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Sln | Topic | Details | ||

Module 1 - Primer |
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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 |
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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) |
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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 |
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Module 2 – Predictive Analytics |
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04 | Linear Regression | Ways to estimate coefficients in Regression Model Simple vs Multiple Linear Regression Regression Assumptions (Multicollinearity, OVB, Serial Correlation, Hateroscedasticity) Stepwise regression |
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05 | Types of Regression | Principal Component Regression MCMC Kalman Regression |
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06 | Time Series Model | Checking Stationarity of Data Deterministic, Stochastic Trend & Seasonality Autocorrelation & Partial Autocorrelation Functions Fitting ARIMA models LSTM - Long Short Term Memory |
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Module 3 – Supervised Learning (Classification) |
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07 | Decision Boundary Algorithms | Linear Discriminant Analysis Linear SVM Non Linear SVM Kernel SVM |
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08 | Logistic Regression | Logistic Regression | ||

09 | Decision Trees | Classification Trees Regression Trees Stooping & Pruning Criterias |
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10 | KNN | Distance Measures K- Nearest Neighbour |
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11 | Neural Networks | Gradient Descent Forward Propoagation Backward Propagation |
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12 | Classification Model Selection and Performance | ROC & CAP Curve Confusion Matrices |
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Module 4 – Supervised Learning (Regression) |
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13 | Bias vs Variance Trade Off | K Fold Cross Validation | ||

14 | Regularisation techniques | Lasso Ridge Elastic Net |
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Module 5 – Unsupervised Learning |
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15 | Dimensionality Reduction | Principal Component Analysis (PCA) | ||

16 | Clustering | Hierarchical Clustering K-Means Clustering Partitive Clustering |
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Module 6 – Reinforcement Learning |
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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 |
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19 | On Policy Evaluation vs Off Policy Evaluation | Utility Based Method SARSA | ||

Module 7 – Natural Language Processing (NLP) |
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20 | Data Preparation | Cleaning Regex |
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21 | Data Wrangling | Tokenisation Normalisation Bag of Words n- Grams Lowercasing Stop words Stemming Document Term Matrix |
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22 | Exploratory Data analysis | Term Frequency (Word Cloud) Document Frequency |
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23 | Feature selection | Chi Sq Test Mutual Information |
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24 | Feature Engineering | n-Grams POS Name entity recognition |
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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. 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 |

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