AI Engineering Expert

Job Ready Training with the real scenarios and projects. Join our training, get trained, create a portfolio website, and get hired.

Course Program​

AI Engineering Expert

A complete comprehensive AI Engineering and Machine Learning Expert training based on hiring companies’ requirements with a portfolio website. The training includes guided and unguided projects with real-time scenarios as well.

Training Overview

The AI Engineering Expert is an instructor-led training course to master the skills needed for an AI Engineering and Machine Learning job. This training program includes everything from beginner to expert level. The training is organized to upskill your AI and Machine Learning knowledge and skills. Furthermore, the training is aligned with the requirements of the industry’s current AI and Machine Learning projects.

Training Content

The training content given below is a brief. The detailed content and structure will be explained in the introduction session. This comprehensive AI Engineering Expert program trains you to build a career in AI or Machine Learning industries. The training covers core skills: Computer Science Fundamentals, Mathematics, Statistics, Business Understanding, Communication, and Tools Skills: Python, SQL, Git, GitHub, Pandas, EDA, Machine Learning, Deep Learning, NLP or Computer Vision, and ML Ops.

Stage 1 | The Key Sets of Knowledge for AI

01 | Computer Science Fundamentals

Fundamentals of computer science includes: basics fo computing, networking, data representation, programing basics and algorithm basics.

02 | Python Programming

Python programming includes: python fundamentals, data structures, exception handling, functional programming, object-oriented programming, modules and packages, python standard library, and familiarity with data science libraries.

03 | Version Control System (Git, Github)

Git and Github essential concepts includes: setup and configuration, stagging, inspect and compare, branching, remote repositories, temporary commits and Github fork, pull request, code review.

04 | Data Structure and Algorithms

Data Structure and Algorithms Essential Concepts includes: big 0 notation, arrays & linked lists, stacks & queues, hash tables, trees & graphs, sorting algorithms, searching algorithms, string manipulation algorithms, and recursion.

Stage 2 | DataBase and AI Tools

01 | SQL for AI

SQL for AI includes: basic operations, complex queries, views, stored procedures & functions, triggers & events, transactions, database design, indexes, security and permissions.

02 | Data and Visualization Essentials

Data and visualization Essential concepts includes: data type, data structures, key statistical concepts, types of visualization, data visualization process, dashboards, and story telling with data.

03 | Numby, Pandas, Matplotlib, Seaborn

Data visualization tools essential concepts includes: basic operations, matrix operations, slicing, stacking, dataframe basics, handling missing data, grouping data, data concatenation & merging, and data visualization with Matplotlib and Seaborn.

04 | Exploratory Data Analysis

EDA includes: Data collection & loading, data inspection, univariate analysis, bivariate analysis, multivariate analysis, outlier detection, feature engineering, and data visualization for EDA.

Stage 3 | Math, Statistics for AI and Data Handling

01 | Mathematics for AI

Math for AI essential concepts includes: 1. Linear algebra: vectors & matrices, matrix operations, eigenalues & eigenvectors, singular value decomposition. 2. Calculus: derivative & gradients, partial derivatives, chain rules, integrals 3. probability: probability distributions, Bayes’ theorem,random variables, expectation and variance.

02 | Statistics for AI

Statistics for AI essential concepts includes: descriptive statistics (mean, median, mode, standard deviation), hypothesis testing, confidence intervals, and regression analysis.

03 | Data Handling and Processing

Data handling and processing essential concepts includes: data cleaning, data transformation, data integration, and exploratory data analysis.

04 | Assignment and practices

Working with guided and unguided assignments with tech tools to be familiar in the AI concepts.

Stage 4 | Machine Learning

01 | Machine Learning and Deep Learning

ML and DL essential concepts includes respectively: 1. Machine Learning: preprocessing, supervised learning, unsupervised learning, model selection, model training and evaluation, overfitting & underfitting. 2. Deep Learning: nural network, forward propagation, back propagation, building multilayer perception and special neural network architectures.

02 | ML Ops

ML Ops essential concepts includes: API, fastAPI for Python server development, DevOps fundamentals, and getting familiar with a cloud platform such as Microsoft Azure, AWS or GCP.

