Machine Learning Algorithms with Python Masterclass
Rs. 999/- Only!
Next Session: 6th – 7th January 2024 | 10AM – 5PM IST
Level: Intermediate | Programming Language: Python 3
✓ Master a comprehensive set of machine learning algorithms with theory and implementation in python
✓ learn the popular scikit-learn library for machine learning, along with statsmodels and prophet for forecasting
✓ Create impactful models using machine learning, perform tuning and interpretation, and deploy using flask
✓ develop a machine learning project live during class and Receive feedback to refine your data Science Skills
✓ prepare for mid-level job interviews for data science and machine learning
✓ Receive A Certificate Of Completion
✓ BONUSES: Templates for Solving ML problems, 50 Conceptual Exercises, Cheatsheet and extra material on other algorithms!
What you will learn
The Masterclass is deigned to be a one-stop place to learn in-depth the working and implementation of “ALL” the important algorithms that are widely used in the Data Science industry using Python. A live hands-on project is covered where learners will develop a project from scratch either individually, or as a group, and receive feedback.
Prerequisites: Participants should have a basic working knowledge of Python programming language. Familiarity with fundamental Data Science concepts is helpful but not required as it’ll be covered during the Masterclass.
1. Fundamentals of Machine Learning
✓ Introduction to Machine Learning including a foundational overview of machine learning concepts, bias-variance tradeoff, key terminology, train-validation-test methodology, k-fold cross-validation, nested cross-validation, interpretability methods including LIME and SHAP, L1 and L2 regularization, ensembling techniques, hyperparameter tuning
✓ Python Libraries such as scikit-learn for Machine Learning, statsmodels and Facebook's Prophet for statistical modelling, PyMC3 for Bayesian modelling, Yellowbrick for Machine Learning visualization
✓ Data Preprocessing for Machine Learning including missing value imputation using mean-median-model, arbitrary values, frequent categories and KNN-based imputation; one-hot encoding for categorical variables; handling outliers, influential points and leverage points; feature scaling using standardization, minmax scaling, robust scaling
✓ Feature Selection and Engineering techniques including stepwise selection, recursive feature elimination for feature selection and aggregation, extracting time information for feature engineering
3. Supervised Learning: Classification
✓ Introduction to Classification highlighting difference between binary, multi-class and multi-label classification, importance of classification threshold
✓ Introduction to Logistic Regression including why it is termed a regression, the mathematical underpinnings, statistical assumptions and associated tests, interpreting results
✓ Other Classification Algorithms including k-nearest neighbours (kNN), support vector machine (SVM), Naive Bayes (Gaussian, Bernoulli, Multinomial), and when to use which algorithm
✓ Evaluation Metrics including confusion matrix, accuracy, precision, recall, F1-score, specificity, cost-based metrics, area under curve of ROC curve
5. Unsupervised Learning
✓ Introduction to Unsupervised Learning explaining the differences between supervised and unsupervised learning, real-world applications, what is semi-supervised learning
✓ Clustering techniques including k-means clustering, its algorithmic approach, advantages and limitations; hierarchical clustering methods and dendogram visualization; when to choose which technique
✓ Association Rule Mining techniques including key terminology such as support, confidence and lift; Apriori algorithm, how it discovers frequent itemsets and generates association rules; introduction to Eclat algorithm and approach; comparing Apriori and Eclat algorithms
✓ Dimensionality Reduction techniques including understanding the curse of dimensionality, factor analysis for reducing feature dimensions, interpretation of factors; introduction to principal component analysis (PCA); linear discriminant analysis (LDA) and discriminant functions
7. Live Project with Mentorship
✓ Project Introduction including introduction to the business problem, overview of dataset to be used
✓ Data preprocessing and cleaning including data loading using Pandas, data preprocessing such as handling missing values and outliers, data transformation and feature engineering with NumPy and Pandas
✓ Basic Exploratory data analysis (EDA) including statistical analysis and visualization using Pandas and Seaborn, and identifying patterns and correlations in the data
✓ Data Visualization and Insights including designing data visualizations with Matplotlib and Seaborn, creating informative and visually appealing plots, extracting actionable insights, and presenting findings
✓ Machine Learning including splitting into training and test sets, model selection, model training, model evaluation, hyperparameter tuning, model interpretability using SHAP and model deployment with Flask
✓ Final Deliverables will include a Jupyter notebook containing code, data visualizations and model output, upon which feedback would be provided
2. Supervised Learning: Regression
✓ Introduction to Linear Regression including simple and multiple linear regression, statistical assumptions and associated tests, residual analysis, variance inflation factor for testing multicollinearity, interpreting results
✓ Variations of Linear Regression including regularized regression with LASSO, Ridge and ElasticNet regression, polynomial regression, quantile regression, Dirichlet regression, robust regression, Bayesian regression
✓ Other Methods including Generalized Additive Models (GAM), leveraging partial dependence plots for interpretability, regularized regression with LASSO, Ridge and ElasticNet regression, polynomial regression, quantile regression, Dirichlet regression, robust regression, support vector regression
✓ Evaluation Metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute % Error (MAPE), R-Squared, Adjusted R-Squared, Predicted R-Squared, AIC and BIC
4. Supervised learning: tree-based models
✓ Introduction to Tree-Based models including key terminology, use in regression & classification, decision tree construction from scratch, splitting criteria, pruning to avoid overfitting, visualizing & interpreting decision trees, ensemble learning including the concept of combining multiple models
✓ Bagging and Random Forest including what is bootstrap aggregation (bagging), applying bagging to linear regression, architecture of Random Forest, feature selection and splitting, out-of-bag (OOB) error, hyperparameter tuning for getting the best performance
✓ Boosting algorithms including architectures of Gradient Boosting algorithms (AdaBoost, GBMs), XGBoost and its advantages, LightGBM and its efficiency, CatBoost and its categorical feature handling, fine-tuning boosting algorithms using hyperparameter tuning
6. Time Series Forecasting
✓ Introduction to Time Series Forecasting including what can be forecasted, key definitions such as point forecast, prediction interval, ex-ante vs. ex-post forecast, time series cross-validation, model evaluation metrics including MASE and custom metrics like direction
✓ Understanding Time Series including components of time series and visual identification, additive and multiplicative decomposition, autocorrelation and partial autocorrelation, stationary time series
✓ Feature Engineering including dsitributed lag variables, month-over-month (MoM) and year-over-year (YoY) analysis, Box-Cox transformations, detrending and seasonal adjustments
✓ Statistical tests including Box-Pierce test, Ljung-Box test, Augmented Dickey-Fuller test, Granger Causality test, Cox-Stuart test
✓ Univariate Forecasting Algorithms including simple models such as moving average, naive, drift models as benchmarks; simple exponential smoothing, Holt and Holt-Winter models; introduction to ARIMA and SARIMA models, finding p,d,q parameters, Facebook's Prophet model and customizing seasonality & holidays
✓ Multivariate Forecasting Algorithms including (S)ARIMA models with exogenous variables, vector autoregression models (VAR, VARMA, VARMAX), volatility models (ARCH and GARCH) for stock price prediction
bonus materials
✓ Gain access to a collection of 50 conceptual exercises designed to help you prepare for machine learning interviews. These exercises cover a wide range of topics and algorithms in Machine Learning.
✓ Receive a handy Machine Learning Algorithms cheatsheet for quick reference. This cheatsheet provides key concepts and comprehensive list of algorithms of supervised, unsupervised learning and time series forecasting
✓ Leverage Machine Learning templates designed to quickly get you started on any Machine Learning problem statement.
✓ Learn more algorithms such as survival analysis, mixed effect models, hierarchical time series models with pre-recorded videos
1. Fundamentals of Machine Learning
✓ Introduction to Machine Learning including a foundational overview of machine learning concepts, bias-variance tradeoff, key terminology, train-validation-test methodology, k-fold cross-validation, nested cross-validation, interpretability methods including LIME and SHAP, L1 and L2 regularization, ensembling techniques, hyperparameter tuning
✓ Python Libraries such as scikit-learn for Machine Learning, statsmodels and Facebook's Prophet for statistical modelling, PyMC3 for Bayesian modelling, Yellowbrick for Machine Learning visualization
✓ Data Preprocessing for Machine Learning including missing value imputation using mean-median-model, arbitrary values, frequent categories and KNN-based imputation; one-hot encoding for categorical variables; handling outliers, influential points and leverage points; feature scaling using standardization, minmax scaling, robust scaling
✓ Feature Selection and Engineering techniques including stepwise selection, recursive feature elimination for feature selection and aggregation, extracting time information for feature engineering
2. Supervised Learning: Regression
✓ Introduction to Linear Regression including simple and multiple linear regression, statistical assumptions and associated tests, residual analysis, variance inflation factor for testing multicollinearity, interpreting results
✓ Variations of Linear Regression including regularized regression with LASSO, Ridge and ElasticNet regression, polynomial regression, quantile regression, Dirichlet regression, robust regression, Bayesian regression
✓ Other Methods including Generalized Additive Models (GAM), leveraging partial dependence plots for interpretability, regularized regression with LASSO, Ridge and ElasticNet regression, polynomial regression, quantile regression, Dirichlet regression, robust regression, support vector regression
✓ Evaluation Metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute % Error (MAPE), R-Squared, Adjusted R-Squared, Predicted R-Squared, AIC and BIC
3. Supervised Learning: Classification
✓ Introduction to Classification highlighting difference between binary, multi-class and multi-label classification, importance of classification threshold
✓ Introduction to Logistic Regression including why it is termed a regression, the mathematical underpinnings, statistical assumptions and associated tests, interpreting results
✓ Other Classification Algorithms including k-nearest neighbours (kNN), support vector machine (SVM), Naive Bayes (Gaussian, Bernoulli, Multinomial), and when to use which algorithm
