Artificial Intelligence Engineer Bootcamp eLearning (100% self-paced)

Artificial Intelligence Engineer Bootcamp eLearning (100% self-paced)

Begin Creating Advanced AI Models and Launch a Rewarding Career in Tech!

Become a High-Demand AI Engineer

Our AI Engineer Bootcamp is a practical, self-paced learning program designed for AI enthusiasts eager to fast-track their careers. The bootcamp guides you from the ground up, starting with Math, Statistics, and Python programming, and progressing to building advanced Machine Learning models (Supervised, Unsupervised, and Reinforcement Learning) and Deep Learning systems.

Gain mastery in key areas such as Natural Language Processing, Neural Networks, and Deep Learning through hands-on training from industry experts—and prepare yourself to take on the most in-demand AI roles in tech.

Across the globe, organizations are actively tapping into the power of Artificial Intelligence to enhance service quality, optimize operations, and drive innovation. As a result, the demand for skilled AI professionals is skyrocketing. This is your opportunity to ride the AI wave and take your tech career to the next level.

AI professionals apply cutting-edge tools like Deep Learning and Natural Language Processing, combined with advanced statistical models, to help companies achieve greater efficiency and profitability. Our comprehensive AI Bootcamp equips you with everything you need—from foundational concepts to hands-on experience using the latest AI tools and techniques.

Gain high-demand skills, master the AI landscape, and get job-ready in just a few months.

Key Features

• Course and material in English
• Beginner - Advanced level
• 288 Hours of E-Learning Material
• 9 Capstone Projects for a Job-Ready Portfolio
• Auto-Graded Assessments and Recall Quizzes
• 130+ Guided Hands-on Exercises
• 15 Real-World Case Studies
• Study time: Approximately 5-7 months
• 2 years of access to the learning platform
• Upon successful completion, learners receive a course completion certificate

Learning Outcome

• Python Programming: Master core Python principles, including data types, conditional logic, and creating custom functions.
• Statistical Modeling: Develop a strong foundation in mathematics and statistics essential for AI and Machine Learning.
• Machine Learning with Python: Explore and implement key algorithms like Regression and Classification to build effective ML models.
• Deep Learning: Use powerful frameworks such as Keras and TensorFlow to tackle complex problems with neural networks.
• Natural Language Processing (NLP): Dive into advanced NLP techniques, leveraging tools like NLTK to build language generation and analysis applications.
• AI with Transformers: Understand the distinct capabilities of Transformer models and learn to develop cutting-edge AI applications using them.

Skills You Will Acquire

• Conduct descriptive analytics to summarize data insights
• Apply inferential statistics for data-driven conclusions
• Create custom functions using Python
• Implement object-oriented programming for efficient Python coding
• Debug and resolve data errors using Python tools
• Perform exploratory data analysis and create visualizations
• Clean and prepare datasets through preprocessing techniques
• Handle outliers and missing data effectively
• Execute feature selection and engineering for model optimization
• Construct and fine-tune regression models
• Design, build, and test machine learning models
• Develop and assess classification algorithms for predictive analysis

Target Group

• Beginners with an interest in Artificial Intelligence, looking to enter the tech industry
• Software developers and engineers aiming to upskill in AI and machine learning
• Data analysts or data scientists who want to deepen their AI knowledge
• Students and graduates from computer science, IT, or related fields
• Tech professionals from other domains (like DevOps, QA, or business intelligence) planning to transition into AI roles
• Professionals from non-tech backgrounds (like finance, healthcare, or marketing) with a strong interest in leveraging AI in their industry
• Entrepreneurs or startup founders exploring AI-based solutions for business innovation

Prerequisites

• There are no prerequisites for attending this bootcamp.
• You can learn AI Engineering even without prior technical experience.
• Some exposure to Mathematics, Statistics, Python or SQL will be beneficial.

