Advanced (ML & DL) Course in Telugu cutting-edge AI techniques. It covers neural networks, optimization, and real-world applications.
Training In Telugu
Recordings
Addon Sessions
Projects
Advanced Machine Learning & Deep Learning focuses on how computers learn from data, identify patterns, make predictions, and solve complex problems using intelligent algorithms and neural networks.
Learn how machines identify patterns from data and make predictions using supervised and unsupervised learning. Work with regression, classification, clustering, feature engineering, and model evaluation.
Understand how Artificial Neural Networks learn complex patterns from large datasets. Build deep learning models using CNNs, RNNs, and other neural network architectures for advanced AI applications.
The technology that enables machines to simulate human intelligence. It powers ChatGPT, self-driving cars, and fraud detection systems.
Learn how machines understand and process human language. Work with text classification, sentiment analysis, language models, chatbots, and other NLP-based applications.
AI is becoming part of products, automation, analytics, healthcare, finance, e-commerce, and intelligent applications. Advanced Machine Learning and Deep Learning give you the skills to build systems that can predict, classify, understand images, process language, and learn from complex data.
Companies are using Machine Learning to improve predictions, recommendations, fraud detection, forecasting, personalization, and business decisions. Professionals who can build and evaluate ML models are becoming valuable across many industries.
Deep Learning is behind technologies such as image recognition, speech processing, NLP, intelligent assistants, and advanced recommendation systems. Learning neural networks helps you understand how modern AI applications actually work.
Generative AI is built on concepts from Machine Learning, Deep Learning, NLP, and neural networks. Understanding these fundamentals gives you a stronger base to move into LLMs, AI agents, Computer Vision, and other advanced AI technologies.
Advanced ML and DL skills can prepare you for roles such as Machine Learning Engineer, AI Engineer, Data Scientist, Deep Learning Engineer, NLP Engineer, and Computer Vision Engineer. These skills also help you work on real-world AI projects and research-driven applications.
We take you from absolute zero to deploying multi-cloud architectures using the exact tools Fortune 500 companies use.
The undisputed language of AI. We start from absolute zero, teaching you how to process and manipulate massive datasets effortlessly.
Master the world’s most popular Deep Learning framework developed by Google. Learn to build neural networks, train AI models, and optimize deep learning workflows.
The ultimate Deep Learning API. Built on top of TensorFlow, Keras dramatically simplifies neural network development, allowing you to build and train complex predictive systems with minimal coding.
Master regression, classification, clustering, and feature engineering to build rock-solid traditional Machine Learning models.
Learn to build and train deep learning models using PyTorch. Work with tensors, neural networks, model training, and experimentation for real-world AI applications.
Learn how to process images and build computer vision applications using OpenCV. Explore image preprocessing, object detection, face recognition, and visual AI workflows.
Introduction to NumPy
Creating Arrays, Indexing Arrays, Array Transposition
Universal Array Functions, Array Processing
Array Input/Output
Introduction to Pandas (Series, DataFrames)
Data Reading, Cleaning, Wrangling, Selection, Extraction with Pandas
Introduction to Matplotlib & Data Visualization
Linear Algebra
Statistics
Probability
Differential Calculus
Introduction to Machine Learning
What is ML, Why ML?
Examples of ML Applications
Types of ML Systems (Supervised, Unsupervised, Batch vs Online, Instance vs Model Based)
Challenges of Machine Learning
Overfitting vs Underfitting
Phases of End-to-End ML Project
Binary Classifier
Performance Measures (Accuracy, Confusion Matrix, Precision, Recall, ROC Curve)
Multi-Class Classification
Error Analysis
Multi-Label & Multi-Output Classification
Linear Regression
Gradient Descent (Batch, Stochastic, Mini-Batch)
Polynomial Regression
Regularized Linear Models (Ridge, Lasso, Early Stopping)
Logistic Regression
Linear SVM Classification
Soft Margin vs Hard Margin
Non-Linear SVM Classification
SVM Regression Models
Introduction & Training Decision Trees
Visualizing Trees
Estimating Class Probabilities
CART Training Algorithm
Computational Complexity
Gini Impurity vs Entropy
Hyperparameter Regularization
Instability & Regression Trees
Ensemble Learning
Voting Classifiers
Bagging & Pasting
Out-of-Bag Evaluation
Random Patches & Subspaces
Random Forests & Extra Trees
Feature Importance
Boosting (AdaBoost, Gradient Boost, Stacking)
Clustering
K-Means Algorithm & Limitations
Preprocessing with Clustering
Semi-Supervised Learning via Clustering
DBSCAN
Biological Neuron & Perceptron
Multilayer Perceptron & Backpropagation
Regression & Classification MLPs
Implementing MLPs with Keras
Image Classifier with Sequential API
Regression MLP with Sequential API
Complex Models with Functional API
Saving/Restoring Models, Callbacks
Hyperparameter Tuning (Hidden Layers, Neurons, Batch Size, Learning Rate)
Vanishing/Exploding Gradients
Glorot & He Initialization
Non-Saturating Activation Functions
Batch Normalization
Gradient Clipping
Transfer Learning, Pretrained Layers
Unsupervised Pretraining
Faster Optimizers
Avoiding Overfitting (L1, L2, Dropout, Max-Norm)
Quick Tour of TensorFlow
Tensors & Operations vs NumPy
Data API & Chaining Transformations
Shuffling, Prefetching, Preprocessing Input Data
Encoding Features (One-Hot, Embeddings)
Keras Preprocessing Layers
TF Transform & TFDS Project
Introduction to Generative AI
Architecture of Generative AI
Introduction to LLMs (Large Language Models)
Foundational / Public LLMs
Components of LLMs (Character Splitters, Embeddings, Vector Stores)
Role of RAGs (Retrieval Augmented Generation)
Prompt Engineering Basics
End-to-End ML/DL Project with Real Dataset
Applying ML & DL Models to Real-World Problem
Model Optimization & Deployment
Documentation & Case Study Presentation
We don’t just teach theory. You will build a production-grade GitHub portfolio to prove your skills to recruiters.
