🤖 Machine Learning Course — Delhi
Best Machine Learning Course in Dwarka Mor Delhi
Master Machine Learning from scratch — Python, ML Algorithms, Deep Learning, NLP, Computer Vision & Model Deployment — at MMIIT's expert-led training centre near Dwarka Mor Metro, New Delhi.
Course Overview
Course Duration4 Months
Batch OptionsMorning / Evening / Weekend
ModeOffline + Online
CertificateISO Govt. Recognised
Projects12+ Real-World ML Projects
Placement100% Assistance
Rating 4.9/5 (155 reviews)
LocationDwarka Mor Metro Gate 2
4Month Programme
12+Real ML Projects
100%Placement Assistance
4.9★Student Rating
ISOCertified Course
DeepLearning Included
About the Programme
What is the Machine Learning Course at MMIIT?
Machine Learning is the engine powering modern technology — from Google Search and Netflix recommendations to fraud detection, medical diagnosis, self-driving cars, and ChatGPT. Every major industry is actively hiring ML engineers, data scientists, and AI developers, making it one of the highest-paying career paths in India in 2025.
MMIIT's Machine Learning course in Dwarka Mor Delhi is a comprehensive 4-month programme covering the complete ML pipeline — from Python and statistics to advanced Deep Learning, NLP, Computer Vision, and deploying production-ready ML models. You will build 12+ real-world projects including a recommendation system, fraud detector, image classifier, and NLP sentiment analyser.
Located steps from Dwarka Mor Metro Station (Blue Line), MMIIT provides industry ML faculty, GPU-enabled lab environment, and 100% placement support to launch your machine learning career.
📞 Call for Free Demo ClassEnd-to-End ML Pipeline
From raw data collection and cleaning to model training, evaluation, and production deployment — the complete workflow.
12+ Real-World Projects
Build portfolio projects companies actually care about — house price prediction, sentiment analysis, fraud detection, image classification.
Industry ML Engineers
Learn from ML professionals with 10+ years experience building production ML systems at startups and MNCs.
Deep Learning Included
Go beyond classical ML — cover Neural Networks, CNN, RNN, LSTM, and Transformer architectures with TensorFlow and Keras.
Common Question
AI vs Machine Learning vs Deep Learning — Explained
Most students are confused about these three terms before joining. Here is a clear comparison so you know exactly what you are learning:
| Factor | Artificial Intelligence (AI) | Machine Learning (This Course) | Deep Learning |
|---|---|---|---|
| Definition | Broad science of intelligent machines | Machines learning from data using algorithms | ML using multi-layer neural networks |
| Scope | Broadest field — includes ML and DL | Subset of AI — focus on algorithms | Subset of ML — focus on neural nets |
| Key Tools | All AI tools | Scikit-learn, Pandas, NumPy, Matplotlib | TensorFlow, Keras, PyTorch |
| Data Required | Varies | Moderate structured data | Large datasets (images, text, audio) |
| Examples | Chess AI, Siri, Alexa | Spam filter, price predictor, recommender | ChatGPT, image recognition, YOLO |
| Covered in This Course? | Overview only | ✅ Full coverage | ✅ Full coverage (TF + Keras) |
💡 Good news: MMIIT's Machine Learning course covers all three levels — classical ML algorithms AND deep learning with TensorFlow. Call +91-7838180031 for details.
Why Learn Machine Learning?
Where is Machine Learning Used?
Machine Learning is not just a tech skill — it is transforming every industry. Here is where your MMIIT ML training will be applied:
Healthcare
Disease prediction, medical image analysis, drug discovery, and patient outcome forecasting.
Finance & Banking
Fraud detection, credit scoring, algorithmic trading, and customer churn prediction.
E-Commerce
Product recommendations, dynamic pricing, demand forecasting, and customer segmentation.
Autonomous Vehicles
Object detection, lane recognition, pedestrian detection in self-driving car systems.
NLP & Chatbots
Sentiment analysis, machine translation, spam detection, and virtual assistant development.
Manufacturing
Predictive maintenance, quality control, defect detection, and supply chain optimisation.
