Machine Learning with Python

4.8(31,452 Ratings)

Duration

5 Days

Upcoming Batch

21 December 2026 - 25 December 2026

Batch Options

4 hours & 8 hours

Language

English / Arabic

Course Overview

Machine Learning with Python is a comprehensive, industry-aligned training programme designed to equip professionals with the practical and theoretical expertise required to build, evaluate, and deploy machine learning models using Python. This course focuses on transforming data into actionable insights by applying statistical learning techniques, predictive modelling, and algorithmic optimisation within real-world business and technical contexts.

The programme covers the complete machine learning lifecycle — from data preprocessing and feature engineering to model selection, validation, and performance tuning — using widely adopted Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and an introduction to TensorFlow and PyTorch where applicable. Emphasis is placed on writing clean, efficient, and scalable Python code while following best practices in reproducible machine learning and responsible AI development.

Designed for professionals aiming to work in data science, artificial intelligence, analytics, and software engineering, this course bridges the gap between theoretical concepts and applied machine learning. Participants gain hands-on experience through case studies, real-world datasets, and practical projects that reflect current industry use cases across finance, healthcare, marketing, and technology sectors.

Key Learning Outcomes:

Apply supervised and unsupervised machine learning algorithms using Python
Perform data cleaning, feature selection, and dimensionality reduction
Build, evaluate, and optimise predictive models using industry best practices
Interpret model results and communicate insights to technical and non-technical stakeholders
Implement ethical, secure, and scalable machine learning solutions

Key Reasons Employers Prefer Machine Learning with Python Certified Experts

Machine Learning with Python Course equips professionals with a powerful blend of Python programming, mathematical foundations, and statistical analysis skills required to design, develop, and deploy effective machine learning solutions. In addition to technical proficiency, learners gain in-depth domain knowledge, enabling them to solve complex, real-world problems and stay competitive in the global job market.

Graduates of this certification are highly sought after by top-tier organizations for their proven ability to apply machine learning techniques, analyze large datasets, and deliver data-driven insights, making them well-prepared for high-impact and demanding roles across industries.

Course Content

13 modules · 102 topics

01Module 1: Introduction to Machine Learning5 topics

Basics of Machine Learning
What and why Machine Learning
Applications of Machine Learning
Types of Machine Learning
Main Challenges of Machine Learning

02Module 2: Scikit Learn5 topics

Introduction to Scikit Learn
Features of Scikit-Learn
Conventions
Implementation Steps
DEMO 1 - Scikit- Learn Introduction and model training

03Module 3: Linear Algebra6 topics

Vectors (2D,3D)
Dot Product
Hyperplane
Square, Rectangle
Hypercube
DEMO 2 - Linear Algebra Concept2

04Module 3.1: Probability6 topics

Data types and its measures
Random Variables,its application with variables
Probability-Application with examples
Probability distribution with examples
Sampling Funnel-why And how
DEMO 3 - Probability Concepts

05Module 4: Statistics12 topics

Introduction to Statistics
Basic Statistical Terminologies
Types of Statistics
Descriptive Statistics
Measures of Central Tendency ( Mean, median, mode )
Measures of dispersion ( Variance,Standard Deviation,Range-its derivation )
Measures of Skewness & kurtosis
Inferential Statistics
DEMO 4 - Descriptive_Statistics
DEMO 5 - Statistics methods
DEMO 6 - Correlation
DEMO 7 - Distribution function

06Module 5: Data pre-processing7 topics

Is your data clean
What is Data Pre processing ?
Data cleaning techniques
DEMO 8 - Missing value imputation by Mean, Median
Handling Missing data
Handling Categorical data
DEMO 9 - Handling Categorical Value

07Module 6: Exploratory Data Analysis (EDA)11 topics

Introduction
2D Scatter-plot
3D Scatter-plot
Pair plots
Univariate, Bivariate and Multivariate
Histogram
Box-plot
Variance, Standard Deviation
Median
IQR ( InterQuartile Range)
DEMO 10 - EDA using Iris dataset

08Module 7: Feature Engineering5 topics

Introduction
Need for Feature Engineering in Machine Learning
Steps in Feature Engineering
Feature Engineering Techniques
DEMO 11 - Feature Transformation and Encoding

