Alexa Echo, Amazon’s innovative drone Prime Air, and their amazing retail experience Amazon Go are just the tip of the iceberg. Machine learning is on the brink of new adventures, and is among the most sought after sectors for intelligent professionals today. Based on the premise that systems can sort out information from data, identify patterns and make informed decisions without explicit human intervention, machine learning is poised to reinvent our lives as we know it.
KnowledgeHut brings you a comprehensive course that will help you go from basic to advanced concepts in Machine Learning using R, the language that was built by statisticians, for statisticians. Learn to build systems that learn from experience, and exploit data to create simple predictive models of the world. Machine Learning with R looks into Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and carries out a comparison of each using R libraries. You will master not only the theory, but also see how it is applied in industry by learning to build predictive models using Machine Learning techniques.
Machine Learning is immensely exciting and creative, and those who have a deep understanding of this smart technology are well equipped to embark on one of the most lucrative careers of this age. Get started on creating innovation that is powered by new-age thinking; become a part of the Machine Learning revolution today!
Understand the behavior of data as you build significant models
Learn about the various libraries offered by R to manipulate, preprocess and visualize data
Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Implement algorithms and R libraries such as CRAN-R in real world scenarios
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Learn to use multiple learning algorithms to obtain better predictive performance
Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics
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Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Our support team will guide and assist you whenever you require help.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Visit basics like mean (expected value), median and mode Distribution of data in terms of variance, standard deviation and interquartile range
Basic summaries about the data and the measures. Together with simple graphics analysis
Basics of probability with daily life examples
Marginal probability and its importance with respective to datascience
Learn baye's theorem and conditional probability
Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value
Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models like Classification, Regression, Clustering. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning
Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp
Learn about cost-functions using python code
Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
Real Life Case Study on Linear Regression
Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
Real Life Case Study on Logistic Regression
Learn how KNN can be used for a classification problem
Real Life Case Study on KNN Classification
Real Life Case Study on Naive Bayesian Classifiers
Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization
Real Life Case Study on SVM
Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering
Real Life Case Study on K-means Clustering
In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
Real Life Case Study on Decision Tree
Cover basic ensemble techniques like averaging, weighted averaging & max-voting
Learn about bootstrap sampling and its advantages followed by bagging.
Boost model performance with Boosting
Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees
Real life case study with heterogeneous ensemble machine learning techniques
Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
Real-Life case study with PCA & FA
Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems. Learn what is IBCF and how is it used in Recommender Engines
Real Life Case Study with Recommender Systems
Covers projects using Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, K-Nearest Neighbor, Support Vector Machine, Adaboost, GBM, Random Forest etc.
Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.
The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.
Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.
Machine learning offers computers the amazing capability of learning cumulatively from data, and making intelligent decisions based on the data, without being programmed specifically for it. This means that machines can alter their behaviour and respond intelligently by ‘thinking’ in much the same way the human brains do. R is one of the most popular programming languages for Machine learning, as its syntax is perfect for data analytics and visualization. Our Machine Learning using R workshop will help you to harness the machine learning capabilities of R and get the smart hands-on skills that employers seek.
Machine learning experts are among the most sought after professionals across the world. With Payscale putting average salaries of Deep Learning engineers at $115,034, this is definitely the space you want to be in!
By the end of this course, you would have gained knowledge on the use of machine learning techniques using R and will be able to build applications models. This will help you land lucrative jobs as a Data Scientist.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Machine Learning experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor