Data Science and Machine Learning is a practical, hands-on training program designed to help participants develop the skills needed to transform raw data into meaningful insights and predictive models. The course combines essential data science concepts, data analysis, machine learning algorithms, and modeling techniques to provide learners with a structured understanding of how data-driven solutions are developed and applied to real-world problems.
Duration 5 days – 35 hrs
Overview
The Data Science and Machine Learning training course is an intensive, hands-on program designed for data professionals and technical practitioners who want to strengthen their ability to build, optimize, interpret, and prepare advanced machine learning solutions for real-world applications. The course goes beyond basic modeling by covering the critical stages of the machine learning lifecycle, from advanced data preparation and feature engineering to model optimization, explainability, and deployment.
Participants will work with practical datasets and industry-relevant scenarios to develop a deeper understanding of how data quality, feature selection, algorithm choice, and model configuration influence machine learning performance. The training introduces advanced techniques for data preprocessing, feature engineering, dimensionality reduction, handling imbalanced datasets, and building reusable machine learning pipelines using Python and Scikit-Learn.
The course provides extensive coverage of both supervised and unsupervised machine learning, allowing learners to work with regression, classification, clustering, and advanced ensemble approaches. Participants will explore powerful modeling techniques such as Random Forests, XGBoost, LightGBM, CatBoost, bagging, boosting, stacking, and blending, while learning how to select and compare models based on appropriate evaluation metrics.
A major component of the training focuses on model optimization and performance improvement. Participants will practice cross-validation, hyperparameter tuning, and optimization techniques using tools such as GridSearchCV, RandomizedSearchCV, and Optuna. These activities help learners develop a systematic approach to improving model accuracy, reliability, and generalization rather than relying on trial and error.
The course also introduces deep learning with TensorFlow and Keras, covering neural network architecture, activation and loss functions, optimization, and practical applications of convolutional and recurrent neural networks. Through guided exercises, participants will build and train deep learning models and evaluate their performance on practical datasets.
Beyond model development, learners will explore machine learning interpretability, fairness, and responsible modeling. Using tools such as SHAP and LIME, participants will learn how to explain model predictions, identify influential features, and communicate model behavior to technical and non-technical stakeholders. The course also addresses potential sources of bias and the importance of evaluating models beyond accuracy alone.
The final stage focuses on deployment and production readiness, introducing practical approaches for serving machine learning models through Flask, FastAPI, or Streamlit. Participants will also gain an overview of MLOps practices, including version control, model monitoring, reproducible workflows, and continuous integration and deployment concepts.
By the end of the Data Science and Machine Learning course, participants will have completed a practical end-to-end workflow that demonstrates their ability to prepare complex datasets, engineer meaningful features, select and optimize machine learning models, apply ensemble and deep learning techniques, interpret model predictions, evaluate model risks, and prepare a machine learning solution for deployment. This provides learners with a stronger foundation for tackling advanced data science projects and contributing to production-oriented AI and machine learning initiatives.
Learning Objectives
- Apply advanced data preprocessing and feature engineering techniques
- Build and evaluate a variety of supervised and unsupervised ML models
- Implement ensemble learning methods including bagging, boosting, and stacking
- Design and train deep learning models using TensorFlow or Keras
- Perform hyperparameter tuning using GridSearchCV, RandomizedSearchCV, and Bayesian Optimization
- Leverage interpretability tools to explain model behavior and predictions
- Prepare and deploy machine learning models into production environments
Audience
- Proficiency in Python programming
- Basic understanding of ML concepts (e.g., regression, classification)
- Familiarity with key Python libraries: Pandas, NumPy, Matplotlib, and Scikit-Learn
- A working knowledge of statistics, linear algebra, and probability
Prerequisites
- Familiarity with AI concepts (optional but helpful).
- Basic understanding of application development principles.
- Fundamental Programming experience is required
Course Content
Day 1: Advanced Feature Engineering and ML Pipelines
- Data preprocessing techniques: scaling, encoding, handling missing values
- Feature engineering and selection methods
- Dimensionality reduction: PCA, t-SNE, and UMAP
- Building reusable ML pipelines with Scikit-Learn
- Managing imbalanced data: SMOTE, class weighting
Day 2: Core Supervised and Unsupervised Modeling
- Evaluation metrics: ROC AUC, F1 score, and more
- Advanced regression and classification algorithms
- Cross-validation strategies: k-fold, stratified, time-series
- Unsupervised learning: clustering with KMeans, DBSCAN
- Hands-on Challenge: Model selection using real-world datasets
Day 3: Ensemble Learning and Model Optimization
- Bagging techniques and Random Forests
- Boosting methods: AdaBoost, XGBoost, LightGBM, CatBoost
- Stacking and blending strategies
- Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- Introduction to Bayesian Optimization with Optuna
Day 4: Deep Learning with TensorFlow/Keras
- Fundamentals of neural network architecture and training
- Understanding activation functions, loss functions, and optimizers
- Overview of CNNs and RNNs: concepts and use cases
- Model training and evaluation using TensorFlow/Keras
- Hands-on: Build a deep learning model for classification or regression
Day 5: Model Interpretability and Deployment
- Model explainability using SHAP and LIME
- Addressing fairness and bias in ML models
- Introduction to deployment: Flask, FastAPI, and Streamlit
- MLOps basics: version control, monitoring, and CI/CD workflows
- Capstone Project: Build, explain, and prepare a model for deployment
