Machine Learning & Deep Learning: Complete 90-Day Career Transformation Roadmap
Starting your journey into machine learning and deep learning might feel overwhelming with so many topics, tools, and technologies to master. The good news? With the right roadmap and dedicated effort over the next 90 days, you can transform from a complete beginner into a job-ready ML professional. This comprehensive guide breaks down exactly what you need to learn each day, ensuring you build strong fundamentals while working on real-world projects that impress recruiters.
The machine learning engineering job market is experiencing explosive growth, projected to reach $113.10 billion in 2025 and expand to $503.40 billion by 2030. In India specifically, AI/ML roles have grown by 36% entering 2025, with cities like Bangalore, Hyderabad, Chennai, and Pune leading the hiring wave. Machine learning engineers in India earn between ₹7-25 lakhs per annum, while deep learning specialists command even higher salaries averaging ₹29.7 lakhs annually. This roadmap prepares you to capture these lucrative opportunities.
Month 1: Building Your Foundation (Days 1-30)
Week 1-2: Python Programming & Mathematics Foundation
Days 1-3: Python Essentials
Day 1: Getting Started with Python
Begin with Python installation, understanding IDEs like Jupyter Notebook and PyCharm, and writing your first programs. Learn variables, data types (integers, floats, strings, booleans), and basic input/output operations. Practice writing simple programs that take user input and display results.
Day 2: Control Flow & Functions
Master conditional statements (if-else, elif), loops (for, while), and loop control (break, continue). Understand how to define functions, pass parameters, return values, and work with default arguments. Write programs using functions to solve real problems like calculators and pattern printing.
Day 3: Data Structures Fundamentals
Explore Python’s built-in data structures including lists, tuples, dictionaries, and sets. Learn indexing, slicing, list comprehensions, and dictionary operations. Practice manipulating these structures as they form the backbone of data processing.
Days 4-7: Mathematics for Machine Learning
Day 4: Linear Algebra Basics
Understand vectors, matrices, matrix operations (addition, multiplication, transpose), and their importance in ML algorithms. Learn about eigenvalues and eigenvectors conceptually. Work through practical examples showing how images and datasets are represented as matrices.
Day 5: Statistics Foundation
Study measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation), and distributions (normal, binomial). Understand percentiles, quartiles, and outliers. These concepts are critical for data analysis and understanding algorithm behavior.
Day 6: Probability Concepts
Learn probability basics, conditional probability, Bayes’ theorem, and probability distributions. Understand how probability underpins classification algorithms and prediction confidence scores.
Day 7: Differential Calculus
Grasp derivatives, partial derivatives, gradients, and the chain rule. These concepts power the optimization algorithms that train neural networks. Focus on understanding how gradients indicate the direction to minimize loss functions.
Days 8-14: NumPy & Data Manipulation
Day 8-9: NumPy Foundations
Master NumPy array creation, array attributes, and array indexing. Learn reshaping, transposing, and concatenating arrays. Understand broadcasting rules that make operations between different-shaped arrays possible.
Day 10: Universal Array Functions
Work with mathematical functions (add, subtract, multiply, divide), trigonometric functions, exponential and logarithmic operations. Practice applying functions element-wise across arrays efficiently.
Day 11-12: Array Processing
Learn array sorting, searching, filtering with boolean indexing, and statistical operations (sum, mean, std). Master random number generation for simulation and data splitting. Practice reshaping multi-dimensional arrays for different ML model inputs.
Day 13-14: NumPy Projects
Build practical projects like image manipulation using NumPy arrays, creating synthetic datasets, and implementing mathematical operations for ML algorithms from scratch. Complete array input/output operations including saving and loading data.
Week 3-4: Pandas & Data Analysis
Days 15-17: Pandas Basics
Day 15: Series & DataFrames
Understand Pandas Series and DataFrame structures. Learn to create DataFrames from dictionaries, lists, and NumPy arrays. Master basic operations like viewing data (head, tail, info, describe) and accessing columns.
