Machine Learning Course in Hyderabad
Build, evaluate and tune machine learning models with Python and scikit-learn
The Machine Learning course teaches you to build predictive models from data using Python. You start with Python, NumPy, pandas, visualisation and the statistics needed for modelling, then study the machine learning workflow: framing problems, splitting data, feature engineering, preventing data leakage and choosing evaluation metrics. You learn supervised algorithms including linear and logistic regression, k-nearest neighbours, naive Bayes, support vector machines, decision trees, random forests and gradient boosting with XGBoost or LightGBM, and unsupervised methods such as k-means, hierarchical clustering, DBSCAN and PCA. Later modules cover handling imbalanced data, hyperparameter tuning, time series forecasting basics, recommender systems, model explainability with SHAP, saving and serving models with FastAPI, and an introduction to MLOps with MLflow. The course suits graduates, analysts and developers; basic programming helps and Python is taught from the basics. You complete labs on real datasets and a capstone project. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Python & Data Libraries
- •Python data types, functions and modules
- •NumPy arrays and pandas DataFrames
- •Jupyter, Colab and project structure
- •Lab: Load, clean and summarise a dataset
- •Outcome: Work fluently with data in Python
Statistics & Exploratory Analysis
- •Descriptive statistics, distributions and sampling
- •Correlation, hypothesis testing and confidence intervals
- •Visual EDA with Matplotlib and Seaborn
- •Lab: Write an EDA report with findings and questions
- •Outcome: Understand data before modelling
Machine Learning Workflow & Evaluation
- •Problem framing and baselines
- •Train, validation and test splits; cross-validation
- •Bias-variance trade-off; metrics for regression and classification
- •Lab: Build a baseline and evaluate it properly
- •Outcome: Evaluate models honestly
Linear & Logistic Regression
- •Linear regression, assumptions and diagnostics
- •Ridge, lasso and elastic net
- •Logistic regression, odds and decision thresholds
- •Lab: Predict sales and classify churn with linear models
- •Outcome: Build interpretable models
KNN, Naive Bayes & SVM
- •k-nearest neighbours and distance metrics
- •Naive Bayes for text and categorical data
- •Support vector machines and kernels
- •Lab: Compare three classifiers on the same data
- •Outcome: Choose algorithms by data shape and need
Decision Trees & Random Forests
- •Decision trees, entropy, Gini and pruning
- •Bagging, random forests and out-of-bag error
- •Feature importance and partial dependence
- •Lab: Tune a random forest and interpret its importance
- •Outcome: Use tree-based models effectively
Gradient Boosting
- •Boosting intuition and the main hyperparameters
- •XGBoost, LightGBM and CatBoost on tabular data
- •Early stopping and regularisation
- •Lab: Build a boosted model and compare it with a forest
- •Outcome: Build strong tabular-data models
Feature Engineering & Pipelines
- •Missing values, categorical encoding and scaling
- •Feature creation, selection and dimensionality reduction
- •scikit-learn pipelines and column transformers
- •Lab: Build a leak-free preprocessing pipeline
- •Outcome: Make preprocessing repeatable and safe
Hyperparameter Tuning & Imbalanced Data
- •Grid, random and Bayesian search with Optuna
- •Class imbalance: weights, resampling, SMOTE and threshold tuning
- •Cost-sensitive evaluation
- •Lab: Improve recall for a rare-event dataset
- •Outcome: Tune models and handle skewed classes
Unsupervised Learning
- •K-means, hierarchical clustering and DBSCAN
- •PCA, t-SNE and UMAP for reduction and visualisation
- •Anomaly detection with isolation forest
- •Lab: Segment customers and detect anomalies
- •Outcome: Discover structure in unlabelled data
Time Series Forecasting & Recommenders
- •Time series components, stationarity and baselines
- •ARIMA, Prophet and machine learning forecasting with lag features
- •Recommender basics: content-based and collaborative filtering
- •Lab: Forecast demand and build a simple recommender
- •Outcome: Apply ML to time-dependent and user-item data
Explainability, Deployment & Capstone
- •Explaining models with SHAP and permutation importance; fairness checks
- •Serving models with FastAPI and Docker; tracking with MLflow
- •Capstone: end-to-end ML project with a written report and deployed API
- •Interview preparation
- •Outcome: A deployed, explained model and a portfolio write-up
Tools Covered
Projects Included
- Sales Forecasting: Build lag-based and ARIMA or Prophet forecasts and compare them; deliverable is a notebook with error analysis.
- Customer Churn Model: Build and tune a boosted model and choose a threshold from a cost matrix; deliverable is a model, report and SHAP explanation.
- Credit Risk Scoring with Imbalanced Data: Handle class imbalance and compare resampling strategies; deliverable is a notebook and metric comparison.
- Customer Segmentation and Anomalies: Cluster customers and flag unusual transactions; deliverable is a profile report and anomaly list.
- Capstone Deployed Model: Package a model as a FastAPI service in Docker with tracking; deliverable is a GitHub repository and demo.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Machine Learning course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Machine Learning.
Certificate of Completion
Machine Learning
This certifies that
[Your Name]
has successfully completed the
Machine Learning
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Machine Learning course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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How long is the Machine Learning course at GloryTecks, and how many hours a week?
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Best Machine Learning Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Machine Learning Engineer (entry level), Data Scientist (entry level), Data Analyst, Applied ML Developer; support is guidance with the job search, not a guarantee of a job.