09Module 8: Performance Metrics & Parameter Tuning5 topics

Confusion Matrix
ROC Curve
Cross Validation in Machine Learning
K fold Cross Validation & Grid search
ML - SUPERVISED LEARNING

10Module 9: Supervised Learning - Regression I12 topics

Linear Regression - Mathematical Intuition
Programming of Linear Regression in Python-scikit learn
DEMO 12 - Simple Linear Regression
Multiple Linear Regression
Multiple Linear Regression - Mathematical Intuition
DEMO 13 - Multi Linear Regression
Polynomial Regression
DEMO 14 - Polynomial Regression
Support Vector Machines
Implementation of SVM In Python
Various Kernels in Support Vector Machines
DEMO 15 - Implement SVM

11Module 10: Supervised Learning - Classification15 topics

Difference between regression and classification
Various Algorithms in Classification
Logistic Regression
DEMO 16 - Logistic Regression
Naive Bayes
DEMO 17 - Naive Bayes
Ensemble Techniques
Introduction to Decision Trees
Introduction to Random Forest
Bagging
Boosting
Developing a Random Forest Model in Python
DEMO 18 - Ensemble Techniques
Mini Project
ML - Unsupervised Learning

12Module 11: UnSupervised Learning - Clustering 9 topics

Unsupervised Learning
Types of Unsupervised Learning
Applications of Unsupervised Learning
Introduction to Clustering Algorithms
Types of Clustering Algorithms
What is K-Means Clustering?
Implementation of K-Means Clustering
Improving Models
DEMO 19 - K-mean Implementation

13Module 12: UnSupervised Learning - Association Rule Mining4 topics

What is Association Rule Mining?
Algorithms in Association Rule Mining
Implementation of Apriori in Python
DEMO 20 - Implementation of Apriori

Schedule Dates

4 upcoming batches

Session Type

Physical sessions run at our training facility.

Machine Learning with Python
Batch Dates	Duration	Batch Options	Language
21 December 2026 - 25 December 2026Next	5 Days	4 hours & 8 hours	English / Arabic
22 March 2027 - 26 March 2027	5 Days	4 hours & 8 hours	English / Arabic
28 June 2027 - 02 July 2027	5 Days	4 hours & 8 hours	English / Arabic
04 October 2027 - 08 October 2027	5 Days	4 hours & 8 hours	English / Arabic

Can’t find a suitable date? Request a schedule that fits your team.

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FAQs

How does this course balance machine learning theory with practical Python implementation?

The course integrates core mathematical and statistical concepts directly into hands-on Python exercises, ensuring learners understand not only how algorithms work, but also how to implement, evaluate, and optimise them effectively in real-world scenarios.

Which machine learning algorithms are covered in this programme?

Participants work with a broad range of algorithms including linear and logistic regression, decision trees, random forests, support vector machines, k-means clustering, hierarchical clustering, principal component analysis (PCA), and ensemble learning techniques.

Does the course address model evaluation and performance optimisation?

Yes. A strong emphasis is placed on model validation techniques, including cross-validation, bias-variance trade-off analysis, hyperparameter tuning, and the selection of performance metrics, to ensure robust and reliable machine learning outcomes.

How much focus is given to data preprocessing and feature engineering?

Data preparation is a core component of the course. Learners gain advanced skills in handling missing data, outliers, categorical encoding, scaling, feature selection, and dimensionality reduction to improve model accuracy and efficiency.

Is this course suitable for professionals transitioning into data science or AI roles?

Yes. The course is structured to support professionals with prior programming or analytical experience who wish to transition into machine learning-focused roles, while also providing depth for those already working in data or software engineering.

Does the programme cover ethical considerations and responsible use of machine learning?

Absolutely. The course addresses key ethical AI principles, including bias detection, fairness, data privacy, and transparency, enabling learners to design machine learning solutions that align with both regulatory and ethical standards.

Are real-world datasets and business use cases included?

Yes. Learners work with real-world datasets and industry-relevant case studies, enabling them to apply machine learning techniques to practical challenges such as forecasting, classification, recommendation systems, and anomaly detection.

This course includes:

Soft copy of Courseware
Certificate of Course Completion
Experienced and Professional Trainer
Lab and Exercises During Training
Sample Questions for Exam Preparation
Mock Test on the Last Day of Training
Exam Registration Guidance
5-Star Training Environment

Upcoming Batch

21 December 2026 - 25 December 2026

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