to both theory and practical learning.

Now, comes the most important part of the training, placement and we have a specialist team who will help you fetch all the necessary information regarding interviews scheduled. We offer the study materials related to the course and guide you even after the course completion. Learn **Machine Learning course in Chennai** at FITA for an one-stop solution for your career.

## What will students get if they pay for the course?

Course schedules are flexible and you will have to access all the features and content to earn a course certificate. You will require session-based courses that acquire you to meet deadlines to stay on track. Even if you fail in it, later sessions will be conducted so that you can complete the work.

## What does FITA do?

With more practical session by our well-versed tutors, students obtain more knowledge. Students are made to do real time projects under the guidance of our trainers. Our commitment doesn’t get concluded within the classes, but we do provide extra classes for the sake of slow-going scholars.

## Machine Learning Syllabus

### Introduction

**Linear Classification**

**Perceptron update rule**

**Perceptron convergence**

**Generalization**

**Maximum margin classification**

**Classification errors**

**Regularization**

**Logistic regression**

**Linear regression, estimator bias and variance, active learning**

**Kernal regression**

**Support vector machine (SVM) and kernels**

**Kernel optimization**

**Model selection**

**Model selection criteria**

**Description length, feature selection**

**Combining classifiers, boosting**

**Boosting, margin, and complexity**

**Margin and generalization, mixture models**

**Mixtures and the expectation maximization (EM) algorithm**

**EM, regularization, clustering**

**Clustering**

**Spectral clustering, Markov models**

**Hidden Markov models (HMMs)**

**Bayesian networks**

**Learning Bayesian networks**

**Probabilistic inference**

**Guest lecture on collaborative filtering**

**Current problems in machine learning, wrap up**

## Benefits of Machine learning

The research and marketing division is in drastic need of people with machine learning knowledge. The giant corporations like Microsoft, Google, Amazon, IBM and Intel have proposed investment plans for the machine learning.

## Future Scope of Machine Learning

Machine learning is still a complex demon. The husky form of **Machine Learning Chennai** is “deep learning” which forms a mathematical structure called neutral network based on vast quantities of data. The future of Machine learning is very bright. It is considered as an incredibly powerful tool because Machine Learning can solve problems which couldn’t be solved.

## Machine Learning Industry Updates

Machine learning is the branch of data science where the performance of the machine is used to analyze the data associated with the machines. Machine learning is widely used by giant tech companies like Google, Azure and Amazon. We would like to educate the students regarding industry exposure and how these companies use machine learning for the betterment. Algorithms with machine learning models will be 200 times faster when compared to the traditional model algorithms. The performance and the need for the analysis thrust the demand to the machine learning. Join **Machine Learning Training in Chennai** to foster job opportunities in the job market.

**Google**

Tensor flow is the platform with an open source model to build and deploy the ML models. Tensor flow offers a high-level API to practice the machine learning models. If the Machine learning model is of big size then the distribution API is used. The large ML tasks will demand the different hardware configurations and the model definition will be the same. Google has joined hands with multiple organizations to make projects out of machine learning. Detecting the fishing activity and deforestation is one of the projects with a social goal from Google. **Machine learning course** is the best course for beginners.

The JavaScript environment is used by the Tensorflow.js for the deployment of the models. The models are developed through the direct path on the servers, web or edge devices. For creating complex topologies the Keras and the model subclassing is used. Tensor Flow is for the general projects and there are options for the mobile-based projects and the JavaScript-based projects. TensoFlow.js is used for JavaScript based projects and Tensor Flow lite is for the devices like IOS, Android, Raspberry Pi and Edge TPU. **Machine Learning Training in Chennai** is conducted with the experienced trainers and they provide ideas for the right path to the customers.

**Azure**

The custom code with the studio of azure machine learning is explained to the beginner with the help of the packages of the Azure. For the analyst, the design, simple interface with drag and drop option and deployment are easily understood with Azure learning. The Azure market place and the APIs are for the data science developers.

At Azure web services there are a variety of models to conduct the analysis using the canvas of Azure. The models are used to input the data, manipulate the data, conduct training with the machine learning algorithms, value the model, analyze the results from the model, and get final values as output. When developing and deploying the solutions using the predictive web services the process is to train the model, analyze the experiment and make the web service as operational. **Machine Learning Course** also covers the knowledge of R programming or python as a part of programming language.

**Training**

The first level of developing web services is training the experiment. The single model or the multiple models are trained to arrive at the solution. After deciding the model with the help of the result the single model is taken and the rest models are eliminated.

Converting the training model in a predictive model is called a predictive experiment. After the predictive experiment, the model trained with new data become the operationalized azure web services. The different modules are saved as the single module; eliminate the unwanted models, the input and output for the use of web services. Non-predictive models are deployed as web services.