03 | Computer Vision, GenAI and NLP

CV, NLP, and GenAI essential concepts includes: regex, text presentation, text classification, fundamentals of spacy and NLTP library, image processing techniques, library to use, convolutional neural networks, data processing and augmentation.

04 | LLM and Langchain

LLM and Langchain essential concepts includes: vector database, embeddings, retrieval augmented generation, and lanchain framework.

Stage 5 | Getting Ready for Placement

01 | Working with Projects

There are eight projects to have experience with before you create a portfolio website for you. You will build about eight AI Projects to experiment with.

02 | Creating the portfolio

You will build a website with all of your work to showcase the skills that you expert in. The hiring companies expect you to have a portfolio website to verify your work these days.

03 | Preparing for interviews

You will be having mock interviews before you start attending the real interviews. We will prepare you well to succeed in your interviews.

04 | Landing in a job

We work with different recruiters and online job boards to find a suitable post. Therefore you have a greater chance of getting the job you want.

Training Duration

Same curriculum. Two different durations to complete and land a job. Whether you are enrolling part-time or full-time you will be trained the same.

Full-Time

5 months

24 hours per week

Extended a month for job placement preparation

Part-Time

7 months

12 hours per week

Extended a month for job placement preparation

Training Fee

There are multiple options for paying your training fee. Please choose one of the options or contact us for a flexible one.

Option 01

Once-off

$2,600

Pay upfront: get a discount on your training fee.

Full price

$3,250

Discounted

20%

Option 02

Initial fee

$1,250

Pay an initial fee, followed by two installments.

Each installment

$800 (800*2)

Discounted

12.3%

Option 03

Initial fee

$1,000

Pay an initial fee, followed by two installments.

Each installment

$1,000 (1,000*2)

Discounted

7.6%

Work on Real World Projects that hiring companies are preferred

DeepFake Face Detection

Deep Face Detection refers to using deep learning techniques, particularly Convolutional Neural Networks (CNNs) and advanced architectures, to identify and locate faces in images or video streams. Deep face detection is a critical first step in many facial recognition systems, emotion detection, and biometric authentication.

AI Based Learning Assistance

AI-based learning assistance refers to leveraging Artificial Intelligence (AI) technologies to create intelligent systems that enhance the learning process for students, educators, and institutions. These systems provide personalized, adaptive, and efficient ways to support learning across various disciplines, enabling a more tailored and engaging educational experience.

Cyber Fraud App Detection

Cyber Fraud App Detection involves using AI and machine learning techniques to identify malicious or fraudulent mobile applications that pose a security risk. These apps may be involved in phishing, stealing user data, spreading malware, or conducting unauthorized financial transactions.

Audio DeepFake Detection

Audio DeepFake Detection focuses on identifying manipulated or synthetic audio created using AI. DeepFake audio technology generates realistic-sounding voices capable of mimicking human speech patterns, tone, and style, often used maliciously for impersonation, misinformation, or fraud.

Student Certificate Validation

Student Certificate Validation refers to the process of verifying the authenticity of academic or professional certificates issued by educational institutions or training providers. The goal is to ensure that certificates are legitimate, reducing the risk of fraud in academic and professional domains.

Student Engagement Prediction

Student Engagement Prediction involves analyzing and forecasting a student’s level of engagement in learning activities. Engagement can be physical (attendance), emotional (interest and motivation), and cognitive (effort and focus). Predicting engagement helps institutions improve learning outcomes.

Oral Cancer Prediction

Oral Cancer Prediction using AI involves leveraging machine learning (ML) and deep learning (DL) techniques to analyze medical data, such as clinical images, biopsy results, or patient history, to detect oral cancer at an early stage. Early detection is crucial as it significantly increases treatment success and reduces mortality rates.

Poverty Prediction

Poverty Prediction using AI aims to identify and forecast areas or individuals living in poverty by analyzing various socio-economic, demographic, and environmental factors. Accurate poverty prediction helps governments, non-profits, and policymakers allocate resources effectively and design targeted interventions to reduce poverty.

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