Complete Curriculum
Machine Learning Course Syllabus at MMIIT
M1
Python & Mathematics for Machine Learning
- Python refresher — NumPy, Pandas, Matplotlib, Seaborn
- Statistics — mean, median, variance, standard deviation
- Probability — distributions, Bayes' theorem, conditional probability
- Linear Algebra — vectors, matrices, dot product, eigenvalues
- Calculus — derivatives, gradient, chain rule for backpropagation
- Data preprocessing — normalisation, standardisation, encoding
- Exploratory Data Analysis (EDA) — patterns, outliers, correlations
M2
Supervised Learning — Regression
- Linear Regression — simple and multiple regression
- Polynomial Regression for non-linear relationships
- Ridge, Lasso, and ElasticNet regularisation
- Model evaluation — MAE, MSE, RMSE, R² score
- Feature engineering and feature selection techniques
- Cross-validation — k-fold, stratified k-fold
- Project — House Price Prediction (Kaggle dataset)
M3
Supervised Learning — Classification
- Logistic Regression — binary and multiclass classification
- Decision Trees — CART algorithm, Gini impurity, entropy
- Random Forest — bagging, feature importance, OOB score
- Support Vector Machine (SVM) — kernel trick, hyperplanes
- K-Nearest Neighbours (KNN) — distance metrics
- Naive Bayes — Gaussian, Multinomial, Bernoulli
- Gradient Boosting — XGBoost, LightGBM, CatBoost
- Model evaluation — accuracy, precision, recall, F1, ROC-AUC
- Project — Customer Churn Prediction / Spam Classifier
M4
Unsupervised Learning & Dimensionality Reduction
- K-Means Clustering — elbow method, silhouette score
- Hierarchical Clustering — dendrograms, Ward linkage
- DBSCAN — density-based clustering for noisy data
- Principal Component Analysis (PCA) — explained variance
- t-SNE for high-dimensional data visualisation
- Anomaly Detection — Isolation Forest, Local Outlier Factor
- Association Rule Mining — Apriori, FP-Growth algorithms
- Project — Customer Segmentation / Market Basket Analysis
M5
Model Selection, Tuning & Pipelines
- Bias-variance tradeoff — overfitting and underfitting
- Hyperparameter tuning — GridSearchCV, RandomizedSearchCV
- Bayesian Optimisation with Optuna
- Ensemble methods — voting, bagging, stacking, blending
- Imbalanced datasets — SMOTE, class weighting, oversampling
- Scikit-learn Pipelines for end-to-end ML workflows
- Saving and loading models — pickle, joblib
- ML experiment tracking with MLflow basics
M6
Deep Learning — Neural Networks
- Artificial Neural Networks (ANN) — perceptrons, layers, activations
- Forward propagation and backpropagation explained
- Activation functions — ReLU, Sigmoid, Softmax, Tanh
- Optimisers — SGD, Adam, RMSProp, AdaGrad
- Regularisation — Dropout, Batch Normalisation, L2
- TensorFlow 2.x and Keras — Sequential and Functional API
- Building and training ANNs on real datasets
- Project — Deep learning model for tabular data classification
M7
Computer Vision & NLP with Deep Learning
- Convolutional Neural Networks (CNN) — filters, pooling, feature maps
- Transfer Learning — VGG16, ResNet50, EfficientNet with Keras
- Object Detection basics — YOLO overview and applications
- Image classification and face recognition projects
- Recurrent Neural Networks (RNN) and LSTM for sequences
- Word Embeddings — Word2Vec, GloVe, FastText
- Transformers and BERT for text classification (Hugging Face)
- Project — Sentiment Analysis + Image Classifier Portfolio
M8
Model Deployment & Career Preparation
- Deploying ML models with Flask REST API
- Building interactive ML apps with Streamlit
- Containerisation basics with Docker for ML models
- Cloud deployment — AWS SageMaker / GCP AI Platform overview
- GitHub portfolio — README, project documentation, demo links
- Kaggle competitions — submitting solutions and improving rank
- ML interview preparation — top 100 questions asked by companies
- Resume building, LinkedIn optimisation, mock technical interviews
📄 Download Full Syllabus PDF — free with course enquiry
Technologies You Will Master
Tools & Technologies
🐍 Python 3.x
🔢 NumPy
🐼 Pandas
📊 Matplotlib
📊 Seaborn
⚙️ Scikit-learn
🚀 XGBoost
⚡ LightGBM
🧠 TensorFlow 2.x
🧱 Keras
🔥 PyTorch (Intro)
👁️ OpenCV
💬 NLTK
🤗 Hugging Face
📈 MLflow
🌐 Flask / FastAPI
🚀 Streamlit
📓 Jupyter Notebook
☁️ Google Colab
🐙 Git & GitHub
Career Opportunities
Jobs After Machine Learning Course
ML Engineers are among the highest-paid professionals in India's tech industry. Here are the roles our students get placed in:
🤖
ML Engineer
Build, train, and deploy machine learning models at scale
₹6–20 LPA📊
Data Scientist
Analyse complex data and build predictive ML solutions
₹6–18 LPA🧠
Deep Learning Engineer
Build neural networks for images, text, and speech
₹8–22 LPA💬
NLP Engineer
Build chatbots, translators, and text processing systems
₹7–18 LPA👁️
Computer Vision Engineer
Build image and video recognition applications
₹8–20 LPA🔬
AI Research Analyst
Research new ML methods and implement SOTA models
₹8–25 LPA🏦
Quant / Risk Analyst
Apply ML for financial modelling and risk prediction
₹7–18 LPA⚙️
MLOps Engineer
Deploy, monitor, and maintain ML models in production
₹8–22 LPA
Eligibility
Who Should Join This Course?
