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Machine Learning Using Python

Unlock the power of data with Aptech’s Machine Learning Using Python course, offered at our Dubai and Sharjah centers. This hands-on program equips learners with essential ML concepts, practical Python skills, and real-world experience in building predictive models. From Python fundamentals and data preprocessing to supervised/unsupervised learning and deployment using Flask, learners gain complete end-to-end exposure. Ideal for aspiring Machine Learning Engineers, Data Scientists, and Analysts, this KHDA-certified course ensures you develop job-ready skills through projects, visualizations, and real datasets.

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Machine Learning Using Python Course Overview

Master the fundamentals of Machine Learning with Python through Aptech’s hands-on training in Dubai and Sharjah. This course covers Python essentials, data preprocessing, visualization, supervised and unsupervised algorithms, and end-to-end model deployment using Flask. Ideal for aspiring data scientists and ML engineers, learners gain practical experience through real-world projects and KHDA-certified training to build job-ready AI skills.

Who Can Enroll?

  • Data Analysts and Engineers
  • Python Programmers exploring AI/ML
  • IT Professionals and Developers
  • Final-year students and fresh graduates

Course Outcomes:

  • Master machine learning using Python and real-world tools
  • Build regression and classification models with evaluation metrics
  • Visualize and interpret complex data
  • Deploy ML models on the web
  • Handle supervised and unsupervised ML tasks efficiently

Course Outline

Module 1: Introduction To Machine Learning

  • What is Machine Learning? Definitions & goals
  • Types of learning: Supervised, Unsupervised, Reinforcement
  • ML terminology, real-life use cases, and roadmap
  • Common languages and tools used in ML
  • Installing software for hands-on ML (e.g., Anaconda, Jupyter)

Module 2: Revisit Python For Machine Learning

  • Core Python concepts: data types, control flow (if-else, loops), functions
  • Working with lists, tuples, sets, dictionaries
  • String operations, lambda functions, OOP basics
  • List comprehensions and best practices for coding

Module 3: Machine Learning Libraries In Python

  • Understanding open-source ML libraries
  • Importing and using NumPy for numerical operations
  • Data handling and analysis with Pandas: Series, DataFrame, filtering, plotting
  • Reading external datasets (CSV, Excel)
  • Mini project using Pandas

Module 4: Data Preprocessing & Visualization

  • Importance of data cleaning and wrangling
  • Feature engineering techniques
  • Handling missing data, string-to-number conversion
  • Standardization and normalization with Scikit-learn
  • Data visualization using Matplotlib and Seaborn
  • Creating 2D and 3D graphs
  • Visualization-based mini project

Module 5: Supervised Learning – Regression

  • Understanding regression vs. classification
  • Splitting data into train/test sets
  • Simple Linear and Multiple Linear Regression
  • Polynomial Regression techniques
  • Model evaluation: R² Score, RMSE
  • House price prediction project

Module 6: Supervised Learning – Classification

  • Logistic Regression, K-Nearest Neighbors (KNN)
  • Model accuracy metrics: confusion matrix, classification report
  • Naive Bayes classification
  • Application of KNN in real datasets
  • Stock market analysis or similar mini project

Module 7: Tree-Based & Ensemble Methods

  • Decision Trees for regression and classification
  • Pruning, overfitting avoidance
  • Hyperparameter tuning using GridSearchCV
  • Cross-validation and k-fold strategies
  • Ensemble Learning: Bagging vs. Boosting
  • Random Forest, Gradient Boosting, XGBoost
  • Mini project on complex datasets

Module 8: Unsupervised Learning Techniques

  • Types of unsupervised learning
  • Dimensionality reduction using PCA
  • Clustering using K-Means algorithm
  • Real-life clustering project (e.g., customer segmentation)

Module 9: Hosting Ml Models On The Web

  • Introduction to Flask for web deployment
  • Flask folder structure and route handling
  • Integrating ML models into web apps
  • Using HTML for front-end rendering
  • Running and deploying an ML-powered web app

Career Opportunities:

  • Machine Learning Engineer
  • Data Scientist / Data Analyst
  • AI Engineer
  • Predictive Modeling Specialist
  • Business Intelligence Analyst

Why Choose Us?

  • KHDA-Certified Training: Get recognized certification to boost your AI career in the UAE
  • Project-Based Learning: Apply ML concepts through real datasets and deployment tasks
  • Hands-On Python Focus: Build strong programming foundations with applied machine learning
  • Expert Trainers: Learn from experienced professionals

Eligibility / Requirements:

  • Working knowledge of Python programming
  • Familiarity with basic statistics and linear algebra is helpful

Notes:

* Course topics and duration may be modified by the instructor based upon the knowledge and skill level of the course participants.

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