Day 16: Data Reading
Practice reading data from CSV, Excel, JSON, and SQL databases using Pandas. Learn to handle different file encodings, delimiters, and missing value indicators during data import.
Day 17: Data Selection & Extraction
Master loc and iloc for label-based and integer-based indexing. Learn boolean indexing, filtering rows based on conditions, and selecting specific columns. Practice chaining operations for complex data extraction.
Days 18-21: Data Cleaning & Wrangling
Day 18: Handling Missing Data
Learn to identify missing values using isnull() and notnull(). Master strategies for handling missing data including dropping rows/columns, forward fill, backward fill, and imputation with mean/median/mode.
Day 19: Data Cleaning Techniques
Remove duplicates, handle incorrect data types, standardize text data (lowercase, strip whitespace), and fix inconsistent category names. Practice dealing with outliers using statistical methods.
Day 20-21: Data Wrangling & Transformation
Master groupby operations for aggregation, pivoting and melting DataFrames, merging and joining datasets, and concatenating DataFrames. Learn to apply custom functions using apply() and map(). Work on reshaping data for analysis.
Days 22-28: Data Visualization with Matplotlib
Day 22-23: Matplotlib Fundamentals
Understand the pyplot interface, figure and axes concepts, and basic plot types (line plots, scatter plots, bar charts). Learn to customize plots with labels, titles, legends, and grid lines.
Day 24: Advanced Visualizations
Create histograms for distribution analysis, box plots for outlier detection, heatmaps for correlation matrices, and subplots for multiple visualizations. Master color maps and styling.
Day 25-26: Statistical Visualization
Build visualizations for exploratory data analysis including distribution plots, correlation matrices, pair plots, and time series visualizations. Practice telling stories with data through effective visualization choices.
Day 27-28: Mini-Project – EDA Dashboard
Complete a comprehensive exploratory data analysis project on a real dataset. Create a complete analysis notebook with data loading, cleaning, statistical analysis, and multiple visualizations answering business questions.
Week 5-6: Introduction to Machine Learning
Days 29-31: ML Fundamentals
Day 29: What is Machine Learning
Understand the definition of machine learning, differences from traditional programming, and real-world applications. Learn the ML workflow from data collection to model deployment.
Day 30: Types of Machine Learning
Study supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Understand when to apply each type based on problem characteristics and available data.
Month 2: Intermediate Mastery with ML Fundamentals (Days 31-60)
Days 29-31: ML Fundamentals
Day 29: What is Machine Learning
Understand the definition of machine learning, differences from traditional programming, and real-world applications. Learn the ML workflow from data collection to model deployment.
Day 30: Types of Machine Learning
Study supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Understand when to apply each type based on problem characteristics and available data.
Day 31: ML System Types
Learn batch learning versus online learning, instance-based versus model-based learning. Understand the tradeoffs between different learning approaches and computational requirements.
Days 32-35: End-to-End ML Project Workflow
Day 32: Problem Definition & Data Collection
Practice defining clear ML objectives, identifying target variables, gathering relevant features, and assessing data quality. Understand how to frame business problems as ML tasks.
Day 33: Data Preparation
Master train-test splitting, cross-validation concepts, feature scaling (normalization and standardization), and encoding categorical variables. Learn why proper data preparation prevents model overfitting.
Day 34: Model Training & Evaluation
Understand the training process, loss functions, and evaluation metrics. Learn to interpret performance metrics for different problem types and recognize overfitting versus underfitting.
Day 35: Project – House Price Prediction
Build your first complete ML project predicting house prices using linear regression. Go through data exploration, cleaning, feature engineering, model training, evaluation, and interpretation of results.
Week 7-8: Classification & Regression Models
Days 36-42: Classification Algorithms
Day 36-37: Binary Classification
Learn logistic regression for binary classification, understand the sigmoid function, and decision boundaries. Study performance measures including accuracy, precision, recall, F1-score, and when to use each metric.
