
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 KMeans 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  nGrams POS Name entity recognition 

25  Model Training & Validation 
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.
Ans Ans 1. There are no prerequisite 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 ‘Dforum’. Any questions asked on Dforum 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 available in the course section. A hardcopy of material is also available on payment of 1500 rs printing and courier charges.

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 KMeans 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  nGrams POS Name entity recognition 

25  Model Training & Validation 
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.
Ans Ans 1. There are no prerequisite 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 ‘Dforum’. Any questions asked on Dforum 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 available in the course section. A hardcopy of material is also available on payment of 1500 rs printing and courier charges.