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⭐ High Demand — 2026 📍 Dilsukhnagar, Hyderabad ✅ Completion Terms Apply

Machine Learning Course

A rigorous, hands-on Machine Learning course taught by an active Senior Data Scientist from an MNC — covering every major ML algorithm, deep learning foundations, real production deployment and career preparation. Build models on real datasets from week one.

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DURATION

10 Weeks

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LEVEL

Beginner to Advanced

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MODE

Online · Offline · Hybrid

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CERTIFICATE

Industry Certificate

Tools & Technologies You Will Learn

Python Scikit-learn TensorFlow Keras PyTorch NumPy Pandas Matplotlib XGBoost LightGBM MLflow FastAPI Streamlit Docker Jupyter Git

Enroll on WhatsApp → View Curriculum

Machine Learning Course at LearnAI Tech Hub

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Free Demo Available
Contact us for current batch fees & EMI options

⏱ Duration: 10 Weeks
🎯 Level: Beginner to Advanced
💻 Online · Offline · Hybrid
🏆 Completion certificate after meeting course requirements
📁 Guided project and portfolio preparation
🧭 Prerequisites explained before enrollment
📞 Current trainer and batch details available before payment

Ask About Enrollment Book Free Demo Class Call: +91 93900 23585

Ask for the current syllabus, trainer, schedule, fees and support terms before enrolling.

About This Course

Machine Learning is not just a skill — it is the engine that powers every AI system

Every Generative AI model, every AI agent, every recommendation engine and every fraud detection system is built on Machine Learning foundations. Understanding ML — how algorithms learn, why they fail, how to evaluate and deploy them — is what separates AI professionals who build production systems from those who only know how to use existing tools. ML engineers are consistently among the highest-paid technology professionals across every geography.

At LearnAI Tech Hub, this 10-week course covers the complete machine learning landscape — from linear regression to deep neural networks — with real projects every week. You will implement algorithms from first principles, build production-grade pipelines, deploy models as live APIs and learn how to monitor and maintain ML systems in production. By the end, you have a portfolio of 8 real ML projects across different domains — the strongest possible foundation for an AI engineering career.

What makes this course different

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Taught by a Senior Data Scientist with 8+ years at MNC

Every algorithm is explained through the lens of real production problems — not academic toy examples. Your trainer has deployed ML models serving millions of users.

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Every algorithm implemented from scratch AND with libraries

You understand what the algorithm is actually doing mathematically, then implement it efficiently using industry tools. This depth is what separates ML engineers from tool users.

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Production-first mindset from week one

Model deployment, MLOps, monitoring and cost management are integrated throughout — not added as an afterthought in the final week.

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GenAI integration in every module

How Large Language Models connect to traditional ML, how to use AutoML and AI assistants to accelerate ML workflows — all taught as part of the standard curriculum.

Course Curriculum

8 modules · 10 Weeks · Guided practical work

The sequence below describes the current course plan. Ask for the dated batch syllabus because tools and module order may change.

Python & ML Foundations

10 topics · Weeks 1–2

Python for ML: NumPy, Pandas, Matplotlib deep dive

The ML workflow: problem → data → features → model → evaluate → deploy

Types of ML: supervised, unsupervised, reinforcement learning

Train/test split, cross-validation and data leakage prevention

Feature engineering: scaling, encoding, selection

Project: End-to-end ML pipeline on a real business dataset

Supervised Learning — Regression

10 topics · Week 3

Linear Regression: OLS, gradient descent, regularisation (Ridge, Lasso)