Day 38: Confusion Matrix & ROC Curve
Master the confusion matrix for understanding true positives, false positives, true negatives, and false negatives. Learn to plot and interpret ROC curves and calculate AUC scores for model comparison.
Day 39-40: Multi-Class Classification
Extend binary classification to multi-class problems using one-vs-rest and one-vs-one strategies. Implement multi-class logistic regression and understand softmax activation. Practice error analysis to improve model performance.
Day 41: Multi-Label & Multi-Output Classification
Learn scenarios requiring multi-label classification where each instance can belong to multiple classes. Understand multi-output classification for predicting multiple target variables simultaneously.
Day 42: Project – Email Spam Classifier
Build a spam detection system using classification algorithms. Practice text preprocessing, feature extraction using TF-IDF, model training, hyperparameter tuning, and evaluation with appropriate metrics.
Days 43-49: Regression Models
Day 43-44: Linear Regression
Understand simple and multiple linear regression, the ordinary least squares method, coefficients interpretation, and R-squared evaluation. Learn assumptions of linear regression and how to validate them.
Day 45: Gradient Descent Optimization
Study the gradient descent algorithm, learning rate selection, and convergence criteria. Understand batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent tradeoffs.
Day 46-47: Polynomial Regression & Regularization
Learn polynomial regression for capturing non-linear relationships. Master regularization techniques including Ridge regression (L2), Lasso regression (L1), and early stopping to prevent overfitting.
Day 48-49: Project – Sales Forecasting
Develop a sales prediction model using regression techniques. Practice feature engineering from date-time variables, handling seasonality, model comparison, and creating prediction intervals for business planning.
Week 9-10: Support Vector Machines & Decision Trees
Days 50-53: Support Vector Machines
Day 50: Linear SVM Classification
Understand how SVMs find the optimal decision boundary by maximizing the margin between classes. Learn about support vectors (the critical data points closest to the decision boundary) and how they define the hyperplane.
Day 51: Hard Margin vs Soft Margin
Study hard margin classification for perfectly separable data and soft margin classification for handling outliers. Understand the C hyperparameter that controls the tradeoff between wide margin and classification errors.
Day 52: Nonlinear SVM with Kernel Trick
Master the kernel trick that transforms data into higher dimensions for non-linear classification. Practice with polynomial kernels, Radial Basis Function (RBF) kernels, and sigmoid kernels for different data patterns.
Day 53: SVM Regression
Learn how SVMs can be adapted for regression tasks (SVR) by fitting as many instances as possible within a margin while limiting violations. Practice tuning epsilon and C hyperparameters for optimal regression performance.
Days 54-60: Decision Trees & Random Forests
Day 54-55: Decision Tree Fundamentals
Understand how decision trees make predictions through a series of yes/no questions about features. Learn the CART (Classification and Regression Trees) algorithm, visualize decision trees, and interpret decision rules.
Day 56: Gini Impurity vs Entropy
Study impurity measures used to determine the best splits. Compare Gini impurity (computational efficiency) with information gain using entropy (theoretical purity). Understand computational complexity of tree training and prediction.
Day 57: Tree Regularization
Learn to control overfitting through hyperparameters like max_depth, min_samples_split, min_samples_leaf, and max_features. Practice pruning techniques to simplify trees while maintaining performance.
Day 58-59: Ensemble Learning – Random Forests
Master ensemble methods including voting classifiers, bagging and pasting. Understand Random Forests as an ensemble of decision trees trained on random subsets of features and data. Learn about out-of-bag evaluation and feature importance ranking.
Day 60: Boosting Algorithms
Study AdaBoost (Adaptive Boosting) that focuses on misclassified instances and Gradient Boosting that builds trees sequentially to correct errors. Understand stacking ensembles that combine different model types for superior predictions.