MS-Azure helps to retrain the model with the new data. The changes can be made while the web service is running, the training model is not linked to the web services and it is easy to make changes through the training model. By saving the changes the new data is created in the model. **Machine Learning Training in Chennai** will help for the development, deployment and maintenance issues with the application.

**Amazon**

Amazon sage maker is used to develop and deploy the machine learning models. Training the data with Amazon will reduce the cost of data labeling by 70 percent. The concept behind the training is training once and run with multiple hardware configurations with high-level performance. The auto-scaling clusters are used to deploy the model and deliver to multiple zones with high availability and performance.

**Machine Learning Course in Chennai** is an interesting subject which is used in diversified platforms for data analysis. Learn the Machine Learning Course in Chennai at FITA to master the skills required to become an efficient data analyst. Different types of algorithms are produced with the help of machine learning to improve the analysis. Density function algorithm, statespaceforcat, experiment management framework, and 3D scatter plot visualization are some of the algorithms in the machine learning field which created the multiple channels and job opportunities.

## Different types of machine learning algorithms

Machine learning is about analyzing the patterns in big data which is helpful for the machines to produce effective decisions. Python, SAS and R are the different types of programming languages used for designing the machine learning algorithm. Data scientist, quantitative analyst, software engineer, data analyst, systems engineer, computer vision engineer, deep learning engineer and software developer are the different names of the same job which deals with the machine learning. Let us deep dive into the different machine learning algorithms and the methodologies used in them. Join the **Machine Learning Course** at FITA for comprehensive knowledge into the technology.

**Three concepts of algorithms**

The different types of algorithm can be grouped as three types and they have supervised learning, reinforcement learning and unsupervised learning. **Machine Learning Course in Chennai** is the best course for the bright future with potential growth. Examples of supervised learning are Regression, random forest, KNN, decision tree, and logistic regression. The second type of algorithm widely used as unsupervised learning. The examples of unsupervised learning are K-means and Apriori algorithm. The reinforcement learning works with the model of trial and mistakes from the trial. This algorithm learn from the past experience and arrives at a decision with the mistakes occurred. Decisions are the part of the results arrived during the past activities. Examples of these types of algorithm are the Markov decision process. **Machine Learning Training in Chennai** at FITA will helps you to understand the different algorithms required for machine learning.

**Linear regression**

In this model of the algorithm the estimation of the values is done with the relationship between the dependent variable and the independents variable. The best line is called as a regression line and the formula for this is Y=a*X+b. Y stands for the dependent variable, X stands for the independent variable, a stands for the Slope, and b stands for the intercept. The best fit line is arrived using the equation and the other details are arrived after fitting the best line. Simple and multiple are the two variations in the linear regression. In the case of the simple method, there will be only one independent variable and in case of the multiple methods, there will be more than one independent variable.

**Logistic Regression**

Logistic regression is a part of the regression algorithm. It predicts the probability of the occurrence. The logic of occurrence is used to arrive at the final decision. In the case of the ordinary regression the sample values are minimized with the errors and in case of logistic regression the parameters to arrive maximum sample values are used. Join FITA to get the **Machine Learning Certification** in a professional institute.

**Decision tree**

A decision tree is the concept of classification of the problem. This method uses continuous dependent variables and categorical variables. The decision is arrived by playing the Microsoft game called jezzball. To group the given data it uses methods like Gini, information gain, entropy and Chi-square.

**SVM**

SVM is the classification of the data with the n-dimensional space and the coordinates are known as the support vectors. The new data can be classifieds as the groups and the black line. Depending upon the landing of the test data the class of the new data is classified. The options are segregated and checked for the movement among the options.

**Naive Bayes**

This algorithm follows the concept of Bayes theorem. This is the classification method which looks into the physical feature and does the classification. The feature of apple is red, 3 inches size and round in nature. If all these characteristics are met then the classification for the apple fruit is done. This method is simple and performs well among all the other classification method.

**KNN method**

This algorithm is used for the classification and it classifies the new cases with the voting majority. K is the biggest challenge when doing KNN modeling. KNN is expensive, normal values are taken for the variables to avoid the bias, and this works on the preprocessing stage. **Machine Learning Training in Chennai **at FITA is helpful to get the fundamental knowledge in machine learning.

**K- Means **

K-Means is a sort of unsupervised algorithm which follows a simple and easy way of classification with the use of clusters. The data points use the closest centroids and the existing cluster member is used to find the centroid. Every cluster has its own centroid. The total sum of the square constitutes inside centroid and data points. If the cluster increases the value will decrease and arrive at the value of K.

**Random Forest**

The decision trees are collected and assembled in the Random forest. This is helpful for the classification through a new object based on attributes. The sample for the training set is taken from the number of cases and the value of M is said as constant. Each tree is filled with data and decisions.

