- The complete course structure is designed in association with ESDS keeping Cloud Data Centre where Scale and Volume are at different level.
- The course is skill development oriented which can help in getting job effectively.
- The syllabus is designed by the industry experts.
- All sessions will be delivered by the experienced industry experts who themselves practice the subject
- The program is a combination of Training + Guided project work by industry experts. Students will undergo training via live sessions during weekdays in July followed weekend mentorship in August for the project work.
- Students will receive a certification on completion of the program.
ESDS is one of India’s leading Managed Data Center Service and Auto-Scalable Cloud Solution provider. ESDS has expertise in Managed Data Center Services, Managed Cloud Solutions, Virtualization and Disaster Recovery Hosting, which is backed with exuberant Technical Support that has helped ESDS build lifetime relationships with customers.
- To understand nature of problem and apply machine learning algorithms to solve those problems.
- To learn how to design and program python application.
- Understand and learn the python programming language with various python syntax, statements, operators.
- Understand the concepts of Classes and Objects, OOP Concepts, Constructors, Exception Handling, File input output operations.
- Understand Python libraries for Machine Learning such as Numpy, scipy, Scikit-learn, Pandas, Matplotlib, Math.
- Learn concepts of Regression and classification.
- Learn the concepts of decision tree and KNN algorithm.
- Understand the Clustering and its fundamental concepts. Also learn the concepts of Artificial Neural Network.
- After completion of this course, students will have a base to work in Data Science field
Who can apply?
This course is eligible for undergraduate third and fourth year Engineering students (B.E.) Following branches will be eligible for this courses –
1. BE – IT, computer, Computer Science Engineering.
2. BE – Electronics, Electronics and Telecommunication
1. To pay in installments, please use credit card and click on ‘Take this course’
2. If you would like to pay in installments or full advance by Debit card/ UPI/ other methods please write to us at firstname.lastname@example.org with your contact details(Name,Email,Phone no), college name, engineering branch & year, Selected course name and we will send you a unique transaction link within 24hrs to enroll you into the course.
In case of any queries regarding the program, please contact email@example.com
|Introduction to Basic Python Programming IDE: PyCharm|
|Python Features, Basic Syntax, Data Types, Basic Operators||01:08:00|
|Decision Making, Loops (While, for, Nested), Comments||01:08:00|
|python-pass, continue statement, python operators, python Date & Time||01:08:00|
|Introduction to Advance Python Programming|
|Python-Strings, List, Tuples, Dictionary, Functions and Methods, Classes and Objects||01:03:00|
|OOP Concepts, Constructors, Exception Handling, File input output operations||01:03:00|
|Python libraries for Machine Learning – Numpy, scipy||01:03:00|
|Scikit-learn, Pandas, Matplotlib, Math||01:03:00|
|Linear Regression- Basic of Linear Regression, Ridge, Lasso, Elastic Net||01:00:00|
|Robust regression with random sample consensus,||01:00:00|
|Polynomial Regression and Isotonic regression||01:00:00|
|Logistic Regression- Basics of Logistics Regression, Types of Logistic Regression (Binary, Multinomial, Ordinal)||01:03:00|
|Classification- Naïve Bayes, Support Vector Machine||01:00:00|
|Impurity Measures, Features Importance||02:07:00|
|Ensemble Learning, Random Forests, Ada boost, Voting Classifier||02:07:00|
|Working of KNN, Algorithm||05:28:00|
|Principal Component Analysis(PCA), Non-Negative Matrix Factorization, Advantages||05:28:00|
|Basics, K-means algorithm, DBSCANS||01:08:00|
|Spectral Clustering, Agglomerative Hierarchical clustering||01:08:00|
|Artificial Neural Network – Concept, Feed forward and Feedback ANNs, Error Back Propagation, Convolutional neural networks||01:08:00|
No Reviews found for this course.