Month 3: Advanced Mastery and Career Preparation (Days 61-90)
Week 11: Unsupervised Learning & Clustering
Days 61-67: Clustering Techniques
Day 61-62: K-Means Clustering
Learn the K-Means algorithm for grouping similar data points. Understand centroid initialization, cluster assignment, and iterative optimization. Practice the elbow method and silhouette analysis for determining optimal cluster numbers.
Day 63: Limitations of K-Means
Recognize K-Means limitations including sensitivity to initialization, difficulty with non-spherical clusters, and the need to specify K beforehand. Study scenarios where K-Means performs poorly.
Day 64: DBSCAN Clustering
Master Density-Based Spatial Clustering (DBSCAN) that identifies clusters of arbitrary shapes and automatically detects outliers. Understand core points, border points, and noise points based on density thresholds.
Day 65-66: Clustering Applications
Apply clustering for customer segmentation, image compression, anomaly detection, and data preprocessing. Practice semi-supervised learning where clustering provides pseudo-labels for unlabeled data.
Day 67: Project – Customer Segmentation
Build a complete customer segmentation analysis using RFM (Recency, Frequency, Monetary) features. Apply multiple clustering algorithms, compare results, visualize segments, and generate actionable business insights.
Week 12: Deep Learning Foundations
Days 68-74: Artificial Neural Networks with Keras
Day 68: Biological Neurons to Artificial Neurons
Understand the biological inspiration behind neural networks. Learn about the perceptron as the simplest neural network unit, activation functions, and how neurons process inputs to generate outputs.
Day 69: Multi-Layer Perceptrons (MLPs)
Study MLPs with multiple hidden layers and the backpropagation algorithm that efficiently computes gradients for weight updates. Understand forward propagation for predictions and backward propagation for learning.
Day 70: Building Neural Networks with Keras
Get hands-on with Keras Sequential API for building feedforward neural networks. Learn to stack layers, compile models with loss functions and optimizers, and train networks on datasets.
Day 71: Regression and Classification MLPs
Build regression MLPs for continuous value prediction using MSE loss and linear output activation. Create classification MLPs with softmax activation and categorical crossentropy loss for multi-class problems.
Day 72: Functional API for Complex Architectures
Master the Keras Functional API for building non-sequential models including networks with multiple inputs, multiple outputs, shared layers, and skip connections. Practice building complex architectures beyond simple sequential stacks.
Day 73: Callbacks and Model Management
Learn to save and restore models, implement early stopping to prevent overfitting, use learning rate scheduling, and create custom callbacks for monitoring training. Practice model checkpointing to save best versions during training.
Day 74: Hyperparameter Tuning for Neural Networks
Understand critical hyperparameters including number of hidden layers, neurons per layer, learning rate, batch size, and epochs. Practice systematic tuning using techniques like grid search and random search to optimize network performance.
Week 13: Advanced Deep Learning
Days 75-80: Training Deep Neural Networks
Day 75: Vanishing and Exploding Gradients
Learn about gradient flow problems in deep networks where gradients become extremely small (vanishing) or large (exploding) during backpropagation. Understand how these issues prevent effective training.
Day 76: Weight Initialization Strategies
Master Glorot (Xavier) initialization for sigmoid/tanh activations and He initialization for ReLU activations. Understand how proper initialization prevents gradient problems and accelerates convergence.
Day 77: Activation Functions and Batch Normalization
Study nonsaturating activation functions like ReLU, Leaky ReLU, ELU, and GELU that mitigate vanishing gradients. Learn batch normalization that normalizes layer inputs during training for faster and more stable learning.
Day 78: Advanced Optimizers
Move beyond standard gradient descent to faster optimizers including Momentum (accumulates gradients for smoother updates), RMSprop (adapts learning rates per parameter), and Adam (combines momentum with adaptive learning rates).
