Course Program Overview
Data Science, ML and Deep Learning Professionals
- Duration: 5 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 6 Hours Per Week
- Session Length: 1 Hour each
- Tech Stack: Mathematics and Programming for Machine Learning, Data Science, Machine Learning, Neural Network and Deep Learning, AI Debugging.
- Outcome: Industry-grade portfolio + Certification + Career acceleration
Become a AI & cloud Expert
Our placement Record
Transform Your Career with the Power of Data & AI
Why this course is your passport to the fastest-growing, best-paid careers of the decade.
This 5-month professional program in AI/ML and Data Science equips working professionals with deep, production-grade knowledge in predictive analytics, classical machine learning frameworks, and foundational deep artificial neural networks. Rather than skimming surface-level application wrappers, this course focuses on your unshakeable core: Core Mathematics First and Algorithm-Centric Programming.
Through intensive, live project-based training, you will bridge the gap between advanced data theory and enterprise implementation. You will learn to manipulate data landscapes natively line-by-line using tools like Python, Scikit-Learn, TensorFlow, and PyTorch, backed by a dedicated self-paced specialization track spanning major high-value global industries.
Build AI. Don’t Just Use It.
Join the only AI & ML course that teaches you the math, programming, and mindset behind ₹1 crore+ careers.
Why Choose Us
- Deep Mathematics: Calculus, probability, ability linear algebra, optimization
- Algorithm-Centric Programming: Python, TensorFlow, custom architectures
- Career-Built Curriculum: Designed for ₹1 crore+ AI roles
- Thought Leaders as Mentors: Resume coaching, GitHub setup, interview prep
Can You Crack This AI Math Challenge?
Solve for the gradient
Where exact behind how ChatGPT ables.
Your ₹1 Crore Career Starts Here.
Your ₹1 Crore Career Starts Here.
Who’s Hiring AI/ML Experts?
We design our training programs around real-world demand — here's who’s building with AI:
Tech & Cloud Leaders
Google · Microsoft · Amazon · Meta · Apple · IBM · NVIDIA
Scaling enterprise AI, cloud services, and generative technologies
AI Research & Platforms
OpenAI · DeepMind · Hugging Face · Anthropic · Cohere
Pioneering large language models, multimodal learning, and democratized AI
Industrial & Robotics AI
Tesla · Waymo · Boston Dynamics · ABB
Deploying autonomous systems for mobility, automation, and smart infrastructure
Consulting & Strategy
Accenture · PwC · Deloitte · EY · Palantir
Leading AI adoption and transformation for Fortune 500 clients
Finance & Fintech
JPMorgan · Goldman Sachs · Capital One · Stripe
Revolutionizing risk modeling, fraud detection, and customer intelligence
Media & Digital Productivity
Netflix · Spotify · Zoom · Notion · LinkedIn
Enhancing personalization with recommendation engines and content intelligence
The Urgent Global Demand & Future of AI/ML
The market is shifting rapidly. Companies are no longer looking for casual tool operators who copy-paste generic code; they are actively seeking true AI architects who understand underlying mathematical optimization, custom network design, and data pipeline scalability.
USA & Europe
Over 180,000+ technical AI jobs are actively listed by employers in the USA, with another 80,000+ active opportunities across Europe.
India
A massive explosion of 55,000+ active openings across major job portals, with total posted AI vacancies exceeding 1 Lakh.
Global Hubs
Regions like Canada, Australia, and the Middle East are investing heavily, with Dubai alone projecting up to 200,000 AI-related engineering roles as traditional industries automate.
Skills You Will Get
Master industry-aligned techniques in data science, predictive analytics, and deep learning—ranging from raw mathematical optimization and advanced data preprocessing to training complex neural networks from scratch.
