What is the duration of the Machine Learning course in Chennai?

The Machine Learning & Deep Learning course runs for 3–4 months, with live sessions 3 times per week. You can also follow at your own pace using recorded sessions.

Do I need prior coding experience for the ML course?

No prior ML experience is needed. Basic Python knowledge is helpful but we cover all Python fundamentals before moving to ML concepts.

ML & Deep Learning Course Chennai

📚 Course Syllabus

ML & Deep Learning Skills You'll Master

Every module is outcome-driven — you build something real at the end of each one.

Linear algebraProbabilityGradient descentCost functionsBias-variance

Understand the math behind every ML algorithm

Compute gradients manually

Explain model behavior analytically

Understand WHY every ML algorithm works, not just how to call it

Linear RegressionPolynomialRidge/LassoR²Residual analysis

Build regression models from scratch

Prevent overfitting with regularization

Interpret model coefficients for business

Predict continuous outcomes with regression models

Logistic RegressionKNNSVMDecision TreeRandom ForestNaive Bayes

Train binary and multi-class classifiers

Tune decision boundaries

Evaluate with precision, recall, F1

Build classification models for spam, fraud, and medical detection

K-MeansDBSCANHierarchicalPCAt-SNE

Discover hidden patterns in unlabeled data

Reduce data dimensionality

Visualize high-dimensional datasets

Segment customers and discover hidden structure in any dataset

k-Fold CVGridSearchCVRandomizedSearchLearning curvesEarly stopping

Avoid data leakage in validation

Tune hyperparameters systematically

Diagnose underfitting and overfitting

Build models that generalize well to real production data

PerceptronActivation functionsBackpropagationGradient descentNumPy NN

Build a neural network from scratch in NumPy

Implement forward and backpropagation manually

Understand every weight and gradient

Truly understand how neural networks learn

Keras SequentialDense layersDropoutBatchNormCallbacksTensorBoard

Build and train deep neural networks

Prevent overfitting with regularization

Monitor training with TensorBoard

Train deep networks on real datasets with high accuracy

Conv2DMaxPoolingTransfer learningVGGResNetData augmentation

Build image classifiers from scratch

Apply transfer learning to new domains

Achieve 95%+ accuracy with limited data

Build image recognition models like those in phones and cameras

RNNLSTMGRUSequence modelingText classification

Build sequence models for time-series

Apply LSTM to sentiment analysis

Process variable-length text inputs

Build models that understand sequential and time-dependent data

PickleStreamlitFastAPIDockerCI/CD basicsModel monitoring

Deploy models as REST APIs

Build interactive model demos

Monitor models in production

Deploy and maintain ML models in production systems

🛠 Tech Stack

ML & DL Frameworks You'll Use

Industry-standard tools you'll use throughout this program — exactly what employers want.

🧠

Scikit-learn

Classic ML

🔥

TensorFlow

Deep learning

⚡

PyTorch

Research + prod

📊

Matplotlib

Visualization

🎯

Streamlit

App deploy

🌐

FastAPI

Model serving

🐳

Docker

Containerization

📊

TensorBoard

Training monitor

💻 Real Projects

AI Models & Projects You'll Build

Portfolio-ready projects that demonstrate your skills to recruiters from day one.

PROJECT 01

End-to-End Churn Predictor

XGBoost pipeline: raw data → features → model → Streamlit dashboard.

XGBoostScikit-learnStreamlitPandas

✅ Live deployed prediction app

PROJECT 02

CIFAR-10 Image Classifier

CNN with ResNet-style architecture achieving 90%+ accuracy on image classification.

TensorFlowKerasCNNData Augmentation

✅ Production-grade image recognition model

PROJECT 03

MNIST Neural Network from Scratch

Build and train a neural network using only NumPy — no ML libraries.

NumPyMatplotlibPure Python

✅ Deep understanding of how NNs actually work

PROJECT 04

Sentiment Classifier with LSTM

Train an LSTM to classify movie reviews as positive or negative.

TensorFlowLSTMTokenizerIMDb

✅ Sequence model deployed as an API

🎯 Transformation

ML Engineer Roles You'll Land

By the end of this program, you won't just have knowledge — you'll have career-changing proof.

🤖

ML Engineer

₹10L–₹35L roles at top companies

🧠

Deep Learning Expert

Build CNNs, RNNs, and custom architectures

🚀

Model Deployer

Ship models to production independently

📊

Data Intuition

Understand and interpret any model

🏢 Placement Network

Our Students Work Here

Frequently Asked Questions

About the Machine Learning & Deep Learning Course in Chennai

The Machine Learning & Deep Learning course runs for 3–4 months, with live sessions 3 times per week. Recorded sessions are also available.

No prior ML experience is needed. Basic Python knowledge is helpful but all Python fundamentals are covered before ML concepts.

Yes. You will build a spam classifier, house price predictor, image recognition system and a neural network from scratch using TensorFlow and PyTorch.

ML engineers in Chennai typically earn ₹6–15 LPA for freshers. With strong projects and skills, senior roles can command ₹20 LPA and above.

Yes. We provide resume building, mock interviews, portfolio guidance and job referrals to help you land ML and data science roles.

Yes. AlgoAcademy provides end-to-end career support including resume preparation, mock technical interviews and job referrals for ML engineer roles.

Entry-level ML engineers in Chennai typically earn ₹6–12 LPA. With strong projects in TensorFlow and PyTorch, senior roles can command ₹20 LPA and above.

Machine Learning & Deep Learning Course in Chennai