Digital Sparks is the division of Malleable Software company is offering a credit based online course on Machine Learning using R. The course is designed to provide learners with a comprehensive understanding of the theoretical and practical aspects of machine learning using R. The course is suitable for learners who are interested in gaining knowledge and skills in this field, and who have a basic understanding of programming concepts. The course is credit-based, which means that learners will earn credits upon successful completion of the course. This will be beneficial for learners who are pursuing a degree or certification program, as the credits earned can be used towards their overall academic progress.

The course is structured into 12 weeks that cover topics such as data pre-processing, regression analysis, classification, clustering, and deep learning using R. Each week will have theoretical lectures as well as quiz to provide learners with experience in implementing the concepts learned. This course provide candidate 3 credits 

The course will be taught by experienced instructors who have expertise in machine learning and R programming. At the end of the course, learners will be able to apply machine learning techniques using R to solve real-world problems. They will also have the necessary skills and knowledge to pursue further studies or careers in this field.

Overall, the Machine Learning using R course offered on Digital Sparks by Malleable Software Company which is on the AICTE platform is an excellent opportunity for learners to gain valuable skills and knowledge in the field of machine learning, and to earn credits towards their academic progress.

The course is structured in the following way:

Part 1 - Data Preprocessing

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 - Clustering: K-Means, Hierarchical Clustering

Part 5 - Association Rule Learning: Apriori, Eclat

Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.                   

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.