Python, Data Science & AI – Live Instructor-Led Course for Professionals & Teams
Are you a working professional or aspiring data specialist looking to supercharge
your career with real-world, job-ready skills in Python programming,
Data Science, Machine Learning (ML), and Artificial Intelligence (AI)?
If so, you're not alone—thousands of professionals across industries are pivoting
to data and AI roles to stay relevant in a rapidly evolving job market.
At Eduarn, we’ve designed a highly practical, instructor-led course specifically tailored
for individual learners, corporate upskilling programs, and retail training
cohorts. Whether you're a software developer aiming to transition
into machine learning, a business analyst moving toward data science, or a
team leader seeking hands-on AI training for your team, this course offers a strategic
and structured path to mastery.
What sets our program apart is the immersive,
real-time learning model that emphasizes applied knowledge. You won’t just watch
tutorials—you’ll work through real projects, build predictive models, understand
AI algorithms, and deploy machine learning solutions using Python libraries such as
Pandas, NumPy, scikit-learn, TensorFlow, and PyTorch.
This course helps bridge the gap between theory and practice, preparing you for high-demand
roles such as Data Scientist, ML Engineer, AI Analyst, and Python Developer.
With cloud-based deployment, MLOps, and portfolio-ready capstone projects, you’ll walk
away not only with expertise but with proven outcomes.
🌟 Why This Course is a Game-Changer
With the global AI and data market projected to grow to over $1.8 trillion by 2030, the demand for data-savvy professionals shows no signs of slowing. Roles like Data Scientists, ML Engineers, and AI Specialists are growing rapidly. But demand isn't just about jobs—it's about skill: this course ensures you're prepared with **Python expertise (used in 86% of data roles)**, **SQL proficiency**, cloud readiness, and experience in real AI projects.
🏆 Key Benefits
- Hands-on use of **Python**, **Pandas**, **scikit-learn**, **TensorFlow**, and **PyTorch**
- Project-based learning: predictive modeling, NLP, computer vision, and AI pipelines
- Deploy ML models in production using Docker and cloud platforms
- CI/CD integration, versioning, and reproducible research workflows
- Live Q&A, personalized mentorship, and peer-based learning
- Ideal for busy professionals, corporate upskilling, and retail cohorts
📘 Course Outline (30‑Hour Intensive)
- Sessions 1–3: Reviewing Python and data manipulation essentials
- Sessions 4–6: Exploratory Data Analysis & visualization (Matplotlib, Seaborn, Plotly)
- Sessions 7–9: Supervised Machine Learning (regression, classification)
- Sessions 10–12: Unsupervised Learning (clustering, dimensionality reduction)
- Sessions 13–15: Deep Learning fundamentals with TensorFlow/Keras
- Sessions 16–18: NLP & text analytics with Hugging Face
- Sessions 19–21: Model deployment: Docker, FastAPI, cloud deployment
- Sessions 22–24: AI model versioning, monitoring, and MLOps principles
- Sessions 25–27: Real-world capstone project – end-to-end AI pipeline
- Sessions 28–30: Soft skills: storytelling, stakeholder communication, data ethics
📈 Market-Relevant Skills & Hiring Demand
A recent analysis of 1,000+ data scientist jobs shows **Machine Learning required in 69%**, **AI in 21%**, and cloud experience with AWS/Azure in nearly 30%. Python remains dominant at 86%, followed by SQL at 62%.
Whether you’re joining as a Data Engineer, Data Scientist, ML Engineer, or AI Specialist, this course aligns with industry-standard tools and competencies.
👨💼 Who Should Enroll?
- Professional developers and engineers expanding into data & AI
- Data analysts leveling up to machine learning roles
- Corporate groups seeking upskilling in AI-driven business transformation
- Career switchers aiming for data roles or AI innovation tracks
🎓 Outcomes You’ll Achieve
- Build and deploy real ML/AI models with confidence
- Create a portfolio showcasing predictive analytics, NLP, vision, and pipeline code
- Understand cloud deployment architectures and scalable MLOps practices
- Communicate insights and model results effectively to stakeholders
- Be industry-ready for roles like Data Scientist, ML Engineer, or AI Analyst
🛠️ Capstone Project – Real-World Deployment
Participants will design and deploy an end-to-end AI solution—starting from data ingestion, through modeling, to cloud deployment with monitoring and CI pipelines.
📣 Testimonials
"This course helped me build my first production ML API—now deployed with Docker on AWS!" – Sneha R., Data Scientist
"As a team lead, we loved the applied labs and cloud deployment track." – Arun K., Tech Lead
📌 Enroll Now and Transform Your Career!
Whether you're an individual, a corporate team, or a retail learner, this Python + Data Science + ML/AI course delivers expert-led, hands-on training with real business impact.
🎯 Top 25+ Interview Questions for Python, ML & AI
Prepare for your next interview with these commonly asked questions in Python, Machine Learning, and AI.
Python is a high‑level, interpreted programming language known for readability and versatility, widely used in AI and ML for its extensive libraries and frameworks.
Easy syntax, dynamic typing, large standard library, object‑oriented support, cross‑platform compatibility, and strong support for data science libraries like NumPy, pandas, and scikit-learn.
Lists are mutable (can be modified), whereas tuples are immutable (cannot be modified once created).
An anonymous inline function defined with the
lambda keyword, often used for short, throwaway functions. *args and **kwargs mean?*args allows a function to accept any number of positional arguments; **kwargs allows it to accept any number of keyword arguments. Machine Learning (ML) is a subset of AI where algorithms learn patterns from data to make predictions or decisions without explicit programming.
