Artificial Intelligence and Machine Learning

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Artificial Intelligence and Machine Learning Training Program

Artificial Intelligence and Machine Learning

Become an AI asset to an IT Industry! A non-layoff role!

Artificial Intelligence and Machine Learning Training Program

Course Overview

Our Artificial Intelligence (AI) course provides a comprehensive introduction to the concepts, tools, and applications behind today’s intelligent systems. Designed for beginners and professionals alike, this programme covers foundational AI principles, machine learning techniques, real-world applications, and hands-on project development.
Learners will gain the skills needed to understand, build, and apply AI technologies across multiple industries.

Duration

12 Weeks (Full-time) / 16 Weeks (Part-time)

Batch Size

Maximum 5 students

Prerequisites

Basic IT Knowledge, Python Fundamentals

Certification

Completion Certificate and LoR

Course Curriculum

Our comprehensive curriculum is designed by industry experts to ensure you master the most in-demand cloud computing skills.

Introduction to AI and ML for Beginners

History and evolution of AI

AI applications across domains (Healthcare, Agriculture, Finance, Robotics, etc)

Problem-solving and Search Strategies

Intelligent Agents and Environments

Essential Mathematics for AI/ML.

Fundamentals of Computing and Python Basics

Introduction to Programming Concepts

Setting up a Development Environment (e.g., Jupyter/Colab Notebooks)

Basics of Python

Introduction to Data Types

Introduction to Variables

Introduction to Constants

Introduction to Operators

Introduction to Input-Output

Introduction to Basic formatting

Introduction to Control Structures

Introduction to Loops

Introduction to Functions

Introduction to Libraries (NumPy, Pandas)

Lab 1 - Basic Python Programs

Lab 2 - Basic Python Programs

Data Pre-processing & Exploratory Data Analysis (EDA)

Data Cleaning

Data Representation and Visualization (Histogram and Bar Chart)

Data Cleaning & Pre-processing - Reading and Writing Data

Data Indexing and Selection

Data Frame Operations

Summarizing Data

Identifying Patterns

Basic Statistical Tests

Feature Engineering and Transformation.

Lab1- Perform EDA on a Real Dataset (e.g., Titanic, Iris, or COVID-19 data).

Lab2- Outlier Detection and Handling using Statistical methods (IQR, Z-score).

Introduction to Machine Learning

Difference between AI and ML

Supervised, Unsupervised and Reinforcement Learning

Introduction to different Classification Models

Introduction to Model Evaluation

Confusion Matrix

Precision

Recall

F1-Score

ROC-AUC, hyperparameter tuning, Bias-variance trade off, overfitting & underfitting.

Lab 1- Developing prediction models for classification, train and evaluate classification models (Decision Tree, SVM, KNN).

Lab 2- A mid-level project applying classification on a real-world dataset.

Regression Models and Unsupervised learning

Linear Regression

Multiple Regression

Logistic Regression

Evaluation Metrics for Regression (MSE, RMSE, R²)

Clustering- K-means clustering

Hierarchical Clustering.

Lab 1- Implement Regression from scratch on a Dataset

Lab 2- Perform Clustering on a Real-world Dataset

Deep Learning Techniques and its applications

Dense Feedforward Neural Networks

Introduction to Neural Networks

Interconnections among Nodes

Design issues for Neural Nets

Linear Algebra Notation

Activation Functions

Loss Functions

Backpropagation and Gradient Descent - Gradients, Jacobians, and the Chain Rule

The Backpropagation Algorithm

Iterating Gradient Descent

Introduction to Convolutional Neural Networks

Convolutional Layers

Convolution and Cross-Correlation

Pooling Layers

CNN Architecture

Regularization: Norm Penalties, Dropout, Early Stopping

Generative Models - GANs (Generative Adversarial Networks)

Autoencoders and their Applications.

Lab1- Implement a CNN for Image Classification

Lab 2- Implement a GAN to Generate Synthetic Images.

Book a Free Session

Reserve your spot in our upcoming free AI training session and discover how our program can help advance your career.

Career Progression

Follow your journey from entry-level positions to expert roles in the AI industry

AI Engineer

Work with real datasets and build ML models This is where you begin solving real-world problems. Supervised/Unsupervised learning Model training & evaluation

Average Salary: £34,000 - £45,000

AI ML Engineer / Data Scientist

Build intelligent systems and specialised models. You start working with advanced architectures. Neural Networks, CNNs, RNNs, Deep Learning frameworks (TensorFlow / PyTorch). Natural Language Processing (NLP)

Average Salary: £45,000 - £65,000

AI Research Scientist / Consultant

Scale AI solutions for production. You move from building models to deploying them in real systems. Innovate, lead teams, design future AI solutions. Drive innovation and decision-making.

Average Salary: £75,000 - £110,000+

AI Engineer

Work with real datasets and build ML models This is where you begin solving real-world problems. Supervised/Unsupervised learning Model training & evaluation

Average Salary: £34,000 - £45,000

AI ML Engineer / Data Scientist

AI Research Scientist / Consultant

AI Engineer

Work with real datasets and build ML models This is where you begin solving real-world problems. Supervised/Unsupervised learning Model training & evaluation

Average Salary: £34,000 - £45,000

AI ML Engineer / Data Scientist

AI Research Scientist / Consultant

Ready to Start Your AI Journey?

Join our next batch and transform your career with in-demand skills.

Enroll Now Download Brochure

Testimonials

What Our Students Say

"The hands-on labs with real cloud platforms helped me understand complex cloud architecture concepts. Within 2 months of completing the course, I landed a job as a junior cloud engineer!"

Robert Chen

Cloud Engineer at TechSolutions

"Coming from a system administration background, I was worried about transitioning to cloud. The instructors were incredibly supportive, and the curriculum was well-structured for professionals looking to upskill."

Maria Rodriguez

Cloud Solutions Architect at CloudCorp

Some FAQ

Frequently Asked Questions

Do I need programming experience to take this course?

While some basic programming knowledge is helpful, our course starts with Python fundamentals to ensure everyone can follow along. If you’re completely new to programming, we recommend taking our introductory Python course first.

How much time should I dedicate per week?

For the full-time program, expect to dedicate 30-40 hours per week. For the part-time program, we recommend 15-20 hours per week including classes and assignments.

Will there be job placement assistance?

Yes, we offer comprehensive career support including resume building, interview preparation, and connections with our hiring partners. Our placement rate is over 85% within three months of course completion.

Is there a certificate upon completion?

Yes, upon successfully completing the course and final project, you’ll receive an industry-recognized Artificial Intelligence certification from White Fire Technologies and LOR.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning Training Program