BOOTCAMP Risk and AI by GARP

At a Glance

New AGE Excel

PYTHON FOR RISK

Stats for Finance

Risk and AI by GARP

Market and CPD Risk

Credit Risk Modelling

Cash Intraday

Deep Quant Finance

ICAAP, ILAAP and IRRBB

Bonds Techno Funda

Sustainability Climate Risk

BOOTCAMP RISK AND AI BY GARP

BROCHURE

No content

Sln	Topic	Details
Module 1 - Primer
01	Introduction to Machine Learning	What is Machine Learning Types of Learning (Supervised, Unsupervised, Reinforced) Structured vs Unstructured Data Applications of Machine Learning in Real Life
02	Math toolbox for ML	Linear Algebra Vector Algebra (Addition, Product, Projections) Matrix Algebra (Transpose, Multiplication, Inverse, Eigen Values) Optimization Maxima and Minima (calculus based) Lagrangian Multipliers Gradient Descent Parameter Estimation Maximum Likelihood Method (MLE) Maximum a Posteriori (MAP)
03	Getting Started with Python	Python basic data types - CRUD Numpy Python Plotting Pandas Probability & Stats in python Regression in Python. Time Series in Python Monte Carlo Simulations in Python
Module 2 – Predictive Analytics
04	Linear Regression	Ways to estimate coefficients in Regression Model Simple vs Multiple Linear Regression Regression Assumptions (Multicollinearity, OVB, Serial Correlation, Hateroscedasticity) Stepwise regression
05	Types of Regression	Principal Component Regression MCMC Kalman Regression
06	Time Series Model	Checking Stationarity of Data Deterministic, Stochastic Trend & Seasonality Autocorrelation & Partial Autocorrelation Functions Fitting ARIMA models LSTM - Long Short Term Memory
Module 3 – Supervised Learning (Classification)
07	Decision Boundary Algorithms	Linear Discriminant Analysis Linear SVM Non Linear SVM Kernel SVM
08	Logistic Regression	Logistic Regression
09	Decision Trees	Classification Trees Regression Trees Stooping & Pruning Criterias
10	KNN	Distance Measures K- Nearest Neighbour
11	Neural Networks	Gradient Descent Forward Propoagation Backward Propagation
12	Classification Model Selection and Performance	ROC & CAP Curve Confusion Matrices
Module 4 – Supervised Learning (Regression)
13	Bias vs Variance Trade Off	K Fold Cross Validation
14	Regularisation techniques	Lasso Ridge Elastic Net
Module 5 – Unsupervised Learning
15	Dimensionality Reduction	Principal Component Analysis (PCA)
16	Clustering	Hierarchical Clustering K-Means Clustering Partitive Clustering
Module 6 – Reinforcement Learning
17	Markov Decision Proces	State, Action, Rewards Matrix
18	Model based Learning vs Model Free Learning	Analytical Solution Iterative Procedure Random Exploration & Exploitation Utility Based Method
19	On Policy Evaluation vs Off Policy Evaluation	Utility Based Method SARSA
Module 7 – Natural Language Processing (NLP)
20	Data Preparation	Cleaning Regex
21	Data Wrangling	Tokenisation Normalisation Bag of Words n- Grams Lowercasing Stop words Stemming Document Term Matrix
22	Exploratory Data analysis	Term Frequency (Word Cloud) Document Frequency
23	Feature selection	Chi Sq Test Mutual Information
24	Feature Engineering	n-Grams POS Name entity recognition
25	Model Training & Validation

ABOUT THE TRAINER

Karan is a highly skilled & knowledgeable Corporate trainer with 5+ years of total work experience spanning across Financial Modelling & Data Analytics. Known for having a knack for problem solving, thought leadership, highly analytical mindset, intrapreneurship, solid fundamentals & learning aptitude. Spearheaded several solution accelerators and spreadsheet based prototypes in Risk and Analytics space.

Satya is an IIT and IIM alumni with 8+ years of total work experience spanning across Financial Risk consulting and project management and strategy. Worked as SME and Lead in Various finance, risk, regulatory engagements and complex data migraflon project. Adept in BASEL, FRTB capital calculations, model development and machine learning.

Ans. 1. Anyone with finance background like having studied some level of CFA FRM or actuaries can join this program.

