Advanced Machine Learning & Deep Learning Course – Build AI Models Like the Experts

Advanced (ML & DL) Course in Telugu cutting-edge AI techniques. It covers neural networks, optimization, and real-world applications.

What Actually IS Artificial Intelligence & Machine Learning ?

Machine Learning (ML)

Instead of manually programming every single rule, Machine Learning models learn patterns from historical data to make predictions automatically (like predicting stock market trends or customer behavior).

Artificial Intelligence (AI)

The technology that enables machines to simulate human intelligence. It powers ChatGPT, self-driving cars, and fraud detection systems.

The Next Level: Deep Learning, NLP & Computer Vision

We don’t just teach basic data analysis; we take you into the absolute cutting-edge of Artificial Intelligence.

Deep Learning & Neural Networks

This is where true AI happens. Deep Learning uses Artificial Neural Networks (inspired by the human brain) to solve highly complex problems, processing massive amounts of text, image, and video data.

Natural Language Processing (NLP)

Teaching computers to understand, interpret, and generate human language. This is the exact technology powering ChatGPT, Google Translate, and automated resume screening systems.

Computer Vision

Giving machines "eyes." You will learn how Deep Learning models analyze and understand images and videos, enabling technologies like facial recognition, medical imaging analysis, and autonomous vehicles.

Generative AI

We introduce you to Large Language Models (LLMs) and Prompt Engineering, ensuring you understand the modern workflows that are revolutionizing content creation and coding.

The Arsenal: Enterprise AI Tools You Will Master

We take you from absolute beginner to deploying production-ready AI models using the exact tech stack dominating the industry.

Python Programming

The undisputed language of AI. We start from absolute zero, teaching you how to process and manipulate massive datasets effortlessly.

TensorFlow

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.

Keras

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.

Scikit-Learn & Math Logic

Master regression, classification, clustering, and feature engineering to build rock-solid traditional Machine Learning models.

The ROI of ML & DL Mastery: Industry Salaries in India

Fresher Data analytics Salaries

Job Role

Experience

Average Salary

ML Engineer

Fresher

₹4.5 – ₹6 LPA

Data Scientist

Fresher

₹3 – ₹6 LPA

AI Engineer

Fresher

₹4.5 – ₹7.5 LPA

NLP / Computer Vision Engineer

Fresher

₹4.5 – ₹15 LPA

Deep Learning Specialist

Fresher

₹5 – ₹8 LPA

Experienced Data analytics Salaries

Job Role

Experience

Average Salary

ML Engineer

3–5 Years

₹9 – ₹15 LPA

Data Scientist

3–5 Years

₹10 – ₹18 LPA

AI Engineer

4–6 Years

₹12 – ₹20 LPA

NLP / Computer Vision Engineer

5–8 Years

₹15 – ₹40 LPA

Deep Learning Specialist

5–8 Years

₹22 – ₹50 LPA

The Companies That Want to Hire You

What Makes This Course Different

👉 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

Machine Learning & Deep Learning Course Syllabus

1. Data Libraries

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

2. Mathematics

Linear Algebra
Statistics
Probability
Differential Calculus

3. Machine Learning

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

4. Classification Models

Binary Classifier
Performance Measures (Accuracy, Confusion Matrix, Precision, Recall, ROC Curve)
Multi-Class Classification
Error Analysis
Multi-Label & Multi-Output Classification

5. Regression Models

Linear Regression
Gradient Descent (Batch, Stochastic, Mini-Batch)
Polynomial Regression
Regularized Linear Models (Ridge, Lasso, Early Stopping)
Logistic Regression

6. Support Vector Machines

Linear SVM Classification
Soft Margin vs Hard Margin
Non-Linear SVM Classification
SVM Regression Models

7. Decision Trees

Introduction & Training Decision Trees
Visualizing Trees
Estimating Class Probabilities
CART Training Algorithm
Computational Complexity
Gini Impurity vs Entropy
Hyperparameter Regularization
Instability & Regression Trees