Day 79: Regularization Techniques
Master L1 and L2 regularization that penalize large weights. Understand dropout (randomly deactivating neurons during training) and max-norm regularization. Learn when and how to apply each technique to prevent overfitting.
Day 80: Transfer Learning
Learn to leverage pretrained models by reusing lower layers that detect general features and retraining upper layers for specific tasks. Understand when transfer learning dramatically reduces training time and data requirements.
Days 81-84: TensorFlow Data Pipeline
Day 81: TensorFlow Fundamentals
Explore TensorFlow tensors, operations, and computational graphs. Understand eager execution for immediate operation evaluation versus graph mode for optimized production deployment.
Day 82-83: The Data API
Master tf.data.Dataset for building efficient input pipelines. Learn chaining transformations, shuffling data, batching, prefetching, and parallel data loading. Practice preprocessing large datasets that don’t fit in memory.
Day 84: Feature Preprocessing
Implement feature scaling, encoding categorical variables using one-hot encoding or embeddings, and text preprocessing. Learn Keras preprocessing layers that become part of the model for consistent training and inference preprocessing.
Days 85-87: Generative AI Overview
Day 85: Introduction to Generative AI
Understand generative models that create new content (text, images, code) versus discriminative models that classify existing data. Learn the architecture of generative AI systems and their applications across industries.
Day 86: Large Language Models (LLMs)
Study the transformer architecture powering modern LLMs. Understand foundational models like GPT, BERT, and LLaMA. Learn about attention mechanisms that enable models to focus on relevant input parts.
Day 87: RAG, Embeddings & Prompt Engineering
Master Retrieval-Augmented Generation (RAG) that combines LLMs with external knowledge bases. Learn text embeddings for semantic similarity and vector stores for efficient retrieval. Practice prompt engineering techniques for optimal LLM responses.
Week 13 (Bonus): Career Acceleration & Job Readiness
Days 88-90: Profile Optimization, Job Platforms & Interview Prep
Day 88: LinkedIn Profile Optimization
Your LinkedIn profile serves as your professional storefront. Start with a compelling headline highlighting “Machine Learning Engineer | Python | TensorFlow | Deep Learning” rather than generic titles. Write a summary showcasing your transformation journey, key projects, and technical expertise using storytelling that demonstrates passion .
Upload a professional photo, customize your LinkedIn URL, and add a background banner reflecting your ML specialization. Feature your GitHub portfolio prominently with project descriptions. Request recommendations from instructors or project collaborators. Join ML communities and engage with content from companies like Google AI, OpenAI, and Microsoft Research.
Creating Your GitHub Portfolio
Build 4-5 polished projects demonstrating diverse skills covering classification, regression, clustering, neural networks, and a capstone combining multiple techniques. Each repository needs a comprehensive README explaining the problem, approach, results with visualizations, and instructions for running code.
Write clean, well-commented code following PEP 8 standards. Include Jupyter notebooks with markdown explanations walking through your thought process. Deploy at least one project as a web application using Streamlit or Flask demonstrating end-to-end capabilities.
Day 89: Job Search Strategy & Platforms
Target companies hiring ML engineers across technology giants (Google, Microsoft, Amazon, Meta), AI startups (OpenAI, Anthropic, Stability AI), consulting firms (Accenture, Capgemini, TCS, Infosys, Wipro), and product companies (Flipkart, Swiggy, Zomato, PhonePe).
Top Job Platforms:
- Naukri.com & LinkedIn Jobs – Primary platforms for Indian market with thousands of ML positions
- AngelList – Startup-focused platform with equity options
- Kaggle Jobs – Connects data scientists with companies valuing competitive achievements
- GitHub Jobs – Tech-focused listings from companies seeking strong coders
- Company Career Pages – Direct applications to Google Careers, Microsoft Careers, Amazon Jobs for better visibility
Filter positions for “entry-level,” “0-2 years,” or “junior” ML roles. Don’t ignore “Data Analyst” or “AI Engineer” positions that often accept ML candidates. Apply to 10-15 positions daily with customized resumes highlighting relevant projects for each job description.
