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AI & Machine Learning

Build, train and deploy real machine learning models — from data cleaning and classical ML algorithms to neural networks — through hands-on labs and a final capstone project.

Duration5 Months
ModeClassroom / Online
LevelBeginner → Intermediate
Batch SizeSmall batches
Python NumPy Pandas scikit‑learn TensorFlow Jupyter Git
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Course Fees
Contact for Fees
Flexible EMI options available
  • ✓ Certificate on completion
  • ✓ Free demo class before enrolling
  • ✓ Placement assistance included
  • ✓ Capstone project & portfolio
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Syllabus

What you'll learn, module by module.

⬇ Download Full Syllabus (PDF)

1Python for Data Science›
NumPy, Pandas, data cleaning and exploratory data analysis.
  • Python syntax, functions and data structures for data science
  • NumPy arrays, vectorized operations and broadcasting
  • Pandas DataFrames — reading, cleaning, filtering and merging data
  • Handling missing values, duplicates and outliers
  • Exploratory data analysis with Matplotlib/Seaborn visualizations
2Statistics & Math Foundations›
Probability, linear algebra and statistics for ML.
  • Descriptive statistics — mean, median, variance, standard deviation
  • Probability distributions and hypothesis testing basics
  • Linear algebra — vectors, matrices and matrix operations for ML
  • Correlation, covariance and feature relationships
3Core Machine Learning›
Regression, classification, clustering, model evaluation with scikit‑learn.
  • Supervised learning — linear & logistic regression, decision trees, random forest
  • Unsupervised learning — k-means clustering, PCA and dimensionality reduction
  • Model evaluation — accuracy, precision, recall, F1-score, cross-validation
  • Hyperparameter tuning with GridSearchCV/RandomizedSearchCV
  • Feature engineering and scaling techniques
4Neural Networks & Deep Learning›
Intro to TensorFlow/PyTorch, CNNs and basic NLP.
  • Neural network fundamentals — perceptrons, activation functions, backpropagation
  • Building models with TensorFlow/Keras and PyTorch
  • Convolutional Neural Networks (CNNs) for image classification
  • Introduction to NLP — text preprocessing, tokenization and sentiment analysis
5Capstone Project›
End‑to‑end ML project: data → model → deployed demo.
  • Problem definition, dataset selection and data pipeline design
  • Model training, evaluation and iteration
  • Deploying the trained model as a simple web demo/API
  • Project documentation and presentation for your placement portfolio
Certification

Finish with proof of what you can build.

🎓

Netwisdome Training Institute Course‑Completion Certificate

Awarded on completion of the AI & Machine Learning track and its capstone project — documenting the exact skills and project you delivered, ready to show employers.

FAQs

Common questions about this course.

?Do I need a coding background to join?›
No — each course starts from the fundamentals. Some tracks (like Robotics AI or the Full Stack tracks) move a little faster if you already know basic programming, but no prior experience is required.
?Is this course available on weekends?›
Yes. Weekday, weekend and evening batches are available — pick whichever fits your schedule during enrollment or the free demo class.
?Will I get a certificate?›
Yes, every course ends with a course‑completion certificate documenting the exact skills and capstone project you delivered.
?Is placement assistance included?›
Yes. All courses include resume reviews, mock interviews and referrals to our hiring partners once you're ready — see the Training & Placement page for the full process.
?Can I book a free demo before enrolling?›
Absolutely — use the "Book Free Demo" button on this page or the Contact page to schedule a no‑obligation demo class with the trainer.
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