8. ASP.NET

Ensemble Learning
Voting Classifiers
Bagging & Pasting
Out-of-Bag Evaluation
Random Patches & Subspaces
Random Forests & Extra Trees
Feature Importance
Boosting (AdaBoost, Gradient Boost, Stacking)

9. Unsupervised Learning Techniques

Clustering
K-Means Algorithm & Limitations
Preprocessing with Clustering
Semi-Supervised Learning via Clustering
DBSCAN

10. Artificial Neural Networks with Keras

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)

11. Deep Learning – Training Neural Networks

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)

12. Loading & Preprocessing Data with TensorFlow

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

13. Generative AI Overview

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

14. Capstone Project

End-to-End ML/DL Project with Real Dataset
Applying ML & DL Models to Real-World Problem
Model Optimization & Deployment
Documentation & Case Study Presentation

Real Projects You'll Build with Machine Learning

Sales Data Insights

Analyze sales data using preprocessing and visualization to uncover trends, patterns, metrics, and provide actionable business recommendations effectively.

AI-Based Resume Screener

Create an Machine learning tool to help HR teams automate resume screening using NLP techniques.

Melbourne Housing Snapshot

Investigate Melbourne housing market using machine learning models, predicting house prices with features like location, size, amenities, and trends.

Obesity Challenge

Predict obesity levels using health and lifestyle data with preprocessing, feature engineering, ML models, evaluating accuracy, and generating public health insights.

Financial Risk for Loan Approval

Develop a machine learning model predicting loan approvals using financial, credit, demographic data with classification, risk assessment, and insights.

FLM Bank Application

Create FLM Bank smart app using machine learning for predictive analytics, loan recommendations, retention, personalized offers, data preprocessing, and deployment.

Bank Statement Aggregator (BSA)

Design NLP-based system to process bank statements, analyze transactions, visualize spending habits, provide insights, and build scalable financial management solution.

Watch the Demo of Our Advanced Machine Learning Course in Telugu to Get a Preview of the Topics Covered.

Success Stories

From Non-Tech to Data Scientist at TCS - ₹12 LPA

Priya

Hyderabad

Got ML Engineer role at Infosys - ₹15 LPA

Rajesh

Vizag

Started AI consulting business - ₹50K/month

Meera

Vijayawada

From Non-Tech to Data Scientist at TCS - ₹12 LPA

Priya

Khammam

Complete Machine & Deep Learning Mastery in Telugu

₨59999

₨ 14,999

low Price

Beyond Courses.
We Build Careers.

Every program includes industry-focused add-on sessions designed to help students crack jobs, freelance, switch careers, and grow professionally.

Career Acceleration Ecosystem

Everything you need beyond coding to become job-ready.

Resume Building

Session 01

LinkedIn Optimization

Session 02

Freelancing

Session 03

Career Switch Guidance

Session 04

Job Search Strategy

Session 05

Financial Awareness

Session 06

MAANG Preparation

Session 07

Agile & Scrum Workflow

Session 08

Git & GitHub

Session 09

HR + Salary Negotiation

Session 10

Communication Bootcamp

Session 11

Career Advancement Results

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.

Happy Students

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Batches Conducted

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Experience

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Satisfaction

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Frequently Asked Questions

1. Do I need a strong background in Mathematics to learn Machine Learning?

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.

2. I am from a non-IT background (B.Com, BBA, B.Sc). Is this a good career path for me?

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.

3. Are the live training classes conducted in Telugu?

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.

4. What is the difference between Machine Learning and Deep Learning?

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.

5. What are TensorFlow and Keras?

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.

6. Will I actually build real AI models, or is this just theory?

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.

7. What is the starting salary for an AI or Machine Learning Engineer in India?

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+.

8. Can I work internationally or remotely after learning AI?

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.

9. Do you provide placement assistance?

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.

10. Will I receive a certificate after completing the course?

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.

Get Certified

Yes, you’ll get a Certificate representing your Industry Readiness once you submit your projects and clear the pre placement test.

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