Polynomial regression and non-linear relationships

Evaluation metrics: MSE, RMSE, MAE, R² — when to use which

Multicollinearity, heteroscedasticity and assumption testing

Feature importance in regression models

Project: House price prediction model with real estate data

Supervised Learning — Classification

12 topics · Week 4

Logistic Regression: sigmoid, decision boundary, multi-class

Decision Trees: entropy, gini, pruning and overfitting

Random Forest: bagging, feature importance, OOB error

Support Vector Machines: kernels, margin and hyperparameters

K-Nearest Neighbours and Naive Bayes

Evaluation: confusion matrix, precision, recall, F1, ROC-AUC, PR curve

Project: Credit card fraud detection system

Ensemble Methods & Boosting

10 topics · Week 5

Ensemble theory: bagging, boosting, stacking

Gradient Boosting Machines (GBM) from scratch

XGBoost: parameters, tree building and regularisation

LightGBM: leaf-wise growth and speed advantages

CatBoost for categorical features

Hyperparameter tuning: GridSearch, RandomSearch, Optuna

Project: Winning a Kaggle-style competition with XGBoost

Unsupervised Learning

8 topics · Week 6

K-Means clustering: algorithm, elbow method, silhouette score

DBSCAN for density-based clustering and anomaly detection

Hierarchical clustering and dendrograms

PCA: eigenvalues, explained variance and dimensionality reduction

t-SNE and UMAP for high-dimensional data visualisation

Project: Customer segmentation for e-commerce personalisation

Deep Learning Foundations

12 topics · Weeks 7–8

Neural networks: perceptrons, activation functions, forward pass

Backpropagation and gradient descent variants (SGD, Adam, RMSProp)

Building neural networks with TensorFlow/Keras and PyTorch

Regularisation: dropout, batch normalisation, early stopping

CNNs for image data: convolutions, pooling, feature maps

RNNs and LSTMs for sequential and time series data

Transfer learning: using pre-trained models (ResNet, EfficientNet, BERT)

Project: Image classification CNN on medical imaging data

NLP & Time Series with ML

10 topics · Week 9

Text preprocessing, TF-IDF and word embeddings

Sentiment analysis, text classification and named entity recognition

Time series decomposition: trend, seasonality, residuals

ARIMA, SARIMA for time series forecasting

ML models for time series: XGBoost with lag features

Prophet for business time series forecasting

Project: Sales forecasting system for retail company

MLOps, Deployment & Career Preparation

10 topics · Week 10

MLflow for experiment tracking and model registry

Model deployment with FastAPI — building a live ML API

Streamlit for ML model demo interfaces

Docker for containerising ML applications

Model monitoring: data drift, concept drift and retraining triggers

ML interviews: case studies, coding rounds and system design

HR placement referral and portfolio review

Get Full Curriculum on WhatsApp →

Career Relevance

Roles where these skills may be useful

Training does not guarantee a job, salary, promotion or internship. Outcomes depend on prior experience, project quality, assessment performance and employer requirements.

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Machine Learning Engineer

AI product companies, tech startups, e-commerce, FinTech, healthcare AI companies

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Data Scientist

Every industry — BFSI, healthcare, logistics, IT services, consulting firms globally

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AI Research Scientist

AI labs, R&D departments, universities, Fortune companies with AI research teams

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ML Platform Engineer

Cloud companies, AI infrastructure teams, ML platform companies

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Predictive Analytics Engineer

BFSI, e-commerce, manufacturing, supply chain, operations analytics teams

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Healthcare ML Specialist

Healthcare tech companies, hospitals, pharma companies, health insurance firms

Ask for current career-support terms

We can explain the included resume, portfolio and interview support before enrollment. Employer decisions remain independent.

Who Is This For

This course is designed for you if…

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Engineering and science graduates

ML is the fastest path from an engineering or science degree to a high-paying AI career. Your mathematical foundation makes you a natural fit for the algorithmic depth this course covers.

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Data analysts wanting to move into ML engineering

If you analyse data but cannot yet build models, this course closes that gap — and with it, typically a 60–100% salary increase when you move from analyst to ML engineer.

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Software developers transitioning to AI/ML

You already know how to code. This course gives you the ML knowledge to shift from building software systems to building intelligent systems — one of the most significant career moves in tech today.

