Complete A.I. & Machine Learning, Data Science Bootcamp — Overview & Key Highlights (2025)
Course Introduction
This bootcamp offers a broad, modern pathway into data science, machine learning, and artificial intelligence using Python. Led by Andrei Neagoie, the course is designed to take learners from the basics to applied, job-ready skills through a structured curriculum, real-world case studies, and portfolio-focused projects.
The training emphasizes practical workflows used in industry—covering data preparation, model selection, evaluation, and presentation—while introducing contemporary tools and libraries such as TensorFlow 2.0, Pandas, NumPy, and Scikit-learn. It’s suitable for complete beginners as well as learners who want a comprehensive refresh across the ML stack.
Instructor: Andrei Neagoie
Duration: ~43.5 hours on-demand video
Rating: 4.6 / 5 (29,352 ratings)
Learners: 157,105 students
Languages: English, Arabic (Auto)
Certificate: Included
Access: Lifetime on mobile & TV
Extras: 1 coding exercise • 61 articles • 14 downloadable resources
Price Note: ₹579 (₹3,459 original)
Coupon Applied: UDEAFFHP22025
Key Highlights
- End-to-end coverage of Data Science, Machine Learning, and AI
- Beginner-friendly start with optional fast-track paths for experienced programmers
- Practical projects and real-world case studies
- Modern ML stack: Python 3, TensorFlow 2.0, Scikit-learn
- Strong focus on data preprocessing, model selection, and evaluation
- Exposure to data engineering concepts and big data tools
- Portfolio-oriented approach for job applications
What You Will Learn
Foundations of Data Science
- Python programming essentials for data workflows
- Working with NumPy for numerical computing
- Data wrangling and analysis using Pandas
- Visual exploration with Matplotlib and Seaborn
Machine Learning Core Concepts
- Supervised and unsupervised learning techniques
- Classification and regression modeling
- Choosing the right model for different problem types
- Model evaluation, tuning, and improvement strategies
Deep Learning & AI
- Neural networks and deep learning fundamentals
- TensorFlow 2.0 workflows
- Transfer learning concepts and applications
- Time-series modeling basics
Real-World ML Workflows
- Cleaning and preprocessing large datasets
- Feature engineering and pipeline design
- Presenting insights and ML results to stakeholders
- Best practices for end-to-end data science projects
Data Engineering & Big Data Exposure
- Overview of industry tools such as Hadoop, Spark, and Kafka
- Understanding how large-scale data systems support ML workflows
Projects & Portfolio Building
- Multiple applied projects with full code and notebooks
- Case studies reflecting real industry scenarios
- Guidance on assembling a resume-ready portfolio
Frequently Asked Questions (FAQ)
Q — Do I need prior experience in math or programming?
A — No. The course starts from the basics and offers separate learning paths depending on your background.
Q — Is this course suitable for complete beginners?
A — Yes. It’s designed to guide beginners step-by-step while still offering depth for more advanced learners.
Q — Does the course focus on real-world applications?
A — Yes. Case studies and projects are designed to mirror real industry workflows and decision-making.
Q — Which tools and libraries are covered?
A — Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow 2.0, and introductions to big data tools.
Q — Will I build a portfolio during the course?
A — Yes. You’ll complete multiple projects suitable for showcasing on a resume or GitHub.
Q — Can this help with job preparation?
A — The course emphasizes practical skills, best practices, and project presentation, all of which are valuable for job readiness.
Why This Course Stands Out
This bootcamp stands out for its balance between breadth and practicality. Rather than focusing narrowly on algorithms, it teaches the full data science workflow—from raw data to deployable insights—using modern tools. The structured progression, combined with real projects and community learning, makes it a strong option for learners aiming for applied ML roles.