Ans.2. Maths Primers and Python Primers have been included in the program, so no previous experience is expected.

Ans 3. This course is quite long & comprehensive only because we have covered the entire curriculum in 3 parts – theory discussion, visualisations in excel, practical implementation through hands-on session in excel & python

Ans.4. To get certificates you need to complete all topic wise assignments, master project and pass the Final exam.

Ans.5. You can take either 1 year access or lifetime access. Please note that lifetime access is chargeable extra

Ans.6 With this website we have integrated a customized P2T player that will allow you to play encrypted classes. There are no limitations on the number of views. Also the software is compatible with Windows, Mac, Android or iPhone

Ans.7. To interact with the trainer we have a dedicated forum ‘D-forum’. Any questions asked on D-forum are expected to be replied within 24 hours by trainers and team of moderators & experts.

Ans. 8. Presently we are conducting exams in Aug mid and Jan mid. You can choose any of the cohort. In case you are not able to pass the exam in one go, you can re-book at a nominal charge

Ans.9. Every class is supported by One note files, Excel sheets & Python notebooks, Assignments and Quizzes, all these are available in the course section only.

Ans. 10. You get Letter of Recommendation physically delivered to you within 60 days of passing the exam. LOR’s also mention the chosen specialisation with the project details.

No content

Sln	Topic	Details
Module 1 - Primer
01	Introduction to Machine Learning	What is Machine Learning Types of Learning (Supervised, Unsupervised, Reinforced) Structured vs Unstructured Data Applications of Machine Learning in Real Life
02	Math toolbox for ML	Linear Algebra Vector Algebra (Addition, Product, Projections) Matrix Algebra (Transpose, Multiplication, Inverse, Eigen Values) Optimization Maxima and Minima (calculus based) Lagrangian Multipliers Gradient Descent Parameter Estimation Maximum Likelihood Method (MLE) Maximum a Posteriori (MAP)
03	Getting Started with Python	Python basic data types - CRUD Numpy Python Plotting Pandas Probability & Stats in python Regression in Python. Time Series in Python Monte Carlo Simulations in Python
Module 2 – Predictive Analytics
04	Linear Regression	Ways to estimate coefficients in Regression Model Simple vs Multiple Linear Regression Regression Assumptions (Multicollinearity, OVB, Serial Correlation, Hateroscedasticity) Stepwise regression
05	Types of Regression	Principal Component Regression MCMC Kalman Regression
06	Time Series Model	Checking Stationarity of Data Deterministic, Stochastic Trend & Seasonality Autocorrelation & Partial Autocorrelation Functions Fitting ARIMA models LSTM - Long Short Term Memory
Module 3 – Supervised Learning (Classification)
07	Decision Boundary Algorithms	Linear Discriminant Analysis Linear SVM Non Linear SVM Kernel SVM
08	Logistic Regression	Logistic Regression
09	Decision Trees	Classification Trees Regression Trees Stooping & Pruning Criterias
10	KNN	Distance Measures K- Nearest Neighbour
11	Neural Networks	Gradient Descent Forward Propoagation Backward Propagation
12	Classification Model Selection and Performance	ROC & CAP Curve Confusion Matrices
Module 4 – Supervised Learning (Regression)
13	Bias vs Variance Trade Off	K Fold Cross Validation
14	Regularisation techniques	Lasso Ridge Elastic Net
Module 5 – Unsupervised Learning
15	Dimensionality Reduction	Principal Component Analysis (PCA)
16	Clustering	Hierarchical Clustering K-Means Clustering Partitive Clustering
Module 6 – Reinforcement Learning
17	Markov Decision Proces	State, Action, Rewards Matrix
18	Model based Learning vs Model Free Learning	Analytical Solution Iterative Procedure Random Exploration & Exploitation Utility Based Method
19	On Policy Evaluation vs Off Policy Evaluation	Utility Based Method SARSA
Module 7 – Natural Language Processing (NLP)
20	Data Preparation	Cleaning Regex
21	Data Wrangling	Tokenisation Normalisation Bag of Words n- Grams Lowercasing Stop words Stemming Document Term Matrix
22	Exploratory Data analysis	Term Frequency (Word Cloud) Document Frequency
23	Feature selection	Chi Sq Test Mutual Information
24	Feature Engineering	n-Grams POS Name entity recognition
25	Model Training & Validation