Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Machine learning is a new technological advancement that has been part of many organizations. It is a study of computer algorithms that can improve automatically through the use of data and experience. It is part of artificial intelligence which is a booming domain and is widely used by organizations. The Python and Machine Learning for Asset Management course is an initiative by the EDHEC Business School for learners who are interested in learning machine learning and python programming specifically for asset management.
Python and Machine Learning for Asset Management training is delivered by the experts on the online learning platform of Coursera. The duration of this online course is 3 weeks and the classes will be completed within the time frame of 16 hours. Candidates will be trained on machine learning approaches in investment management. The platform is providing multiple benefits and Python and Machine Learning for Asset Management certification by EDHEC Business School for the learners who opt for certification mode for this course.
The Highlights
- 3 weeks duration
- Online course
- 16 hours of classes
- Shareable certification
- Self-paced learning
- Quizzes
- Graded assignments
- Financial Aid
Programme Offerings
- trained by experts
- Online Course
- Shareable Certification
- Flexible Deadlines
- Feedback
- quizzes
- assessments
- 3 Weeks Duration
- 16 hours of classes
- financial aid.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The interested candidates who are opting for the certification mode can pay the fee through credit card, debit card, and internet banking. The students who have chosen the audit mode can start learning for free, but they shall not be given any benefits or certification. As per the time requirement by the candidate to complete the course, the portal has given a detailed fee structure which is mentioned below.
Python and Machine Learning for Asset Management fee structure
Description | Fee in INR |
Course Fee, 1 Month | Rs. 3,277 |
Course Fee, 3 Months | Rs. 6,554 |
Course Fee, 6 Months | Rs. 9,831 |
Eligibility Criteria
Certification Qualifying Details
Python and Machine Learning for Asset Management certification is given to all the candidates who have chosen the certification mode for this course. The candidates should complete the associated assignments, and quizzes to receive a shareable certification from EDHEC Business School. The certification will open gates of opportunities for the learner and shall inculcate the skills of machine learning and python programming proficiently.
What you will learn
Python and Machine Learning for Asset Management certification syllabus is detailed and designed in a three-step learning process. The first step includes the introduction of a meaningful investment problem and then focuses on solving and addressing the problem using statistical techniques. The last or the final step solving it through machine learning and checking the relevance of the analysis. The learner will gain perfection in the following areas by the end of this online course.
- Grasping the foundational knowledge of supervised and unsupervised machine learning techniques, especially for financial data sets.
- In-depth understanding of the logistical regression and ML algorithms for the classification of variables into one of two outcomes.
- Utilizing Python libraries for the implementation of machine learning algorithms in case studies.
- In-depth understanding of the factor models and regime-switching models. Studying about their use in investment management.
Who it is for
Python and Machine Learning for Asset Management certification benefits candidates who are stepping into the domain of machine learning for asset management. The course could be advantageous for the people working at the following designations.
- Python programmers,
- Engineers,
- ML Engineers,
- Financial advisors
Admission Details
To enroll yourself in the Python and Machine Learning for Asset Management online course, follow the steps mentioned below:
Step 1: Visit the portal of Coursera for the Python and Machine Learning for Asset Management course. The direct link for the course is https://www.coursera.org/learn/python-machine-learning-for-investment-management
Step 2: The screen with the detailed introduction of the course shall be displayed on the portal of Coursera.
Step 3: Click on the tab “Enroll for free” and sign up for the course.
Step 4: Complete the enrollment process by paying the Python and Machine Learning for Asset Management certification fee if the candidate is opting for the certification course.
Step 5: Start learning by attending Python and Machine Learning for Asset Management classes.
The Syllabus
Videos
- Welcome to the Python Machine-Learning for Investment management course
- Introduction to machine-learning
- Financial applications
- Supervised learning
- First algorithms
- Highlights of best practice
- Unsupervised learning
- Challenges ahead
- Lab session optimal portfolio
Readings
- Requirements
- Material at your disposal
- Machine Learning for Investment Decisions: A Brief Guided Tour
- References for module 1"Introducing the fundamentals of machine learning"
- Lab session optimal portfolio
Quiz
- Module 1Graded Quiz
Discussion Prompt
- Challenges ahead
Ungraded Lab
- Python lab sessions
Videos
- Introduction to module 2 - Basics of factor investing
- Introducing Factor Models
- Typology of factor models
- Using factor models in portfolio construction and analysis
- Penalty methods
- Setting factor loadings and examples
- Shrinkage concepts
- Lab session - Jupiter notebook on Factor Models
Readings
- References for module 2"Machine learning techniques for robust estimation of factor models"
- Information on Jupyter notebook - Factor models
Quiz
- Module 2 Graded Quiz
Videos
- Introduction to module 3 -Machine learning techniques for efficient portfolio diversification
- Benefits of portfolio diversification
- Portfolio diversification measures
- Principle component analysis
- Role of clustering
- Graphical analysis
- Selecting a portfolio of assets
- Lab session: Graphical Network Analysis
Readings
- Supplementary material PCA
- References for the module "Machine learning techniques for efficient portfolio diversification"
- Reference for the module "Selecting a portfolio of assets"
- Lab session: Graphical Network Analysis
Quiz
- Module 3 Graded Quiz
Discussion Prompt
- Selecting a portfolio of assets
Videos
- Introduction to economic regimes
- Portfolio Decisions with Time-Varying Market Conditions
- Trend filtering
- A scenario based portfolio model
- A two regime portfolio example
- A multi regime model for a University Endowment
- Lab session- Jupyter notebook on regime-based investment model
Readings
- Information on the "trend filtering" video
- Information on "scenario based portfolio model" video
- References for the module "Machine learning techniques for regime analysis"
- Information on Jupyter notebook on regime-based investment model
Quiz
- Module 4 Graded Quiz
Videos
- Introduction to module
- Traditional approaches
- Machine-Learning Processes
- Several Machine Learning Methods
- Predicting recessions
- Challenges ahead
- Lab session 5: Regime Prediction with Machine Learning
Readings
- References for the module "Identifying recessions, crash regimes and features selection"
- Information on Jupyter notebook on Forecasting recession with machine learning
- To be continued (3)
Quiz
- Module 5 Graded Quiz