**Dimensionality reduction**

The algorithm which captures all the details like the like, dislike, purchases, feedback, crawling history, demographics, and personalized attention is called a dimensionality reduction algorithm. This helps for the other algorithms like PCA, Random forest, decision tree, and factor analysis.

**GBM**

GBM is the algorithm used over plenty of data with high prediction. Multiple weak predictors are assembled together to create the predictor. GRM is used for the data science project for the Kaggle, Crowd analytics and AV hackathon. Obtain the **Machine ****Learning Certification **in Chennai from FITA to climb high in the ladder of the job profiles.

Become a **Machine Learning** Professional. Explore the New Technology by getting connected with FITA for **Machine Learning Course in Chennai**. For more information enroll with **FITA.**

## Other Related Trainings

### Student Testimonial

It was very great days with the FITA. I was in the machine learning training program in the institution so, the clasaes was good and mostly i like the environment of the institutions.

The machine learning class which I attended in FITA was really helpful and the training was conducted professionally.Class timings are flexible.

#### Machine Learning Interview Questions

Machine learning is a branch subject in the data science. To educate the students to take up the interview with confidence and avoid the stumped experience at the interview place we have collected the set of frequently asked interview questions to enrich the knowledge of the students. Prepare yourself with the confidence needed to win over the difficult scenarios in the interview. We present you the curated questions to fuel your knowledge and then join the race in the interviews with desired answers. Machine learning questions can be sub divided as algorithm based, programming based and industry based. As machine learning is widely used, the industrial knowledge is also essential to equip yourself for the interview. Let us deep dive in to the topic and provide you the interview questions for the preparation. Join the **Machine Learning Training in Chennai **at FITA to gain in-depth knowledge in to the technology and become an expert.

**Differentiate variance and bias in machine learning?**

These two are two different concepts in machine learning. Bias refers to the simple algorithm used for the learning and training whereas variance refers to the complex algorithm used for the learning and training in the machine learning. Bias under fit the project due to lack of accuracy whereas variance refer to over fit of the algorithm due to sensitivity with the high degree of variation with respect to the training data. The mixture of these two compositions is used in the project to reduce the error and manage the complexity in the application. Join the **Machine Learning Course in Chennai** to avail the huge opportunities in the job market and climb up high in the professional ladder.

**What do you infer from the learning in the context of machine learning?**

The labeled data are trained in the supervised learning whereas in case of unsupervised learning no need to classify the data to label them. The classification of data and labeling the data are trained in case of the supervised learning.

**Explain the process of ROC curve?**

The contrast lies between the favorable rates, false positive rates and the comparisons are represented in the graphical form which is known as ROC curve. It is the proxy to show the sensitivity of the data and the false alarm of the model. The expected positives and the real positives are compared to arrive at a decision. The recall is the positive rate and precision is the predictive value with positives of the model.

**Explain Bayes theorem and how it is used in the machine learning?**

The event and its probability measurement before happening are called as bayes theorem in machine learning. The formula for bayes theorem is positive rate of a condition sample with the fact/ real positive rate of a condition sample + false positive value of a population. Join the Machine Learning Training in Chennai and know about the vast usage of Machine learning in different industries.

**What do you infer from the term naïve bayes naïve?**

Naïve Bayes is the probability based on conditions which is calculated with the individual probabilities of the component. This condition is not even met for one time in the real time scenario. It is called as naïve in the practical applications.

**Explain the term Type 1 and Type 2 error in machine learning?**

False positive is called as type I and false negative is called as type II in the machine learning. Type I is about something happened but not claimed whereas type II means nothing has happened but claimed.

**Differentiate the generative and discriminative model?**

Generative model will read all the data whereas the discriminative model will learn only the categories of the data. In case of the classification tasks the discriminative model outperforms the generative model.

**Mention some of the favorite algorithms in machine learning?**

Perceptron is the algorithm in math which is used for the successful classification. This is a simple algorithm which provides support towards vector machines, logistic regression and solves using stochastic gradient descent. Boosted tree is the algorithm which is accurate and combines many simple ones. Convolutional neural networks are used for the deep learning and they are useful in the computer vision and speech recognition. Dynamic algorithm is used for searching the optional solution in a big space or huge data. Nearest neighbor is the algorithm which is used for comparison of methods towards accuracy. Thus these four algorithm forms the most interesting algorithm in machine learning. **Machine Learning Course in Chennai **will help for getting placed in the top companies.

**Explain the term Fourier in Machine learning?**

Transformation of generic function in to symmetric function is called as Fourier in machine learning. The change of signal in the machine learning for the frequency domain is termed as Fourier. The audio signals are converted in to sensor data for the analysis with the help of Fourier in machine learning.