✓ Evaluation Metrics including confusion matrix, accuracy, precision, recall, F1-score, specificity, cost-based metrics, area under curve of ROC curve
4. Supervised learning: tree-based models
✓ Introduction to Tree-Based models including key terminology, use in regression & classification, decision tree construction from scratch, splitting criteria, pruning to avoid overfitting, visualizing & interpreting decision trees, ensemble learning including the concept of combining multiple models
✓ Bagging and Random Forest including what is bootstrap aggregation (bagging), applying bagging to linear regression, architecture of Random Forest, feature selection and splitting, out-of-bag (OOB) error, hyperparameter tuning for getting the best performance
✓ Boosting algorithms including architectures of Gradient Boosting algorithms (AdaBoost, GBMs), XGBoost and its advantages, LightGBM and its efficiency, CatBoost and its categorical feature handling, fine-tuning boosting algorithms using hyperparameter tuning
5. Unsupervised Learning
✓ Introduction to Unsupervised Learning explaining the differences between supervised and unsupervised learning, real-world applications, what is semi-supervised learning
✓ Clustering techniques including k-means clustering, its algorithmic approach, advantages and limitations; hierarchical clustering methods and dendogram visualization; when to choose which technique
✓ Association Rule Mining techniques including key terminology such as support, confidence and lift; Apriori algorithm, how it discovers frequent itemsets and generates association rules; introduction to Eclat algorithm and approach; comparing Apriori and Eclat algorithms
✓ Dimensionality Reduction techniques including understanding the curse of dimensionality, factor analysis for reducing feature dimensions, interpretation of factors; introduction to principal component analysis (PCA); linear discriminant analysis (LDA) and discriminant functions
6. Time Series Forecasting
✓ Introduction to Time Series Forecasting including what can be forecasted, key definitions such as point forecast, prediction interval, ex-ante vs. ex-post forecast, time series cross-validation, model evaluation metrics including MASE and custom metrics like direction
✓ Understanding Time Series including components of time series and visual identification, additive and multiplicative decomposition, autocorrelation and partial autocorrelation, stationary time series
✓ Feature Engineering including dsitributed lag variables, month-over-month (MoM) and year-over-year (YoY) analysis, Box-Cox transformations, detrending and seasonal adjustments
✓ Statistical tests including Box-Pierce test, Ljung-Box test, Augmented Dickey-Fuller test, Granger Causality test, Cox-Stuart test
✓ Univariate Forecasting Algorithms including simple models such as moving average, naive, drift models as benchmarks; simple exponential smoothing, Holt and Holt-Winter models; introduction to ARIMA and SARIMA models, finding p,d,q parameters, Facebook's Prophet model and customizing seasonality & holidays
✓ Multivariate Forecasting Algorithms including (S)ARIMA models with exogenous variables, vector autoregression models (VAR, VARMA, VARMAX), volatility models (ARCH and GARCH) for stock price prediction
7. Live Project with Mentorship
✓ Project Introduction including introduction to the business problem, overview of dataset to be used
✓ Data preprocessing and cleaning including data loading using Pandas, data preprocessing such as handling missing values and outliers, data transformation and feature engineering with NumPy and Pandas
✓ Basic Exploratory data analysis (EDA) including statistical analysis and visualization using Pandas and Seaborn, and identifying patterns and correlations in the data
✓ Data Visualization and Insights including designing data visualizations with Matplotlib and Seaborn, creating informative and visually appealing plots, extracting actionable insights, and presenting findings
✓ Machine Learning including splitting into training and test sets, model selection, model training, model evaluation, hyperparameter tuning, model interpretability using SHAP and model deployment with Flask
✓ Final Deliverables will include a Jupyter notebook containing code, data visualizations and model output, upon which feedback would be provided
bonus materials
✓ Gain access to a collection of 50 conceptual exercises designed to help you prepare for machine learning interviews. These exercises cover a wide range of topics and algorithms in Machine Learning.
✓ Receive a handy Machine Learning Algorithms cheatsheet for quick reference. This cheatsheet provides key concepts and comprehensive list of algorithms of supervised, unsupervised learning and time series forecasting
✓ Leverage Machine Learning templates designed to quickly get you started on any Machine Learning problem statement.
✓ Learn more algorithms such as survival analysis, mixed effect models, hierarchical time series models with pre-recorded videos
Instructor
anant agarwal
Anant works as a Data Science Manager at a Fortune Global 500 company. An alumnus of The Doon School and IIT Kharagpur, he holds an MS from University of Minnesota Twin Cities where he received the Forrest Fellowship and MBA from Indian School of Business Hyderabad with Dean's and Merit List awards.
Anant has been featured as a Guest Speaker at Analytics Vidhya's DataHack Summit and DataHour sessions, Zhejiang University China and has been a Judge in Responsible AI category for Altair Global Enlighten Award.
He is also a 2-time 99.8 percentiler in CAT, National-level Squash player and a fingerstyle guitarist.
For any assistance or queries, please reach out to us at [email protected]
Contact Us
© 2024 All Rights Reserved.