Build a Career in AI, Machine Learning, and Data Science | Udemy Coupons 2025

A warm welcome to the Build a Career in AI, Machine Learning, and Data Science course by Uplatz.

Are you curious about Artificial Intelligence, Machine Learning, or Data Science but unsure where to begin? Do titles like AI Engineer, ML Research Scientist, or Data Science Manager sound exciting but confusing? This course is your beginner-friendly guide to understanding what these cutting-edge tech careers are really about—no advanced math or coding knowledge required!

“Build a Career in AI, Machine Learning, and Data Science” is designed for students, professionals, job seekers, and career changers who want to explore the world of AI and data careers from a clear, career-focused perspective. You’ll learn about high-demand roles, skill requirements, career paths, tools and technologies, job titles, salaries, and the evolving trends shaping this dynamic industry.

By the end of this course, you’ll have a strong understanding of:

• What AI Engineers, ML Engineers, Deep Learning Engineers, and Data Scientists actually do
• The difference between applied roles, research roles, and managerial positions in the AI/ML field
• The tools and technologies used in AI/ML, such as Python, TensorFlow, PyTorch, and cloud platforms
• The skills, educational paths, and certifications needed to break into or grow within the AI/ML ecosystem
• Career progression and salary expectations across various AI/ML and data-related roles
• How to build your personalized roadmap into AI or data science based on your interests and background

Whether you’re from a technical, non-technical, academic, or business background, this course will provide the clarity, structure, and motivation you need to step confidently into the world of artificial intelligence and data science.

No prior experience is required—just curiosity and a passion for learning in one of the most exciting fields of the future.

Key Benefits of Learning This Course

1. Understand the core differences between AI, machine learning, deep learning, and data science
2. Learn what top roles like AI Engineer, ML Research Scientist, and Data Science Manager actually involve
3. Discover the skills, tools, and technologies most in demand in AI/ML careers
4. Get clarity on job titles, responsibilities, and career progression paths
5. Explore specialized fields such as computer vision, deep learning, and research in AI
6. Know the typical salary ranges and hiring trends across the AI/ML industry
7. Identify which role suits your background—engineering, research, or leadership
8. Build a step-by-step roadmap to start or transition into an AI/ML career
9. Save time by starting with a structured and guided overview of the entire career landscape
10. Gain the confidence to pursue a career in one of the fastest-growing and most impactful fields in tech

Build a Build a Career in AI, Machine Learning, and Data Science – Course Curriculum

1. Become a Machine Learning Engineer
2. Become a Deep Learning Engineer
3. Become a Computer Vision Engineer
4. Become an AI Engineer
5. Become a AI/ML Research Scientist
6. Become a Chatbot Developer
7. Become a Data Science Manager

Job Roles

Understanding the career landscape in AI, Machine Learning, and Data Science will help you target job roles such as:

• Machine Learning Engineer – Design and develop machine learning models, algorithms, and systems to solve real-world problems using data.
• Deep Learning Engineer – Specialize in neural networks and deep learning frameworks to build intelligent applications like speech recognition or image classification.
• AI Engineer – Create intelligent systems that simulate human behavior, combining ML, natural language processing, and computer vision.
• AI/ML Research Scientist – Conduct in-depth research in artificial intelligence and machine learning, contributing to the development of new models and methodologies.
• Computer Vision Engineer – Focus on building systems that interpret visual information using deep learning and image processing techniques.
• Data Science Manager – Lead data science teams, manage end-to-end analytics projects, and align data-driven initiatives with business goals.
• Data Scientist – Analyze large datasets to uncover patterns, generate insights, and build predictive models using statistical and machine learning techniques.
• NLP Engineer – Develop natural language processing systems such as chatbots, language models, and text analytics solutions.
• AI Product Manager – Manage the development and deployment of AI-powered products, working closely with engineers and data teams.
• MLOps Engineer – Operationalize machine learning models, manage deployment pipelines, and ensure model performance in production environments.
• Data Analyst transitioning into AI/ML – Use existing analytics experience as a stepping stone into more technical AI/ML roles.
• Academic Researcher or PhD Student in AI/ML – Apply theoretical knowledge in real-world research settings or further academic study.
• AI Consultant – Advise businesses on how to implement AI/ML strategies, tools, and solutions that align with organizational goals.