**Differentiate probability and the expected result for an event?**

The parameter values for the observed outcomes are called as likelihood and a set of parameter towards the observed outcomes is called as probability.

**What is deep learning? How it works along with machine learning algorithms?**

Deep learning explains about using the unlabeled data or semi structured data with certain principles and neuroscience is used to handle the large volume of data. It deals with learning without supervision used in algorithms which learns the data with the help of the neural networks.

**Explain which validation technique with cross would be used on a dataset with time series?**

Time series is not the unstructured or data distributed randomly but it is the chronological order of the data. The forward chaining is about designing with the past data and then considering the data focused in the future.

**Explain the pruning of the decision tree?**

Pruning is a technique in Machine learning and it says is about the power branches of the tree and it works to decrease the power branches. Thus by eliminating the sections it helps for the accuracy of the final output. The reason for pruning depends upon various complexities like pruning with error and pruning for the cost complexity. It can be done with bottom-up or top-down. The simplest form of the pruning is error pruning which replace the node and maximize the accuracy. **Machine Learning Training in Chennai** is the best course for the beginners and experienced professionals.

**What do you infer from the word model in Machine learning?**

Training the machine with the different models with the algorithm, training data and training process is called as training the model in Machine learning.

**Explain the terms model accuracy and model performance in Machine learning?**

Model accuracy is the sub set of model performance. Classification accuracy is used to judge the performance of the model used for the machine learning.

**What’s the F one score? Where it is used?**

The weighted average score of the precision and the model is called again to check the performance. 1 and 0 shows the positive part and worst part. This is used for the classification and the true negatives of the tests are not much concerned when using.

**Explain the tactics used to handle the imbalanced data set?**

If 90 percent of the data used is in the same class then it leads to the imbalance in the data set. To overcome the concerns with the balance in the data the following measures are used. Collecting data to match the imbalances, prepare samples which match the imbalances for analysis, and change another algorithm which matches the data. As imbalance in the data and category of data leads to inaccuracy preventive measures are essential to minimize the damage.

**Explain the usage of classification over regression?**

The differences between individual points are well distinguished in the regression and it provides continuous results. For the strict categories the classification gives the discrete values and dataset to support the data. If the data points need to be like a reflection of the data sets then explicit categories are used.

**What is ensemble technique and how it is inscribed in machine learning?**

Combination of learning algorithms used for the better predictive performance is called as ensemble technique. They make the model more robust and they are not over fit into the data sets. To increase the predictive power the ensemble technique is used.

**What are the methods followed to avoid the over fitting in Machine learning?**

The three main methods used for avoiding the over fitting are keeping the model simpler, using K-folds cross validation method and using LASSO which is the regularization technique. If they are causing over fitting then one the three methods are used to make the data usage and data analysis easy.

**Explain the evaluation approaches for the Machine learning model?**

The data sets are divided in to test sets and training models. The data sets are transformed in to composite test and test sets with the techniques for the cross validation. The performance metrics such as F1 score, the accuracy and the confusion matrix are used to measure the performance. These are the evaluation approach for the machine learning model.

**The Logistic regression model is used to fulfill which goals?**

Classification and prediction are the goals of the logistic regression model and it is achieved through cross validation.

**Explain the term kernel trick?**

The images and the data are used in a featured space and inner product which is known as kernel trick. This helps for the calculation of the coordinates of higher dimensions which is cheaper than the explicit calculation and many algorithms are expressed as the inner products in the kernel trick.

**Explain about the project of Google for training data for the self-driving cars?**

The recaptcha is used by the Google to train data for self-driving cars. This is used to source the labeled data at the store fronts and traffic signs. The data collected through Google X are also used for training the new model. The advanced usage of machine learning is known through these self-driving cars.

#### Machine Learning Job Openings

**Job Title:**Senior Data Scientist

**Responsibility:** Responsible for creating infrastructure for the delivery team, the responsibility includes the data, analytics and dash boarding teams, ensure the quality and work with the internal teams.

**Job Description:** Knowledge in data mining, visualization best practices, work for better and quick results, develop or program the databases, perform the statistical analysis and understand the query of the databases, provide the relevant output of the data, analyze the data, design the data for the designated project, understand the architecture principles and design accordingly, should meet the expected deliverables, management understand the work to create examples, demonstrate the management to better understand the work, and train the juniors with peer approach. The desired candidate should have 7 to 9 years of developing platform experience and 3 to 5 years of statistical analysis.

**Company Name:** Kantar

**Location:** 7th Floor, ORION Ascendas IT Park, Madhapur Hyderabad, Telangana, India 500081

**Contact Details:** s:stephenarun.davidpeter@kantar.com

**Date of Interview: **Send the bio-data to the mentioned email and wait for the interview schedule from the company.