ML with Python Training is an intensive three-day hands-on program designed to help participants build a strong practical foundation in machine learning using Python. This course focuses on end-to-end machine learning workflows including data preparation, model building, evaluation, and interpretation using Python-based tools and libraries. Participants gain the ability to develop, test, and apply machine learning models for real-world business and technology use cases.

• Understand machine learning with Python
• Learn Python-based ML workflows
• Understand data preprocessing techniques
• Gain knowledge of predictive modeling basics
• Learn model training and evaluation concepts
• Understand data visualization workflows
• Explore ML libraries and tools
• Identify practical ML use cases

• Aspiring machine learning engineers
• Data analysts and data scientists
• Software developers working with data
• Technology professionals transitioning into AI roles
• Graduate students and early-career professionals

• Basic knowledge of Python programming
• Familiarity with basic mathematics and statistics concepts
• Understanding of data handling using spreadsheets or datasets
• Interest in applying machine learning techniques programmatically

• Implementing machine learning workflows using Python
• Preparing and preprocessing data for machine learning
• Building supervised and unsupervised ML models
• Evaluating model performance and accuracy
• Applying responsible and interpretable ML practices

By the end of this training, participants will be able to develop machine learning models using Python, prepare and analyze data effectively, evaluate and optimize models, understand ethical and practical considerations, and apply machine learning techniques confidently to real-world problems.

Python for Machine Learning
• Python ecosystem for machine learning
• Working with NumPy and Pandas for data handling
• Data loading, exploration, and cleaning
• Feature selection and preprocessing

Foundations of Machine Learning with Python
• Machine learning workflow and lifecycle
• Training and testing datasets
• Understanding bias and variance
• Introduction to scikit-learn

Supervised Learning – Regression
• Linear regression concepts
• Multiple regression using Python
• Model training and prediction
• Evaluating regression models

Supervised Learning – Classification
• Classification problems and use cases
• Logistic regression and decision trees
• k-Nearest Neighbors and Naive Bayes
• Classification performance metrics

Model Evaluation and Optimization
• Train-test split and cross-validation
• Overfitting and underfitting
• Hyperparameter tuning
• Improving model performance

Feature Engineering Techniques
• Handling categorical variables
• Feature scaling and normalization
• Dimensionality reduction concepts
• Improving model interpretability

Unsupervised Learning with Python
• Clustering techniques and use cases
• k-Means and hierarchical clustering
• Principal Component Analysis
• Interpreting unsupervised model results

End-to-End Machine Learning Project
• Problem definition and data understanding
• Model selection and training
• Evaluation and result interpretation
• Presenting insights from ML models

Responsible and Practical Machine Learning
• Bias, fairness, and ethical considerations
• Model explainability and trust
• Deployment readiness concepts
• Best practices for real-world ML projects

• Python for machine learning
• Data preprocessing concepts
• Predictive modeling techniques
• Model training and evaluation
• Feature engineering basics
• Data visualization workflows
• ML libraries and frameworks
• Supervised and unsupervised learning
• Performance optimization concepts
• Practical ML with Python scenarios

• Hands-on Python-based ML exercises
• Model building and evaluation assessment
• End-to-end ML mini project
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in ML with Python Training, validating their expertise in building, evaluating, and applying machine learning models using Python for practical applications.