Course Program Overview
Data Science & Machine Learning Foundations
- Duration: 3 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 6 Hours Per Week (Designed for Working Professionals)
- Session Length: 1+ Hours per lecture (5 Sessions per week)
- Tech Stack: Mathematics and Logic for Data Science, Python Core Scripting, Matrix Manipulation, Exploratory Data Analysis (EDA), Advanced Data Preprocessing, Supervised Machine Learning Architectures, Model Validation, and Performance Auditing.
- Outcome: Foundational Industry Portfolio + Certification + Career Acceleration
Become a AI & cloud Expert
Our placement Record
Master the Algorithms Powering the Future
Explore the computational and statistical bedrock of modern technology with this 3-month professional foundation track engineered for developers, analysts, and aspiring data leaders. The curriculum is completely rooted in building algorithmic clarity, focusing on how mathematical logic translates into predictive engine code.
Learners dive deep into core linear algebra vector matrices, multivariable optimization calculus, and descriptive probability distributions—while also mastering structural programming logic and data preprocessing techniques used to transform messy enterprise datasets. The program emphasizes reproducible clean data pipelines, version-controlled repository setups on GitHub, and clear architecture-level modeling.
By combining rigorous mathematical theory with hands-on scripting labs and practical model building, this course prepares working professionals to crack core data analytics interviews and confidently contribute to enterprise-level machine learning frameworks.
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
Skills You Will Get
Master industry-aligned techniques in exploratory data analysis, statistical modeling, and classical machine learning—ranging from raw programming logic to building portfolio-grade predictive data tracks.
15+ Core Tools
15+ Core Tools
10+ Real-Time Projects
10+ Real-Time Projects
Core Analytics Mathematics
Core Analytics Mathematics
Basic Scripting & Logic
Basic Scripting & Logic
Advanced Data Preprocessing
Advanced Data Preprocessing
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA)
Supervised Analytics Modeling
Supervised Analytics Modeling
Model Validation & Strategy
Model Validation & Strategy
Course Program
Classroom / Live Online | 3 Months (72+ Hours)
This professional foundation track is specifically designed for working professionals who can commit to 6 hours per week. Instead of overwhelming you with deep learning networks or generative placeholders, this curriculum focuses entirely on building an unshakeable mathematical and programming base in core Data Science and predictive Machine Learning.
● Month 1
Core Mathematics First & Python Data Basics
This month builds the analytical and technical base essential for core data mastery. Learners explore foundational vector spaces, basic optimization calculus, and python-powered scripting blocks to engineer data pipelines.
- Basic to Advanced Content
- Python Core Programming: Variable structures, loops, functions, object-oriented concepts (OOP), and algorithmic logic.
- Data Storage Infrastructure: Working with structured file systems, relational databases, and utilizing Python dictionaries for raw data intake.
- Mathematical Baselines: Linear equations, vector properties, matrix multiplication, derivatives, and tracking parameter behavior during Gradient Descent.
- Training Methodics & Tasks: 8+ syntax lab sessions, 10 mathematical coding assignments, and data logic debugging sprints.
- Skills You Get: Clean Coding Logic, Structural Mathematics, Basic Data Manipulation.
● Month 2
Advanced Data Preprocessing & Exploratory Analysis
Learners transition from syntax into data engineering and visual analysis. This phase focuses on using advanced libraries to clean real-world data and extract clear, visual business insights.
- Basic to Advanced Content
- Matrix Data Engineering: Deep dive into NumPy arrays and Pandas DataFrame optimization for handling million-row tables.
- The Data Cleaning Pipeline: Handling missing data cells, multi-variant feature scaling, categorical transformations, and extreme outlier extraction techniques.
- Statistical Insights & EDA: Descriptive statistics (mean, median, variance), normal distributions, correlation metrics, and visual plotting with Matplotlib and Seaborn.
- Training Methodics & Tasks: 6+ comprehensive data cleaning labs, real-world exploratory assignments, and visual charting sprints.
- Skills You Get: Advanced Preprocessing, Visual Pattern Recognition, Exploratory Data Analysis.
● Month 3
Classical Machine Learning & Portfolio Capstone
The final month introduces predictive modeling. Students learn to implement and evaluate core supervised machine learning algorithms, culminating in a live portfolio-grade foundation capstone project.
- Basic to Advanced Content
- Supervised Framework Basics: Foundations of Linear Regression (predicting values) and Logistic Regression (binary classification modeling).
- Instance & Tree Models: Implementing $K$-Nearest Neighbors (KNN), Naive Bayes structures, and basic Decision Tree splits.
- Validation Mechanics: Understanding the Confusion Matrix, tuning accuracy targets (Precision vs. Recall), and setting train-test split splits with Scikit-Learn.
- Foundation Project Delivery: Project scoping, building an end-to-end predictive pipeline, version control documentation on GitHub, and clear portfolio presentation.
- Training Methodics & Tasks: 5+ algorithmic modeling labs, 1 Live Comprehensive Foundation Capstone Project, and a dedicated Portfolio Showcase.
- Skills You Get: Predictive Modeling, Model Evaluation Strategy, GitHub Versioning, Project 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
Hello world 3
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
Hello world 2
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
Hello world!
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
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