Stand Out to Recruiters with an Impressive AI Project Portfolio

Develop industry-grade projects that mirror the work of top-performing AI Engineers and craft a compelling portfolio that attracts top-tier employers. Strengthen your expertise, grow your confidence, and position yourself for a high-paying role in AI. Here’s a preview of the kind of projects you’ll work on:

• SleepyFace – Automotive Safety App
• An AI-powered application that analyzes drivers' facial expressions and eye movements to detect signs of drowsiness or fatigue.
• OneArmDistance – Social Distancing Monitor
• A real-time application that uses AI to ensure individuals maintain appropriate social distancing by tracking their movements and proximity.
• PreFace – AI Face Swap Tool
• Build an app that enables users to seamlessly swap faces in videos or animate well-known images with their own, creating realistic visual effects.
• Recco – Personalized Media Recommender
• Develop a smart recommendation system that curates playlists of music and movies tailored to user preferences and viewing/listening history.
• TraffiControl – Smart Traffic Management System
• Design a traffic control app that optimizes signal timing and reduces accidents by analyzing data from surrounding intersections.

Bootcamp Curriculum

1. Mathematics and Statistics Fundamentals

Learning Goals:

• Gain a strong grasp of Descriptive Statistics and the basics of Probability Theory
• Explore various probability distributions, including Normal, Binomial, and Poisson
• Understand Inferential Statistics to make meaningful conclusions from data

Covered Topics:

• Probability
• Statistical Concepts
• Linear Algebra
• Calculus

2. Python for Data Science

Learning Goals:

• Begin with foundational Python programming concepts
• Learn to utilize built-in functions and write your own custom functions
• Work with essential Python libraries like Pandas and NumPy
• Understand how to create visualizations using Python

Topics Covered:

• Python Basics
• Working with Code and Data
• Core Programming Elements
• String Handling
• Data Structures
• Control Flow
• Functions and Modules
• File Operations
• NumPy and Pandas
• Regular Expressions
• Data Visualization

3. Machine Learning with Python

Learning Goals:

• Understand the distinction between Regression and Classification techniques
• Explore a variety of Regression algorithms
• Learn and compare different Classification methods
• Dive into Feature Selection and Feature Engineering
• Learn how to assess and evaluate the performance of ML models

Topics Covered:

• Overview of Machine Learning
• Python Fundamentals
• Data Handling in Python
• Data Visualization Techniques
• Statistical Foundations
• Advanced Analytics Concepts
• Core ML Concepts
• Feature Extraction Techniques
• Regression and Support Vector Machines
• Supervised vs Unsupervised Learning
• Dimensionality Reduction Methods
• Ensemble Learning Techniques
• Recommender Systems and Association Rules

4. Deep Learning with Keras and TensorFlow

Learning Objectives:

• Begin with the core concepts of Neural Networks and Deep Learning
• Gain hands-on experience using TensorFlow and Keras libraries
• Understand and build Convolutional Neural Networks (CNNs)
• Explore Generative Adversarial Networks (GANs) and their applications

Topics Covered:

• Introduction to Deep Learning
• Setting Up and Working with TensorFlow
• Fundamentals of CNNs
• Advanced Architectures in CNNs
• Basics of Natural Language Processing (NLP)
• Building and Training GANs
• Applying AI in Real-World Scenarios

5. Natural Language Processing

Learning Objectives:

• Gain a clear understanding of Natural Language Processing (NLP) and its real-world applications
• Learn how to generate human-like text using Natural Language Generation techniques
• Get hands-on experience with tools like NLTK for processing language data
• Explore various use cases of NLP across industries

Topics Covered:

• Introduction to NLP
• Key Concepts in NLP
• Feature Extraction Techniques
• Using TextBlob for NLP Tasks
• NLP Applications with spaCy
• Building Text Classification Models
• Implementing Text Summarization
• Exploring Attention Mechanisms
• Topic Modeling Techniques
• Performing Sentiment Analysis
• Creating and Deploying Chatbots

6. Applied Machine Learning

Learning Objectives:

• Master both fundamental and advanced concepts in machine learning
• Acquire practical experience in implementing and deploying ML algorithms
• Analyze real-world case studies across multiple industries

Topics Covered:

• Identifying and Defining Business Requirements
• Machine Learning Applications Across Sectors
• Explainable Machine Learning Techniques
• Building and Managing Modeling Pipelines
• ML Model Deployment and MLOps
• Industry-Standard Best Practices in Machine Learning

7. Basics of Reinforcement Learning

Learning Objectives:

• Explore the three core paradigms of Machine Learning
• Understand how the Reinforcement Learning loop operates
• Gain hands-on experience with the OpenAI Gym environment
• Learn how to balance exploration vs. exploitation
• Study Contextual Bandit problems and their applications

Topics Covered:

• Introduction to Reinforcement Learning
• Single-Step Reinforcement Learning: Multi-Armed Bandits
• Multi-Step Reinforcement Learning Concepts
• Practical Approaches for Applying Reinforcement Learning in Real-World Scenarios

8. Getting Started with Advanced AI Using Transformers

Learning Objectives:

• Work with large-scale Transformers having billions of parameters for natural language processing
• Explore multimodal neuron functionality in Vision Transformers
• Understand the concept of Economic Artificial General Intelligence (E-AGI)

Topics Covered:

• The Revolutionary Shift Brought by Transformer Models
• Exploring Billion-Parameter Transformers in NLP
• Vision Transformers and Multimodal Neuron Capabilities
• Introduction to Economic Artificial General Intelligence (E-AGI)

9. Computer Vision for Aspiring AI Experts

Learning Objectives:

• Understand the fundamentals of image processing
• Explore deep learning components used in feedforward neural networks
• Master Convolutional Neural Networks (CNNs) and their practical uses
• Learn techniques for image segmentation and object recognition

Topics Covered:

• Basics of Image Processing
• Image Classification Techniques
• Understanding and Applying CNNs
• Enhancing CNN Performance
• Techniques for Segmentation and Object Detection

FAQ

What Training Format Is Available ?

The Bootcamp is delivered via our interactive and immersive learning platform, in a flexible On-Demand Self-Learning format. This self-paced course gives you the freedom to learn whenever and wherever it suits you. You can take your time with challenging topics, revisit lessons as often as needed, and pause or replay videos to reinforce your understanding. With 2-year access to the course materials, you’ll have ongoing support to refresh concepts and clear doubts anytime you choose.

Can I Take This Course While Working Full-Time?

Yes, you can! We understand that balancing a full-time job and upskilling can be demanding. That’s why our Bootcamp is available in a flexible, part-time format designed specifically for working professionals. With the Flex option, you can learn at your own pace without disrupting your current commitments.

Do I Need Any Specific Software for This Bootcamp?

You’ll need a web browser like Google Chrome, Microsoft Edge, or Firefox. Additionally, an Anaconda setup is required—but don’t worry, it will be installed as part of the program.

To attend the AI Bootcamp online smoothly, it’s recommended that you have a laptop or desktop with at least 8GB of RAM and a stable internet connection.

What Skills Are Required to Become an Artificial Intelligence Engineer?

To thrive as an AI Engineer, you’ll need a solid grasp of programming languages like Python, C++, R, or Java. Additionally, strong analytical thinking and problem-solving skills are essential for building intelligent systems and models.

What Are the Benefits of Becoming an Artificial Intelligence Engineer?

As AI continues to revolutionize industries, becoming an AI Engineer places you at the forefront of technological innovation. Entering the field during its rapid growth means you’ll have the opportunity to shape the future of computing, unlock high-paying job opportunities, and make a significant impact across sectors. It’s a career path that offers both professional advancement and the chance to drive meaningful change.

What Are the Most Popular Artificial Intelligence Frameworks You’ll Learn in This Bootcamp?