30+ Tools
30+ Tools
20+ Real-Time Projects
20+ Real-Time Projects
Core Mathematics Mastery
Core Mathematics Mastery
Algorithm-Centric Programming
Algorithm-Centric Programming
Advanced Data Preprocessing
Advanced Data Preprocessing
Deep Learning Frameworks
Deep Learning Frameworks
AI Project Lifecycle Engineering
AI Project Lifecycle Engineering
Domain AI Integration
Domain AI Integration
Course Program
Classroom / Live Online | 5 Months (120 Hours)
This professional program is meticulously structured for working professionals who can commit to 6 hours per week. Instead of rushing through surface-level buzzwords, this course dedicates its timeline to establishing an unshakeable engineering foundation. Every month concludes with hands-on labs and challenge-based tasks to guarantee absolute production readiness.
● Month 1
Core Mathematics First & Python Foundations
This month establishes the raw mathematical and computational foundations required to think like an AI arcThis month establishes the raw mathematical and computational foundations required to think like an AI architect. It eliminates tool dependency by focusing on the underlying algorithms that drive data pipelines from scratch.hitect. It eliminates tool dependency by focusing on the underlying algorithms that drive data pipelines from scratch.
- Core & Advanced Content
- Python Foundations: Object-Oriented Programming (OOP) principles, classes, methods, and algorithmic complexity (Big-O notation).
- Data Infrastructure: High-performance data manipulation using NumPy arrays and Pandas DataFrames; handling missing values and structural data anomalies.
- Deep Mathematics for AI: Systems of linear equations, vector matrices, dot products, and matrix multiplication.
- Calculus & Optimization: Partial derivatives, directional gradients, and the mathematical mechanics of Gradient Descent.
- Statistical Inference: Probability distributions (Normal, Binomial), Central Limit Theorem, and Hypothesis Testing ($Z$-tests, $T$-tests).
- Training Methodics & Tasks: 10+ lab sessions, 12 practical assignments, and numerical mathematics problem-solving challenges.
- Skills You Get: High-level Scripting, Structural Data Preprocessing, Optimization Gradients, Algorithmic Clarity.
● Month 2
Supervised Machine Learning Architectures
Learners transition from pure data theory into constructing rigorous predictive models. This phase focuses on designing, training, and debugging classical supervised learning frameworks using real-world enterprise data.
- Core & Advanced Content
- Linear & Logistic Regression: Mathematical derivations, cost functions, Ordinary Least Squares (OLS), and binary classification boundaries.
- Instance-Based Models: $K$-Nearest Neighbors (KNN), Naive Bayes Classifiers, and Support Vector Machines (SVM) boundaries.
- Tree-Based Foundations: Structure of Decision Trees, Information Gain, Gini Impurity, and pruning strategies.
- Ensemble Methods & Boosting: Random Forests, Bootstrapping, Aggregation (Bagging), Gradient Boosting Machines (GBM), and XGBoost architecture.
- Advanced Feature Engineering: Mathematical transformations, polynomial features, and handling target class imbalances (SMOTE techniques).
- Training Methodics & Tasks: 8+ guided case studies, 15+ hands-on lab sessions using Scikit-Learn, and real-time algorithm debugging sprints.
- Skills You Get: Advanced Predictive Modeling, High-Level Feature Engineering, Supervised Learning.
● Month 3
Unsupervised Learning & Advanced Model Optimization
This month shifts to discovering hidden patterns in unlabeled data and mastering the mathematical evaluation metrics needed to validate and optimize models before enterprise integration.
- Core & Advanced Content
- Clustering Fundamentals: $K$-Means Clustering algorithms, within-cluster sum of squares (WCSS), and selecting optimal cluster numbers via the Elbow Method.
- Hierarchical Clustering: Agglomerative vs. Divisive strategies, dendrogram construction, and linkage criteria variations.
- Dimensionality Reduction: Principal Component Analysis (PCA) mathematical derivation, variance retention, and handling the "Curse of Dimensionality."
- Advanced Validation Strategies: $K$-Fold Cross-Validation, Stratified splits, Bias-Variance Tradeoff decomposition, and Hyperparameter Optimization via GridSearch.
- Performance Auditing: Confusion Matrix analysis, Precision, Recall, $F_1$-Score, and ROC-AUC curve analysis.
- Training Methodics & Tasks: 5+ unsupervised learning labs, complex statistical validation challenges, and model optimization sprints.