Resume Building for ML Roles
Structure your resume with clear sections: Contact Info, Professional Summary, Technical Skills, Projects, Education, and Certifications. Lead with a strong summary like “Machine Learning Engineer with expertise in Python, TensorFlow, and Scikit-learn. Built 5+ production-ready ML models including image classification CNNs and NLP sentiment analyzers”.
List technical skills categorically:
- Programming: Python, SQL, Git
- ML Libraries: Scikit-learn, TensorFlow, Keras, PyTorch, NumPy, Pandas
- Techniques: Supervised/Unsupervised Learning, Deep Learning, NLP, Computer Vision
- Tools: Jupyter, Docker, AWS/Azure, Streamlit
Describe projects with STAR format (Situation, Task, Action, Result). Example: “Developed customer churn prediction model achieving 89% accuracy using Random Forest and XGBoost, reducing churn by 15% in test scenarios”.
Day 90: Interview Preparation Mastery
ML interviews typically include four components: coding rounds, ML theory questions, practical case studies, and behavioral questions.
Coding Preparation:
Practice Python programming on LeetCode and HackerRank focusing on arrays, strings, dictionaries, and algorithmic thinking. Master implementing algorithms from scratch including linear regression, k-means clustering, and decision trees without libraries.
ML Theory Questions:
Prepare crisp explanations for concepts like bias-variance tradeoff, overfitting prevention, cross-validation importance, differences between bagging and boosting, when to use SVM versus Random Forest, activation function selection, and gradient descent variants.
Common Questions Include:
- Explain how a Random Forest works to a non-technical person
- How would you handle imbalanced datasets?
- Walk through building an end-to-end ML pipeline
- Explain regularization and why it prevents overfitting
- Difference between L1 and L2 regularization
- How does backpropagation work in neural networks?
- When would you choose precision over recall?
Case Study Preparation:
Practice solving real-world problems like “Design a recommendation system for an e-commerce platform” or “Build a fraud detection model for credit card transactions.” Structure answers discussing data collection, feature engineering, model selection, evaluation metrics, and deployment considerations.
Behavioral Questions:
Prepare STAR-format stories demonstrating teamwork, handling failure, learning new technologies quickly, and problem-solving under pressure. Examples: “Tell me about a time you debugged a poorly performing model” or “Describe a project where you had to learn a new framework”.
📚 Want a complete 200+ Machine Learning Interview Questions Guide? Frontlines Edutech offers comprehensive interview preparation resources covering technical questions, system design, case studies, and behavioral scenarios with model answers. Visit our Interview Preparation Hub for the complete ML Engineer Interview Guide.
4. Career Paths After Course Completion
Machine learning engineering represents one of the hottest career paths entering 2025 with demand far exceeding qualified candidates. India’s AI/ML job market grew 36% with Bangalore, Hyderabad, Chennai, Pune, Mumbai, Gurugaon, and Noida leading hiring.
Salary Potential:
- Entry-level ML Engineers: ₹6-10 lakhs per annum
- Mid-level ML Engineers: ₹12-20 lakhs per annum
- Senior ML Engineers: ₹25-45 lakhs per annum
- Deep Learning Specialists: ₹30-50 lakhs per annum
Key Hiring Sectors:
- Technology and software development companies
- Data science and analytics firms
- AI/ML specialized startups
- Media and entertainment platforms
- Financial services and fintech
- Healthcare and medical imaging
- E-commerce and retail
- Autonomous vehicles and robotics
Top Companies Hiring:
Infosys, TCS, Accenture, Wipro, Cognizant, Capgemini, IBM, Google, Microsoft, Amazon, Meta, Flipkart, Swiggy, Zomato, Paytm, and hundreds of AI-focused startups.
