Machine Learning with Python Training in Astana

Machine Learning with Python Training in Astana, Kazakhstan

Participants will learn how to process data, build predictive models, evaluate their performance, and deploy machine learning applications using widely adopted Python libraries.

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Machine Learning with Python Training is a professional training program delivered by ProgNXT in Astana, Kazakhstan. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Machine Learning with Python Training course with hands-on, instructor-led sessions. This course provides a comprehensive introduction to Machine Learning using Python, focusing on both theory and practical implementation....

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: SJD15
21 Hrs
Last Updated: April 2026

Course Rating5/5

This course provides a comprehensive introduction to Machine Learning using Python, focusing on both theory and practical implementation. Participants will learn how to process data, build predictive models, evaluate their performance, and deploy machine learning applications using widely adopted Python libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib.

Welcome to the official Machine Learning with Python Training certification program in Astana, Kazakhstan. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Proficiency in Python programming
Basic understanding of statistics and linear algebra
Familiarity with Jupyter Notebooks is helpful
Prior exposure to data analysis is beneficial but not required

By the end of the course, participants will be able to:

Understand core concepts of supervised, unsupervised, and reinforcement learning
Preprocess and clean data for machine learning models
Implement algorithms like linear regression, decision trees, SVMs, and clustering
Evaluate model performance using various metrics
Tune models for optimal performance
Deploy models using Python frameworks

Data preprocessing and exploration
Building ML models using Python
Feature engineering and selection
Model evaluation and validation techniques
Hyperparameter tuning
Use of libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
Introduction to model deployment techniques

Python for Data Science
Data Cleaning and Preparation
Supervised vs Unsupervised Learning
Model Evaluation Metrics
Bias-Variance Tradeoff
Feature Selection Techniques
Model Tuning (GridSearch, Cross-Validation)
Basic Model Deployment Concepts

Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion.

Course Agenda

Introduction to Machine Learning

What is Machine Learning?
Categories of ML: Supervised, Unsupervised, Reinforcement
ML workflow: Data to Deployment
Overview of ML libraries in Python

Python for Machine Learning

Essential Python syntax
Data structures, functions, and control flow
Introduction to NumPy and Pandas

Data Preprocessing and Exploration

Handling missing values and outliers
Data normalization and encoding
Exploratory Data Analysis (EDA)
Visualization with Matplotlib and Seaborn

Supervised Learning Techniques

Linear and Logistic Regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)

Unsupervised Learning

K-means Clustering
Hierarchical Clustering
Dimensionality Reduction (PCA)

Model Evaluation and Tuning

Confusion Matrix, Precision, Recall, F1 Score
ROC-AUC and Cross-Validation
GridSearchCV and Hyperparameter tuning
Avoiding overfitting/underfitting

Working with Real-World Datasets

Feature engineering
Building end-to-end workflows
Case studies (e.g., house price prediction, customer segmentation)

Introduction to Model Deployment

Saving models with Pickle and Joblib
Using Flask for simple API-based deployment
Overview of tools like Streamlit and FastAPI

Hands-on exercises

Jupyter notebooks with hands-on examples
Real-world datasets
Presentation slides for each topic
Cheat sheets for Scikit-learn, Pandas, NumPy

Quizzes and hands-on assignments
Participation in lab activities and discussions

ProgNXT Certificate of Completion will be awarded to participants who successfully complete the course and assessments.

Machine Learning with Python Training Events in astana

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

26th Oct 2026→28th Oct 2026

November 2026

📍 Astana, Kazakhstan

2nd Nov 2026→4th Nov 2026

🌐 Online Instructor-Led

7th Nov 2026→9th Nov 2026

📍 Astana, Kazakhstan

21st Nov 2026→23rd Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

🌐 Online Instructor-Led

4th Dec 2026→6th Dec 2026

📍 Astana, Kazakhstan

11th Dec 2026→13th Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→23rd Dec 2026

📍 Astana, Kazakhstan

26th Dec 2026→28th Dec 2026

Events for other courses in Astana, Kazakhstan

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Machine Learning with Python Training in Astana