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Final year B.Tech, M.Tech and MCA students

An ML portfolio project built on real data is the single most effective way to stand out at campus placements or off-campus applications in 2026.

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Domain professionals in healthcare, finance, manufacturing

Domain knowledge + ML skills = domain-specialised ML engineer, which commands a higher salary than a generic ML engineer and is easier to place because of the domain fit.

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GenAI learners wanting engineering depth

If you use ChatGPT and want to understand and build the underlying systems — ML is the foundation. This course takes you from tool user to system builder.

Prerequisites

Basic Python programming (variables, loops, functions) Intermediate school-level mathematics (basic algebra and statistics) No prior ML experience needed Curiosity and commitment to work through challenging concepts

FAQs

Questions about Machine Learning Course?

Not finding your answer? Ask for the current syllabus, prerequisites, trainer, fees and batch schedule.

Ask on WhatsApp →

Is Machine Learning the same as AI?

Machine Learning is a subset of AI. AI is the broad goal of creating intelligent systems. ML is one specific approach — teaching machines to learn from data by finding patterns and making predictions. Deep Learning is a further subset of ML using neural networks. When people say "AI engineer" today, they usually mean someone who works with ML and deep learning systems.

How long does it take to get a job after completing this course?

Based on our placed students, freshers typically receive job offers within 2–4 months of completing the course, going through our HR team's placement process. Working professionals transitioning careers typically take 3–6 months. Results depend on how actively you pursue opportunities with our HR team's support.

Do I need strong maths for Machine Learning?

You need basic algebra and statistics — which is covered in the first week. You do not need advanced calculus or linear algebra to start. We build the mathematical intuition gradually as part of the curriculum. Our trainer explains the maths behind algorithms in plain language before showing the implementation.

How does this compare to online courses like Coursera or Udemy?

Three key differences. First, this is live instruction from an active MNC professional — not recorded content. Second, you get project feedback, doubt-clearing sessions and mentor support. Third, after the course our 50+ HR team actively works on your placement — Coursera cannot do that.

What is the difference between the Data Science course and the ML course?

Data Science is broader — it covers the full data pipeline from data cleaning and EDA through to ML and deployment, plus BI tools like Power BI and SQL. The ML course goes deeper into algorithms, model mathematics and MLOps. Many students do Data Science first, then ML as a specialisation. Both courses include placement support.

Can I join online from outside Hyderabad or India?

Yes. All online sessions are live and instructor-led. We have students from across India, UAE, USA, UK, Australia, Canada and Singapore currently enrolled. Recordings are available for sessions you miss. Placement support is available for students regardless of location.

Machine Learning Course

Machine Learning is not just a skill — it is the engine that powers every AI system

Taught by a Senior Data Scientist with 8+ years at MNC

Every algorithm implemented from scratch AND with libraries

Production-first mindset from week one

GenAI integration in every module

8 modules · 10 Weeks · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Engineering and science graduates

Data analysts wanting to move into ML engineering

Software developers transitioning to AI/ML

Final year B.Tech, M.Tech and MCA students

Domain professionals in healthcare, finance, manufacturing

GenAI learners wanting engineering depth

Prerequisites

Questions about Machine Learning Course?

Review the Machine Learning Course before you enroll

Machine Learning Course

Machine Learning is not just a skill — it is the engine that powers every AI system

Taught by a Senior Data Scientist with 8+ years at MNC

Every algorithm implemented from scratch AND with libraries

Production-first mindset from week one

GenAI integration in every module

8 modules · 10 Weeks · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Engineering and science graduates

Data analysts wanting to move into ML engineering

Software developers transitioning to AI/ML

Final year B.Tech, M.Tech and MCA students

Domain professionals in healthcare, finance, manufacturing

GenAI learners wanting engineering depth

Prerequisites

Questions about Machine Learning Course?

Review the Machine Learning Course before you enroll

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