- Skills You Get: Dynamic Clustering, Pattern Recognition, Enterprise Model Assessment, Advanced Validation Strategy.
● Month 4
Introduction to Deep Learning & Neural Networks
Students bridge the gap between classical machine learning and deep artificial neural networks. You will learn how to design, write, and train computational networks line-by-line using industry-standard deep tech frameworks.
- Core & Advanced Content
- Foundations of Neural Networks: Biological to Artificial Neurons, the mathematical Perceptron model, linear separability limitations, and Multi-Layer Perceptrons (MLPs).
- Activation Functions: Mathematical properties and limitations of Sigmoid, Tanh, ReLU, and Leaky ReLU.
- Framework Onboarding: Working with tensors, computational graphs, and building neural networks in TensorFlow/Keras and PyTorch.
- Backpropagation Math: Deep dive into the Chain Rule of calculus for updating network weights and calculating error gradients.
- Advanced Optimization: Comparative analysis and coding implementations of Stochastic Gradient Descent (SGD), Momentum, and Adam optimizers.
- Training Methodics & Tasks: 10+ deep learning labs, building custom loss functions from scratch, and 5 practical assignments.
- Skills You Get: Neural Network Architecture Design, Custom Backpropagation Tuning, TensorFlow Mastery, PyTorch Scripting.
● Month 5
AI Project Lifecycle & Capstone Project
The final month centers on the end-to-end execution of a portfolio-grade project, replicating real-world AI challenges faced by elite global firms.
- Core & Advanced Content
- Project Scoping: Defining clear business problem statements, converting business goals into machine learning targets, and outlining data collection strategies.
- GitHub Engineering: Establishing clean version control, technical readme documentation, and building structured repositories for code review.
- AI Project Lifecycle Execution: Running the full lifecycle—from raw exploratory analysis and mathematical optimization to final model selection, hyperparameter validation, and metric evaluation.
- Enterprise Showcase: Structuring a complete portfolio review, presenting actionable technical metrics, and proving measurable business impact.
- Specialized Domain Integration (Self-Paced Track): Guided elective case studies spanning Healthcare (Disease risk prediction), BFSI (Real-time fraud detection pipelines), and Retail (Sales forecasting).
- Training Methodics & Tasks: 5+ Self-paced enterprise case studies, 1 Major Comprehensive Live Capstone Project, and a dedicated Portfolio Showcase.
- Skills You Get: End-to-End AI System Delivery, Challenge-Based Problem Solving, Portfolio Presentation.
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as Machine Learning Engineer, Cloud Solutions Architect, Data Scientist, and AI Product Lead—across sectors like technology, BFSI, healthcare, retail, and manufacturing.
Machine Learning Engineer
Designs, trains, and optimizes ML models for prediction, classification, and automation across industries like finance, healthcare, and manufacturing.
Data Scientist
Extracts insights from structured and unstructured data using statistical analysis, visualization, and modeling techniques. Drives decision-making and business intelligence.
AI Solutions Architect
Leads the design and deployment of scalable AI systems using cloud platforms, MLOps pipelines, and enterprise-grade frameworks
Deep Learning Researcher
Specializes in neural networks, CNNs, RNNs, GANs, and transformers. Builds models for image recognition, NLP, and generative tasks.
MLOps Engineer
Implements CI/CD pipelines, containerization, and cloud-native deployment for AI models. Ensures reliability, scalability, and performance monitoring.
Generative AI Developer
Builds intelligent applications using LangChain, Hugging Face, and LLMs. Applies RAG pipelines and transformer models to domains like legal tech and marketing.
Computer Vision Engineer
Develops image and video analysis systems using OpenCV, TensorFlow, and deep learning. Works on facial recognition, object detection, and OCR.
NLP Engineer
Creates language models and conversational AI systems using NLTK, SpaCy, and transformers. Powers chatbots, sentiment analysis, and document summarization.
AI Product Lead
Bridges technical teams and business strategy. Oversees AI product lifecycle—from ideation to deployment—ensuring alignment with market needs and ethical standards.
Our Clients
- Client Testimonials
What Our Clients Say
Blogs and Insights
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators