Online Classroom Batches Preferred
(Class 1Hr - 1:30Hrs) / Per Session
(Class 1Hr - 1:30Hrs) / Per Session
(Class 3hr - 3:30Hrs) / Per Session
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Machine Learning Online Training Overview
Our Machine Learning course module is structured regarding a way to analyze huge Data using R programming and Hadoop. This online Machine Learning certification course can revolve around the ideas of Python, Machine Learning, Data improvement, and Data Analysis. This Machine Learning course in Chennai will assist you to perceive core ideas and also the latest advancements as well as aspects of supervised, unattended, and also the latest and interesting field of Deep Learning by acting on many projects.
Machine Learning Online Training will:
- Learn about the most effective project management methodology for data processing - CRISP-DM at a high level.
- This course will make you associate degree skilled in Machine Learning ideas like Machine Learning, Algorithm, Regression, and Deep Learning.
- Learn SQL, Tableau, Java, Hive, Hadoop, Big Data, and stand out at the side of this expert Program.
- Practice the tools and techniques concerned in Machine Learning underneath the demand of industry experts.
Top Skills You Will Gain
- R Programmming, Python, SAS
- Artifical Intelligence
- Deep Learning
- Machine Learning
- Statistics, Naive Bayes
- Linear Algebra, CART
- Programming, Neural Networks
- Data Mining, Visualization
Machine Learning Course Key Features 100% Money Back Guarantee
5 Weeks TrainingFor Become a Expert
Certificate of TrainingFrom Industry Machine Learning Experts
Beginner FriendlyNo Prior Knowledge Required
Build 3+ ProjectsFor Hands-on Practices
Lifetime AccessTo Self-placed Learning
Placement AssistanceTo Build Your Career
Top Companies Placement
Annual SalaryHiring Companies
Machine Learning Course Curriculam
LearnoVita is filled with the best and top MNC trainers +11 years of highly experienced professionals. As all Trainers are working professionals so they are having many live projects , trainers will use these projects during training sessions . Our trainer will give you technical supports and passionate about data and data-driven decision making.
Syllabus of Machine Learning Course in Chennai Download syllabus
- What is Machine Learning, significance of Machine Learning in today’s digitally-driven world, applications of Machine Learning, lifecycle of Machine Learning, components of the Machine Learning lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
- Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
- Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
- Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
- Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
- Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
- Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
- Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
- Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
- Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
- Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
- Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
- Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
- Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
- What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
- Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
- What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
- Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data
- Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
- Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
- Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
- What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
- Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
- Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
- What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
- Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
- This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
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Wallmart Sales Data Set
Retail is another industry that extensively uses analytics to optimize business processes.
Flipkart Classification Dataset
This project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.
Credit Card Fraud Detection
The project consist of data analysis for various parameters of banking dataset and data visualization for finding the probability of occurrence of fraudulent activities.
- Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
- In our mock interviews will be conducted by industry best Machine Learning Course in Chennai experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
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Machine Learning Course Objectives
- Machine Learning can be defined as the process of extracting useful information from structured and unstructured data using a variety of tools and approaches.
- Data extraction, data analysis, data mining, and data retrieval are some of the techniques taught in Machine Learning to create informative results.
- A data scientist is the charLearnoVitar who does such a wide range of tasks. Furthermore, authoritative analytics, machine learning, and predictive causal analytics are commonly utilised to make choices and predictions.
- Data scientists are important wranglers of information. They require a massive amount of unstructured and structured data points, which they scrub, massage, and produce using their impressive math, statistics, and programming skills.
- Then they use all of their analytical skills — industry knowledge, contextual awareness, and scepticism of pre-existing assumptions – to uncover hidden answers to business problems.
- Based on my training experience, the first and most important step in learning Machine Learning is to choose an effective learning resource - one that recognises that learners are new to the field and unfamiliar with the Machine Learning environment, one that does not gloss over issues, and one that explains why the programme is performing the way it is.
- Well, I had to work hard for these as well.
- Yes, Machine Learning occupations are still synonymous with success on the outside.
- The following are the reasons for this. Machine Learning, like a number of other business-related phenomena, follows the basic economic laws of supply and demand. The demand for Machine Learning experts is extremely high, but the supply is simply insufficient.
- Consider engineering from a few decades back. The network was becoming a "thing," and other people were profiting from it in dangerous ways. Everyone wanted to improve their skills as a programmer or web designer, or anything else that would allow them to work in the technology field.
- Data scientists and Machine Learning are continually advancing, and in the next ten years, they will entirely transform.
- We can safely expect that Data Scientists will have a plethora of degrees in the future, and that the number of firms looking for Data Scientists will increase.
- Machine Learning is used in banking, social insurance, biotechnology, pharmaceuticals, media communications, web-based business, continuity, and automobile companies in India.
- A statistician who is 'computer literate' is referred to as a "data scientist." However, in this context, computer literacy refers to familiarity with new technologies that are critical to a few new business models that have emerged in the last five years. There isn't anything in this strategy that isn't well understood by practitioners of Business Intelligence, a subject that is between 20 and 30 years old, depending on whom you ask.
- Machine Learning is a term that refers to the study of Assume you're in charge of building a design that will attempt to forecast which customers will purchase your product.
- The corporation wants to exploit your image to prevent wasting money on clients who aren't working in order to purchase their product.
- This will preserve their business because marketing to clients who will not buy their product is a significant financial investment.
- One key piece of data scientist knowledge is that you must read a lot, all of the time! In most other jobs, you'd get your MBA or degree first, then quietly go on to holding meetings, calls, some ppt presentations, team coaching, administration, and so on.
- The constraint is that the majority of what you envisaged isn't utilised in your day-to-day work.
- On the other hand, with Machine Learning, each day and each project bring with them knowledge-related challenges.
- The problem could be with the coding, or with a new methodology for a unique data case, or with troubleshooting a piece of code that isn't working.
- According to Western standards, a Machine Learning income alone will not make you wealthy.
- You thought you'd be wealthy with a salary of $100k-$200k a year? Consider it over and over. While that is good business and would make you feel at ease in a developing country, it is nevertheless perfectly acceptable in a first-world country.
- Both in terms of what roles a data scientist should be selected for and what tasks a data scientist requires, the profession is fairly loosely defined
- To a variety of people, the term "data scientist" connotes a wide range of meanings (and companies).
- This is why you've started to see titles like "Machine Learning Engineer" appear, which is a little more precise.
Exam & Certification
At LearnoVita, You Can Enroll in Either the instructor-led Machine Learning Online Course, Classroom Training or Online Self-Paced Training.
Machine Learning Online Training / Class Room:
- Participate and Complete One batch of Machine Learning Course Course
- Successful completion and evaluation of any one of the given projects
Machine Learning Online Self-learning:
- Complete 85% of the Machine Learning Certification Training
- Successful completion and evaluation of any one of the given projects
These are the Different Kinds of Certification levels that was Structured under the Machine Learning Certification Path.
- Certified Analytics Professional (CAP)
- Cloudera Certified Associate: Data Analyst
- Cloudera Certified Professional: CCP Data Engineer
- Machine Learning Council of America (DASCA) Senior Data Scientist (SDS)
- Machine Learning Council of America (DASCA) Principle Data Scientist (PDS)
- Dell EMC Machine Learning Track
- Google Certified Professional Data Engineer
- Google Data and Machine Learning
- IBM Machine Learning Professional Certificate
- Microsoft MCSE: Data Management and Analytics
- Microsoft Certified Azure Data Scientist Associate
- Open Certified Data Scientist (Open CDS)
- SAS Certified Advanced Analytics Professional
- SAS Certified Big Data Professional
- SAS Certified Data Scientist
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LernoVita Machine Learning Certification Training in Chennai That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
Pranav SrinivasSoftware Testing, Capgemini
Machine Learning Course FAQ's
- LearnoVita Best Machine Learning Course in Chennai will assist the job seekers to Seek, Connect & Succeed and delight the employers with the perfect candidates.
- On Successfully Completing a Career Course from LearnoVita Best Machine Learning Course in Chennai, you Could be Eligible for Job Placement Assistance.
- 100% Placement Assistance* - We have strong relationship with over 650+ Top MNCs, When a student completes his/ her course successfully, LearnoVita Placement Cell helps him/ her interview with Major Companies like Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree and MPhasis etc...
- LearnoVita is the Legend in offering placement to the students. Please visit our Placed Students's List on our website.
- More than 5400+ students placed in last year in India & Globally.
- LearnoVita is the Best Machine Learning Course Institute in Chennai Offers mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
- 85% percent placement record
- Our Placement Cell support you till you get placed in better MNC
- Please Visit Your Student's Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
- LearnoVita Certification is Accredited by all major Global Companies around the World.
- LearnoVita is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of Machine Learning and National Institute of Education (nie) Singapore
- Also, LearnoVita Technical Experts Help's People Who Want to Clear the National Authorized Certificate in Specialized IT Domain.
- LearnoVita is offering you the most updated Machine Learning certification training in Chennai, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the Machine Learning classes in Chennai as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.