- Introduction to course
- Advanced concepts in trading strategies
- Basic trading strategy building a trading model
- Basic trading strategy entries and exits endogenous exogenous
Using Machine Learning in Trading and Finance
Learn the process of training on large datasets by pursuing Using Machine Learning in Trading and Finance by Coursera.
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
The Using Machine Learning in Trading and Finance online course is providing a platform for the students to learn and study about the basics that are involved in developing advanced strategies that are used in modern machine learning techniques.
In the online programme on Using Machine Learning in Trading and Finance by Coursera, the applicants will be studying the key components that are common to all the trading strategies irrespective of the platforms and dimensions.
In the Using Machine Learning in Trading and Finance certification syllabus, the enrolled students will be covering the comprehensive chapters of- Regularization: L1, L2, and early stopping, Regularization: dropout, Lab Intro: Keras functional API, Regularization: the basics, Neural networks with Keras functional API, Collect the data, Backtest on unseen data, Selecting a machine learning algorithm, Lab Intro: momentum trading, Creating features, Define the problem, Split the data and others.
- 100% online programme
- Shareable certificate
- Deadlines flexible
- Course level intermediate
- Programme by Coursera
- Course offered by New York Institute of Finance and Google cloud
- 19 hours total course duration
- Subtitles in English
- Online course
- Video lectures
- Practice sets
- Case study
Course and certificate fees
- The enrollment for the Using Machine Learning in Trading and Finance fee is free for a 7-day trial.
- The certification fees amount to Rs. 3583 needs to be paid to continue ahead.
Using Machine Learning in Trading and Finance fees details
certificate providing authority
Who it is for
The Using Machine Learning in Trading and Finance programme is for the following students-
- Candidates who want to study Kalman filters can apply for the course directly.
The candidates must have a working knowledge of SQL to apply and enrol for this programme.
To apply for the Using Machine Learning in Trading and Finance certification course, the students need to have advanced competency in the domain of Python programming and familiarity with pertinent libraries for machine learning - StatsModels, Pandas and Scikit-Learn. The student also needs to have a background in statistics (linear regressions, probability, higher moments, Gaussian distributions, standard deviation, expected values) and financial markets( hedging, market structure, derivatives, bonds, equities).
Certification Qualification Details
Students will be getting the Using Machine Learning in Trading and Finance certification after they successfully complete the course in the stipulated time frame. They are also required to make a fee payment to pursue the programme once the 7 days free trial period ends.
What you will learn
The Using Machine Learning in Trading and Finance programme will give detailed insight about the following elements to the students:
- The candidates will get a detailed understanding of the subject of Tensorflow.
- The topic of the TensorFlow API hierarchy will be studied by the students in the course.
- Applicants will be studying the process of Working in memory and its application.
- The Using Machine Learning in Trading and Finance certification syllabus includes the chapter on activation functions
- The process of Picking pairs with clustering is also included in the course.
- The detailed dynamics and process of Google Cloud will be taught to the candidates.
- The Components of TensorFlow tensors will be taught to the students.
- Applicants will be taught about neural methods of programming.
- Learners will study Keras functional AP
- The process of collecting data is also included in the programme module.
Filling the form
To apply for the Using Machine Learning in Trading and Finance online programme the students have to follow the given steps-
Step 1: The students have to visit the following website- https://www.coursera.org/learn/machine-learning-trading-finance
Step2: The students have to click on the “enrol” button.
Step 3: The students then fill in the credentials that are mentioned in the form.
Step 4: The course fee needs to be deposited for accessing the course materials.
Week 1: Introduction to quantitative trading and TensorFlow
- Welcome to Using Machine Learning in Trading and Finance
- Understand Quantitative Strategies
Week 1: Introduction to TensorFlow
- Introduction to TensorFlow
- TensorFlow API Hierarchy
- Components of TensorFlow Tensors and Variables
- Getting Started with Google Cloud Platform and Qwiklabs
- Lab Intro Writing low-level TensorFlow programs
- Working in-memory and with files
- Training on Large Datasets with tf.data API
- Getting the data ready for model training
- Lab Intro Manipulating data with TensorFlow Dataset API
Week 2: Training neural networks with TensorFlow 2 and Keras
- Regularization: dropout
- Neural networks with Keras sequential API
- Regularization: l1, l2, and early stopping
- Regularization: the basics
- Activation functions
- Lab intro: Keras functional API
- Lab intro: Keras sequential API
- Activation functions: pitfalls to avoid in backpropagation
- Neural networks with Keras functional API
- Serving models in the cloud
Week 3: Build a momentum-based trading system
- Introduction to momentum trading
- Split the data
- Creating features
- Backtest on unseen data
- Collect the data
- Momentum trading lab solution
- Understanding the code: simple ml strategies to generate a trading signal
- Lab intro: momentum trading
- Selecting a machine learning algorithm
- Building a momentum trading model
- Introduction to Hurst
- Define the problem
- Hurst Exponent and Trading Signals Derived from Market Time Series
Week 4: Build a pair trading strategy prediction model
- Introduction to pair trading
- Picking pairs
- Picking pairs with clustering
- How to implement a pair trading strategy
- Evaluate results of a pair trade
- Backtesting and avoiding overfitting
- Next steps: improvements to your pair strategy
- Lab intro: pairs trading
- Lab solution: pairs trading
- Kalman filter introduction
- Kalman filter trading applications
- Pairs Trading Strategy concepts
Candidates who are looking for financial aid can apply for the same online.
How it helps
The Using Machine Learning in Trading and Finance certification benefits the applicants by helping them to become an expert in the field of machine learning. The Online session has been structured by the New York Institute of Finance and Google cloud and is coordinated by the Institute of Coursera. The course has been designed for a platform to be 100% online. The Using Machine Learning in Trading and Finance fee for the registration has been kept free so that students can access the course materials more easily. The course also has the provision of providing the students with the provision of seven days free trial.
Yes, in the course of Using Machine Learning in Trading and Finance training the students will be provided with scholarship aids.
The programme does not have any mandated registration fee. The registration for the course is free.
Yes, for the ease of the students understanding, the course has the provision of English subtitles.
No, for the Using Machine Learning in Trading and Finance certification there is no specified eligibility.
The students will get to access the course guides once they enrol for the Using Machine Learning in Trading and Finance programme.
For applying for the course, the following link has to be visited- https://www.coursera.org/learn/machine-learning-trading-finance
Yes, the Using Machine Learning in Trading and Finance online course is having a shareable certificate.
Yes, for a period of 7 days, the students will get to access the free trial.
Yes, the Using Machine Learning in Trading and Finance certification benefits the students by allowing them to access the course in the self-paced learning mode.
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