Machine Learning
Plunge into this intriguing world of Machine Learning and satiate your interest in data analysis, algorithms, automation and a lot more. This course will help you go through all the important concepts of machine learning, enabling you to acquire or enhance your skills in this sub-field of Data Science.
Content Provider
iNURTURE
Course Level
Intermediate
Mode Of Delivery
Self-Paced
Duration
40 hrs
Certification By
iNURTURE
Course Validity
Valid for 6 months post activation
Learning Resources
Videos Easy, Accessibility, Certification
Description
Machine learning is the science of getting computers to act without being explicitly programmed. Instead of writing a code, you feed data to the generic algorithm, and it builds the logic based on the data provided. Machine learning algorithms allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.
This Course
In this course, you will go through the basic concepts of machine learning, Python and regression. Further, this course will explain detailed information regarding machine learning as a method of data analysis that automates analytical model building.
Learning Outcomes
- Understand the different machine learning techniques and its application
- Understand the importance of assumptions in estimating the parameters in simple linear regression analysis.
- Understand the importance of simple linear regression in predicting new observations
- Understand the important multiple linear regression in predictive techniques and its assumptions.
- Apply the non-linear model for the new observation predictions and its importance in business.
- Understand the effect of model assumptions in estimating the coefficients in multiple linear regression analysis.
Curriculum
Module 1
- Introduction to Python
- Numpy Basics
- Pandas Basics
Module 2
- Matplotlib basics
- Seaborn basics
Module 3
- Introduction to Machine Learning
- Data Preprocessing
- Creating validation rules
Module 4
- Introduction to Regression
- Regularized Regression
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 5
- Introduction to Classification
- Regularized Classification
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 6
- Introduction to Decision Tree
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 7
- Introduction to Random Forest
- Auto selection of parameters
- Bagging and Boosting Models
- Evaluation of best models
- Model representation
Module 8
- Introduction to SVM
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 9
- Introduction to Neural Network
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 10
- Introduction to Unsupervised Learning
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 11
- Introduction to Dimension Reduction
- Auto selection of parameters
- Evaluation of best models
- Model representation
Module 12
- Introduction to Nearest Neighbors
- Auto selection of parameters
- Evaluation of best models
- Model representation
Key Features
- 12 well-structured modules
- 12 Explanatory Videos
- Certificate of Completion
Career Opportunities
After successfully completing this course, you can look for opportunities as:
- Data Architect
- Data Scientist
- Data Mining Specialists
- Cloud Architect
- Cyber Security Analysts
Certification
After completing this course and successfully passing the certification examination, the student will be awarded the “Machine Learning” certification.
If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'.
Who Should Attend
- Engineering and IT students
- Graduates with a programming background
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