In the AI Engineer Bootcamp, you’ll gain hands-on experience with the most widely used tools and frameworks in the AI industry. These include:

• Python – the go-to language for AI and ML development
• NumPy and Pandas – essential for data manipulation and analysis
• Keras and TensorFlow – powerful frameworks for building and training deep learning models
• SQL – for managing and querying structured data

These tools are integral to developing real-world AI applications and will equip you with the practical skills needed to launch a successful career in Artificial Intelligence.

What Are the Major Common Challenges in Artificial Intelligence?

Artificial Intelligence faces several key challenges:

Lack of Clean and Structured Data:

AI systems require massive amounts of high-quality, well-labeled data to function effectively. However, gathering such data is difficult due to data being unstructured, inconsistent, or incomplete. Additionally, strict data privacy regulations in many regions limit access to usable datasets.

Shortage of Skilled Talent:

As AI technology rapidly evolves, there's a growing gap between the pace of innovation and the availability of professionals with the right expertise. The industry urgently needs more trained individuals who can develop, maintain, and optimize AI systems.

These challenges continue to shape the trajectory of AI adoption and development across industries.

Who Can Become an Artificial Intelligence Engineer?

Anyone with a passion for analytical thinking and logical problem-solving can pursue a successful career in AI. While prior experience in programming or a tech-related background can be helpful, it’s not a strict requirement.

What truly matters is your willingness to learn. Skills like programming, mathematics, and statistics are all teachable—and with the right mindset and a well-structured learning program, anyone can develop the capabilities needed to thrive as an AI Engineer.

What Can You Become After Completing the AI Bootcamp?

Upon completing the AI Bootcamp, you'll have the skills and knowledge to pursue a range of roles in the field of Artificial Intelligence, including:

• Machine Learning Engineer – Design and deploy ML models that power intelligent applications.
• Data Scientist – Analyze complex data to derive actionable insights using AI and statistical tools.
• AI Engineer – Build and implement AI models across industries for automation, prediction, and optimization.
• Business Intelligence Developer – Use data modeling and visualization to support strategic decisions.
• Computer Vision Engineer – Work on applications like facial recognition, image classification, and object detection.
• Natural Language Processing (NLP) Specialist – Develop AI systems that understand and process human language.

The bootcamp prepares you to take on high-impact, in-demand roles across various sectors using real-world tools and practical training.

Roles and Responsibilities of an Artificial Intelligence (AI) Engineer

An AI Engineer plays a pivotal role in designing and deploying intelligent systems that can learn and make decisions. Their responsibilities typically include:

• Collaborating with Data Scientists and Business Analysts to understand business requirements and transform them into AI solutions.
• Designing and developing machine learning models that solve specific business problems.
• Testing, validating, and optimizing AI models to ensure accuracy and performance.
• Deploying models into production environments and integrating them with applications.
• Converting ML models into APIs so they can be easily accessed and used by other systems.
• Monitoring AI systems in production for performance, errors, and retraining needs.
• Staying updated with the latest advancements in AI frameworks, libraries, and best practices.

AI engineers bridge the gap between advanced analytics and practical implementation, making data-driven automation a reality for businesses.

Is This Bootcamp Truly Beginner-Friendly?

Absolutely! Around 70–80% of learners who join our Back-End Development Bootcamp have little to no prior coding experience. Unlike many programs that claim to be beginner-friendly but still present early challenges like coding tests or technical screenings, we remove those barriers. Our focus is on guiding you from the ground up. This Bootcamp has been thoughtfully designed in collaboration with industry professionals to help learners—regardless of their starting point—gain practical, job-ready development skills and confidently step into a tech career.

What if I Find the Bootcamp Too Challenging and Need to Drop Out?

If you’re finding the Bootcamp difficult, don’t hesitate to contact our support team. We’re here to help and will do everything we can to guide you through the tough spots and keep you moving forward with confidence. Keep in mind—mastering development skills takes time and effort. While anyone can learn to code, perseverance and a willingness to grow are key to success.