Analyze sales data to identify revenue trends, customer behavior, product performance, and business patterns. Use preprocessing, visualization, and predictive techniques to generate useful business insights.
Python · Pandas · NumPy · Matplotlib · Scikit-learn · Data Visualization
Analyze Melbourne housing data and build a machine learning model to predict property prices using location, property size, rooms, amenities, and market-related features.
Python · Pandas · NumPy · Scikit-learn · Regression Models · Matplotlib
Build a machine learning model to predict obesity levels using health, lifestyle, and demographic data. Apply preprocessing, feature engineering, classification, and model evaluation techniques.
Python · Pandas · NumPy · Scikit-learn · Random Forest · SVM · KNN · ANN
Develop a predictive model to analyze customer financial data and estimate loan approval probability and risk level. Use classification models to support smarter lending decisions.
Python · Pandas · NumPy · Data Visualization
👉 Module Wise Quizzes, Assignments, and Practice Questions
👉 100% Placement assistance
👉 Industry professionals with 10+ years of experience
👉 Live Chat support assistance
👉 Access to session recordings for 2 years
👉 EMI Option also available with specified cards
👉 Get ₹10,000 Worth of Free Career Booster Add-On Sessions
👉 Dedicated Job Updates
👉 100% Live Training in Telugu
👉 Industry Ready Training
👉 Interview Questions & Study Materials
👉 Resume & Linked In Preparation
Every program includes industry-focused add-on sessions designed to help students crack jobs, freelance, switch careers, and grow professionally.
Everything you need beyond coding to become job-ready.
Session 01
Session 02
Session 03
Session 04
Session 05
Session 06
Session 07
Session 08
Session 09
Session 10
Session 11
Rs. 59,999/-
Rs. 14,999/-
Rs. 45,000/-
DevOps professionals are among the highest-paid IT workers in the world. Here is what the market is currently paying:
Job Role
Experience
Average Salary
ML Engineer
Fresher
₹4.0 – ₹6.0 LPA
Data Scientist
Fresher
₹3.0 – ₹6.0 LPA
AI Engineer
Fresher
₹4.0 – ₹7.0 LPA
NLP / Computer Vision Engineer
Fresher
₹4.0 – ₹9.0 LPA
Deep Learning Specialist
Fresher
₹5.0 – ₹8.0 LPA
Job Role
Experience
Average Salary
ML Engineer
3–5 Years
₹09.0 – ₹15.0 LPA
Data Scientist
3–5 Years
₹10.0 – ₹18.0 LPA
AI Engineer
4–6 Years
₹12.0 – ₹20.0 LPA
NLP / Computer Vision Engineer
5–8 Years
₹15.0 – ₹40.0 LPA
Deep Learning Specialist
5–8 Years
₹22.0 – ₹50.0 LPA
At Frontlines Edutech, we believe in transforming careers through high-quality, industry-relevant education. Our focus is on providing practical skills and knowledge that lead to real-world success. With a proven track record of impactful learning, we ensure our students are well-equipped to excel in their chosen fields and achieve their career goals with confidence.
No! While Machine Learning involves math, modern Python frameworks (like TensorFlow, Keras, and Scikit-Learn) do the complex mathematical calculations for you. We teach you the logic behind the algorithms and how to apply them. You do not need a Ph.D. to become a highly paid AI Engineer.
Absolutely. AI is about solving business problems using data. Students with non-IT backgrounds often excel because they understand the business logic. We will teach you the Python programming from absolute zero.
Yes! All sessions, practical model training, and doubt-clearing classes are taught in Telugu. We break down complex Machine Learning algorithms and Deep Learning networks in your regional language to ensure absolute conceptual clarity.
Machine Learning uses statistical algorithms to find patterns in structured data (like Excel sheets). Deep Learning is a more advanced subset of ML that uses “Neural Networks” (mimicking the human brain) to process highly unstructured data, like recognizing faces in images (Computer Vision) or understanding human speech (NLP). We teach you both.
TensorFlow is an incredibly powerful, open-source library created by Google used to build Deep Learning models. Keras is an interface that sits on top of TensorFlow, making it much easier and faster for developers to code and train Neural Networks without writing hundreds of lines of complex code.
This course is entirely project-driven. You will process real-world datasets and build live AI models—including an AI Resume Screener, Real Estate Price Predictors, and Banking Risk Analyzers—to showcase directly to hiring managers on your GitHub.
Because AI directly drives massive corporate revenue and efficiency, professionals are paid a premium. Freshers can expect starting salaries between ₹6 LPA and ₹12 LPA. With Deep Learning expertise and experience, salaries rapidly scale to ₹25 LPA to ₹50 LPA+.
Yes. AI and Deep Learning are universally demanded skills. Companies worldwide are desperate for talent. You can work remotely, take on lucrative international consulting contracts, or participate in global predictive modeling competitions.
Yes. We provide comprehensive placement support. This includes aggressive GitHub portfolio reviews, building an ATS-friendly AI Engineer resume, conducting live technical mock interviews, and providing direct job updates for top tech companies.
Yes. Upon successfully completing the training and deploying your real-time Machine Learning projects, you will receive an industry-recognized certificate from Frontlines EduTech to massively boost your LinkedIn profile and resume credibility.
Yes, you’ll get a Certificate representing your Industry Readiness once you submit your projects and clear the pre placement test.
