Project-based learning
Build real work you can show in interviews.
Course details
Artificial Intelligence & Applied Machine Learning
Learn. Build. Apply. Deploy. — industry-oriented AI & applied ML.
Taught by Rohan Mehta · AI Mentor
CertificateIncluded on completion
Lifetime access
Revisit lessons anytime after you enroll.
Mentor support
Learn with guidance from industry experts.
Career-ready skills
Practical modules aligned to real roles.
Overview
What this course covers
A 3-month industry-oriented program: 12 weeks, 2–3 live classes per week, hands-on projects, an industry capstone, and professional certification. Build AI systems you can ship — from foundations through applied machine learning.
Outcome
Finish with practical skills and a portfolio-ready project in Artificial Intelligence & Applied Machine Learning you can use for jobs, freelancing, or your next role.
Skills
What you'll learn
AI & ML foundations for industry roles
Learn → Build → Apply → Deploy workflow
Hands-on projects every module
Industry capstone with mentor review
Career paths: AI/ML Engineer, Data Scientist, GenAI roles
Professional certification on completion
Audience
Who this course is for
Beginners
Starting Artificial Intelligence & Applied Machine Learning from scratch and want a clear path.
Students & grads
Building a portfolio and interview-ready skills.
Working pros
Upskilling into ai roles with practical projects.
Before you start
Prerequisites
- Basic computer and internet skills
- Curiosity and consistency to practise weekly
- Open to learners of every background
Syllabus
Curriculum overview
12 modules · week-by-week topics · labs · certificate on completion
01Week 1: Python for AI
Phase 01 · Build the Foundation — get productive in Python for AI work.
Topics covered
- Environment setup
- Jupyter notebooks
- Variables
- Loops
- Functions
Lab: Environment setup & first Python notebook
02Week 2: Real-World Python
Work with real files and APIs the way industry teams do.
Topics covered
- Data structures
- File handling
- CSV
- JSON
- APIs
Lab: Build a small data-processing utility
03Week 3: Data Analysis
Turn messy tables into analysis-ready data with NumPy and Pandas.
Topics covered
- NumPy
- Pandas
- DataFrames
- Cleaning
- Transformation
Lab: Clean and transform a real dataset
04Week 4: Visualization & Statistics
Explore patterns visually and summarize data for decisions.
Topics covered
- EDA
- Matplotlib
- Seaborn
- Variance
- Correlation
- Outliers
Lab: EDA insights report with charts
05Week 5: ML Foundations
Learn how machine learning problems are framed and prepared.
Topics covered
- Supervised learning
- Unsupervised learning
- Feature engineering
- Preprocessing
Lab: Prepare features for a first ML model
06Week 6: Regression Systems
Build prediction systems and measure how well they perform.
Topics covered
- Linear regression
- Multiple regression
- MAE
- MSE
- RMSE
- R²
Lab: Train and evaluate a regression model
07Week 7: Classification
Phase 02 · Build Intelligent Systems — classify outcomes with confidence.
Topics covered
- Logistic regression
- Decision trees
- Precision
- Recall
- F1
Lab: Compare classifiers with precision & recall
08Week 8: Advanced ML
Level up models with ensembles, validation, and hyperparameter tuning.
Topics covered
- Random Forest
- Bagging
- Boosting
- Cross validation
- Tuning
Lab: Tune an ensemble model end to end
09Week 9: Neural Networks
Understand deep learning building blocks and train your first network.
Topics covered
- Neurons
- Layers
- Activation functions
- TensorFlow
- Keras
Lab: Build a small neural network in Keras
10Week 10: Generative AI & LLMs
Connect LLMs to real data with prompts, embeddings, and retrieval.
Topics covered
- Prompt engineering
- AI APIs
- Embeddings
- Vector search
- RAG
Lab: Prototype a RAG / GenAI application
11Week 11: SQL & Business Intelligence
Query business data and present KPIs stakeholders can use.
Topics covered
- SQL queries
- Joins
- Power BI
- ETL
- DAX
- AI-assisted analytics
Lab: Build a BI dashboard from SQL data
12Week 12: Deployment & Capstone
Deploy your work, polish the portfolio, and complete the industry capstone.
Topics covered
- Streamlit
- Gradio
- GitHub portfolio
- Final presentation
Lab: Ship capstone + present outcomes
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After the course
You'll walk away ready
By completing this course, you will have the practical skills and a portfolio project in Artificial Intelligence & Applied Machine Learning to confidently move forward in your career.
- Job-ready ai fundamentals
- A completed capstone you can showcase
- Certificate of completion from Syncpedia
Faculty
Your course mentor
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