BOOTCAMP FDP (English)

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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

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. There are no pre-requisite to attend this program. Basic knowledge of statistics is required but not compulsory

Ans 2. There are dedicated maths primers and python primers session for those with no maths and coding experience.

Ans 3. It is a 100% practical program with dozens of case studies and spreadsheet models. The approach of delivering the concepts is application based to make you a right fit for data analytics profile.

Ans 4. If you appear for the FDP exam, you will get the certificate from CAIA. However we also offer a Bootcamp in ML certificate after completion of all topic wise assignments, final project and passing MCQ based 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. Every class is supported by PDF notes and excel sheets. All these are available in the course section only.

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  

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. There are no pre-requisite to attend this program. Basic knowledge of statistics is required but not compulsory

Ans 2. There are dedicated maths primers and python primers session for those with no maths and coding experience.

Ans 3. It is a 100% practical program with dozens of case studies and spreadsheet models. The approach of delivering the concepts is application based to make you a right fit for data analytics profile.

Ans 4. If you appear for the FDP exam, you will get the certificate from CAIA. However we also offer a Bootcamp in ML certificate after completion of all topic wise assignments, final project and passing MCQ based 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. Every class is supported by PDF notes and excel sheets. All these are available in the course section only.