Supervised learning uses labeled data to train models, while unsupervised learning works on unlabeled data to find hidden patterns or groupings.
Overfitting occurs when a model learns noise instead of the underlying pattern, performing well on training data but poorly on new data. Prevent it using techniques like cross-validation, pruning, regularization, or early stopping.
A neural network is a series of interconnected layers of nodes inspired by the human brain, used in deep learning to model complex patterns in data.
Gradient descent is an optimization algorithm that minimizes a loss function by iteratively moving in the direction of the steepest descent (negative gradient).
NumPy, pandas, scikit-learn, TensorFlow, Keras, PyTorch, Matplotlib, and Seaborn.
Regularization adds a penalty term to the loss function to prevent overfitting by discouraging complex models.
Classification predicts discrete labels; regression predicts continuous values.
It refers to problems caused by high-dimensional data where the volume increases exponentially, making data sparse and models less effective.
By imputation (mean, median, mode), removing rows/columns, or using models that support missing values.
The process of selecting, modifying, or creating new features from raw data to improve model performance.
Bias is error from erroneous assumptions; variance is error from sensitivity to training data. Balancing them optimizes model performance.
A table used to evaluate classification model performance by showing true positives, true negatives, false positives, and false negatives.
Using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score.
A subset of ML using multi-layered neural networks to model high-level abstractions in data.
Reusing a pre-trained model on a new, related task to save time and improve performance.
Precision is the ratio of true positives over predicted positives; recall is true positives over actual positives; F1 score is the harmonic mean of precision and recall.
Techniques include resampling (oversampling/undersampling), using appropriate metrics, synthetic data generation (SMOTE), and algorithmic adjustments.
AI is the broader concept of machines simulating intelligence; ML is a subset where machines learn from data; deep learning is a further subset using neural networks.
By serializing the model (e.g., with pickle), creating an API (using Flask or FastAPI), and hosting it on cloud services or servers to serve predictions.
🧠 Test Your Skills Now – Python, AI & ML Quiz
Ready to evaluate your knowledge in Python, Data Science, Machine Learning, and Artificial Intelligence? Take this interactive quiz and test your foundational and advanced skills. Ideal for interview prep and upskilling!
-
Which file extension is used for Python scripts?
A) .pt B) .pyt C) .pyth D) .py
Answer: .py -
What is the output of
len("Hello")?
A) 4 B) 5 C) 6 D) Error
Answer: 5 -
Which of these is immutable in Python?
A) List B) Set C) Tuple D) Dictionary
Answer: Tuple -
Which keyword defines a function in Python?
A) function B) def C) define D) lambda
Answer: def -
Which statement is used as a placeholder in Python?
A) null B) continue C) pass D) stop
Answer: pass -
What is the output of
type([])?
A) tuple B) dict C) set D) list
Answer: list -
Which method is used to add elements to a list?
A) insert() B) extend() C) append() D) push()
Answer: append() -
Which of these is a Python IDE?
A) Eclipse B) PyCharm C) Sublime D) Dreamweaver
Answer: PyCharm -
Which Python library is best for data manipulation?
A) NumPy B) Matplotlib C) Pandas D) Seaborn
Answer: Pandas -
Which library is used for numerical computations?
A) pandas B) NumPy C) matplotlib D) requests
Answer: NumPy -
Which algorithm is used for classification?
A) Linear regression B) Logistic regression C) KMeans D) PCA
Answer: Logistic regression -
What is overfitting in ML?
A) High test accuracy B) High training, low test accuracy C) Low accuracy D) None
Answer: High training, low test accuracy -
Which technique reduces dimensionality?
A) Regression B) Decision Tree C) PCA D) Clustering
Answer: PCA -
Which is a Python package for machine learning?
A) Pandas B) TensorBoard C) Scikit-learn D) Flask
Answer: Scikit-learn -
Which file format is commonly used to save trained ML models?
A) .txt B) .json C) .pkl D) .csv
Answer: .pkl -
What does MLOps stand for?
A) ML Math B) Machine Learning + DevOps C) ML Optimization D) ML Operations
Answer: Machine Learning + DevOps
👉 Think you can score better? Practice more with our interactive quizzes, real-world projects, and instructor-led sessions today!
🎓 How Eduarn LMS Works for Students & Trainers
Eduarn LMS is a modern training and mentorship system designed to streamline learning, communication, and certification — all in one platform.
👩🎓 Student Learning Experience
- Sign Up: Quick registration with email confirmation.
- Access Dashboard: View courses, session schedules, notes, and progress.
- Join Live Classes: Attend instructor-led Zoom/MS Teams sessions (with auto-attendance).
- Course Materials: Downloadable notes, recorded videos, diagrams, and lab exercises.
- Assignments & Quizzes: Regular practice tests, weekly assignments, and feedback.
- Feedback & Support: Submit doubts, feedback, and connect with mentors.
- Course Progress: Track module completion and participation.
- Certification: Earn a Course Completion Certificate after final project/test.
🧑🏫 Trainer & Admin Panel Features
- Trainer Dashboard: Manage courses, session schedules, attendance, and feedback.
- Upload Resources: Notes, videos, assignments, quizzes per module.
- Track Student Activity: Real-time insights into login activity, progress, and quiz scores.
- Evaluate Submissions: Grade assignments, provide inline feedback, and track attempts.
- Certificate Generator: Automatically issue completion certificates to students who qualify.