Python Programmers
Developers with basic Python knowledge ready to move into ML, AI, and data science roles.
Data Analysts
Analysts working with Excel, SQL, or Power BI wanting to advance into machine learning and predictive modelling.
Software Developers
Backend and full-stack developers wanting to add ML to their skillset and move into AI product roles.
IT / CS Graduates
B.Tech, MCA, BCA, M.Sc graduates wanting to specialise in ML and AI for higher-paying career opportunities.
Research Enthusiasts
Students and professionals interested in cutting-edge AI research, Kaggle competitions, and academic ML projects.
Career Switchers
Professionals from engineering, banking, or science backgrounds wanting to transition into ML and AI careers.
Student Reviews
What Our Students Say
★★★★★
"MMIIT's Machine Learning course is exceptional. The hands-on projects — from building a neural network from scratch to deploying an ML model on Flask — gave me the confidence to crack interviews. Got placed as an ML Engineer at a Gurgaon startup within 6 weeks of completing the course!"
★★★★★
"I was a data analyst before joining MMIIT's ML course. The Deep Learning and NLP modules were a game-changer. Built a complete sentiment analysis model and image classifier for my portfolio. Got 3 Data Scientist offers and joined the best one with a 70% salary hike!"
★★★★★
"Best ML institute near Dwarka Mor! The faculty explains complex topics like backpropagation and transformers in simple terms. The project guidance for Kaggle competitions was invaluable. Cracked interviews at 2 top AI companies. Highly recommend MMIIT for anyone serious about ML!"
FAQs
Frequently Asked Questions
Have more questions? Call us at +91-7838180031 or visit MMIIT at Dwarka Mor Metro, Delhi.
Free counselling available Mon–Sat, 9AM–8PM.
The Machine Learning course at MMIIT is 4 months long. Flexible morning, afternoon, and weekend batches are available for both students and working professionals at our Dwarka Mor campus — just 2 minutes from Dwarka Mor Metro Station on the Blue Line.
The course covers Python for ML, Statistics and Linear Algebra, Supervised Learning (Regression + Classification), Unsupervised Learning, XGBoost, Deep Learning with TensorFlow and Keras (ANN, CNN, RNN, LSTM), NLP, Computer Vision, Model Deployment with Flask and Streamlit, and 12+ real-world ML projects.
Data Science is a broader field covering data collection, cleaning, analysis, visualisation, and ML modelling. Machine Learning is more focused on building algorithms and models that learn from data. MMIIT's ML course goes deeper into algorithms, deep learning, and model deployment, while our Data Science course covers the full analytics workflow including Power BI and SQL.
Basic Python knowledge is recommended. The course starts with a Python and statistics refresher before ML algorithms. Graduates with any programming background — Java, C++, or JavaScript — can join. Non-programmers should first complete MMIIT's Python course before joining ML.
Students get placed as ML Engineer, Data Scientist, Deep Learning Engineer, NLP Engineer, Computer Vision Engineer, AI Developer, and MLOps Engineer. Average starting salary in Delhi NCR ranges from ₹6–20 LPA — among the highest in the Indian IT industry.
Yes — Deep Learning is fully covered. The MMIIT ML course includes two complete modules on Deep Learning: neural networks, CNN for images, RNN and LSTM for sequences, and NLP with Transformers using TensorFlow, Keras, and Hugging Face. You will build image classifiers and NLP models as portfolio projects.
Yes. MMIIT offers both offline classroom ML training at Dwarka Mor and live online / hybrid classes with the same project support and curriculum quality. Call +91-7838180031 to check online batch availability.
MMIIT is at Plot No. 65, Opposite Gate No. 2, Dwarka Mor Metro Station, Uttam Nagar, New Delhi – 110059. Just a 2-minute walk from the metro exit — easily reachable from Dwarka, Uttam Nagar, Janakpuri, Vikaspuri, Nawada, and Rajouri Garden.
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