The skills you master in this 90-day program position you perfectly for these opportunities. The combination of strong fundamentals, hands-on project experience, and interview preparation gives you everything needed to confidently enter the job market and launch a lucrative ML career.
Your Journey Starts Now
This 90-day roadmap transforms you from wherever you are today into a job-ready machine learning engineer equipped with in-demand skills, a portfolio of impressive projects, and the confidence to ace interviews. Whether you follow this independently or accelerate your journey with Frontlines Edutech’s structured program, the key is starting today.
Machine learning isn’t just about algorithms and code – it’s about solving real problems, creating intelligent systems, and shaping the future of technology. The companies hiring don’t just want people who watched tutorials; they want builders who can take messy data and create value.
Every expert ML engineer was once exactly where you are now – standing at the beginning, wondering if they could really do this. The answer is yes, you absolutely can. With dedication, consistent daily effort following this roadmap, and proper guidance, you’ll be amazed at your transformation in just three months.
Ready to accelerate your ML career transformation?
📞 Contact Frontlines Edutech: +91-83330 77727
📧 Email: media.frontlines@gmail.com
🌐 Website: www.frontlinesedutech.com
Follow us on LinkedIn: Frontlines Media and Frontlines Media – Ignited Minds for daily learning content, industry insights, and success stories from our alumni community.
Don’t wait for the “perfect time” – it doesn’t exist. Your future as a machine learning engineer starts with Day 1 of this roadmap. Let’s build something extraordinary together.
5. Why Choose Frontlines Edutech for Your Machine Learning & Deep Learning Course?
Completing this 90-day roadmap independently requires tremendous discipline, access to quality resources, and guidance when stuck. Frontlines Edutech transforms this challenging journey into a structured, mentor-supported learning experience.
Industry-Standard Comprehensive Curriculum
The course covers everything from mathematics foundations through advanced deep learning and generative AI – identical to the roadmap above but with expert instruction, live Q&A sessions, and daily assignments ensuring you truly master each concept rather than just watching tutorials.
Expert Mentorship from Top Industry Professionals
Learn from passionate mentors working at leading tech companies who bring real-world experience into every session. Get personalized feedback on your projects, code reviews improving your programming practices, and insider perspectives on what companies actually seek in ML engineers.
From Scratch to Master Level Training
Whether you’re a complete beginner or have some programming background, the course starts from fundamentals ensuring nobody gets left behind. Special care is given to non-IT students through additional support sessions and simplified explanations making complex concepts accessible.
Complete Career Support Package
Resume Building: Professional resume crafting highlighting your projects and skills optimally for ATS systems and recruiter attention.
LinkedIn Profile Optimization: Transform your LinkedIn into a recruiter magnet with strategic positioning, keyword optimization, and professional branding.
Interview Guidance: Mock interviews, common question preparation, and strategies for handling technical rounds, case studies, and behavioral interviews.
Placement Updates: Regular notifications about hiring companies, referral opportunities, and direct connections with recruiting partners including Infosys, TCS, Accenture, Wipro, Cognizant, and Capgemini.
Hands-On Learning with Real Projects
Theory alone never got anyone hired. Complete multiple industry-aligned projects including customer segmentation, fraud detection, image classification, and sentiment analysis. Build a portfolio that demonstrates practical skills employers value.
Certification & Continued Resources
Receive a course completion certificate validating your skills. Access on-demand video lectures for revision, downloadable resources including cheat sheets and code templates, and lifetime access to course materials for continuous learning.
Affordable Investment in Your Future
Top machine learning bootcamps charge ₹1.5-3 lakhs. Frontlines Edutech delivers the same comprehensive training, mentorship, and placement support at a fraction of the cost, making career transformation accessible to everyone committed to learning.
Join Thousands of Successful Alumni
Frontlines Edutech has earned recognition and trust from thousands of learners across the state who’ve successfully transitioned into ML and AI roles. The testimonials speak for themselves – this course genuinely transforms careers.