Elevate Your Tech Career With
TOPUP SKILL
“Discover Your Path to Success in Data Analytics, Data Science, and Machine Learning by staying market relevant and real time Interview Questions”.
2000+Students
48.5 LPA Package
5 Stars Rating
WHY CHOOSE US
Why Topup Skill?
Structured industry vetted training programs
Learn what you will actually do in the industry by real life problem solving with the mentor.We have structured the training for everyone from a beginner to experienced to ace in their career.
Opt 1 to 1 training with the mentor to get proper attention and training at your pace and time. We have both 1 to 1 and 1v4 options available for you to decide what’s best for your learning.
LIVE 1 to 1 Coaching with Mentor
Practice Interview questions with the mentor after completing the training and assignments before applying for a new job to get a hike or land a better package with the right answering skills.
Job Ready Interview Preparation
Clear all your doubts from training and assignments on a weekly basis with the mentor on a LIVE call. This will make your learnings of the week solid and you can easily move forward next week with better understanding.
Weekly LIVE Doubt Sessions
Get your hands on industry grade projects and understand the approach needed to solve those challenges along with learning the theory part behind it. Gain practical experience by implementing theory on problems.
Practical Experience With Industry Grade Projects
Get exclusive support from the mentor and team in every aspect of your journey of excelling your career. We are here to help you at every stage during your training program.
Extensive Student
Mentorship & Support
MEET VED
Hi, I am Ved Prakash
With 8+ years of experience in Data Science, Machine Learning, LLM, DevOps, MLOps, SQL, Deep Learning & NLP, I am currently working with Dun & Bradsheet, Singapore and developing their generative AI solutions for advertisement technologies, using deep learning, computer vision, and natural language processing.
To be honest with you, I am not a course provider nor I like courses.
Neither will I leave corporate and start only teaching people how to excel in their career.
I am here to help you master Data Science using my experience and industry knowledge with the help of a structured training program suiting your needs and time because I struggled a lot when I needed this knowledge and support at my time, and I don’t want this to happen to you.
Know more
I train working professionals because I love to help them out. My journey of training students started in 2017 because of my passion to learn more and switch my career. I struggled a lot, invested in various courses, got rejected many times, only to find out that theory is available everywhere but you can’t learn without implementing or solving problems.
And so I started practicing a lot, but no one was there to solve my doubts instantly.I have to google for hours to get that answer. Meanwhile I started training my colleagues and learned that I was able to solve more because of working with them. It not only helped them but me also.
So,I started training students & via training others I learned a lot. I have mentored in Upgrad & TopMentor too but the real connection with students was missing in a batch of 80-100 students.
That’s why I started TopUp Skill, so that I can help you personally and with separate attention and focus towards each of you. I can only help you via 1on1 training or 1on4 training. With my knowledge and experience, I will help you to learn industry vetted knowledge and real time problem solving so that you can gain experience before entering into the industry.
I have put in my whole efforts to present you with the best training program at your own pace which you can manage with both your job and house work. I have incorporated effective learning approaches to master Data Science that are being used in the current industries. I will be there guiding you through every step of your journey.
Hope to see you in the training !
ALWAYS THERE
With Our Flexible Programs For You
Don't Want To Learn With 50 Others?
Missed a Class?
Have Doubts?
Have Less Time Due To Family/Job?
Long Career Gap or Stuck at a Position
Urgent Work/ Family Thing Came Up?
courses
Curriculum of Topup Skill
Beginner
Data Analysis with Microsoft Excel
Module 1: Excel Basics and Data Handling
● Introduction to Excel for Data Analysis
● Data Entry and Management
● Formulas and Functions
Module 2 :Cleaning and Wrangling Data using Excel
● Data Importing and Validation
● Data Entry and Management
● Data Wrangling Best Practices
Module 3:Analyzing Data using Excel
● Data Filtering and Sorting
● Advanced Excel Functions
● Pivot Tables and Slicers
● What-IF Analysis
● Cohort Analysis
Module 4: Visualizing and Dashboarding using Excel
● Data Visualization Techniques
● Interactive Dashboard Creation
Module 5: Capstone Project
● End to End Data Analysis Project
● Peer Review and Feedback
SQL for Data Analysis
Module 1: Introduction to SQL
● Overview of SQL and Its Importance in Data Analysis
● Understanding Data Types and SQL Statements
● Walkthrough of MySQL Workbench Interface
Module 2: Working with SQL on Single Table
● Introduction to Scenario and Scope of Data Analysis Work
● Basic SQL Queries
● Filtering and Sorting Data
● Advanced Data Selection Techniques
Module 3: Working with SQL on Multiple Tables
● Understanding Database Normalization and Relationships
● Keys and Schema Design
● Table Joins and Unions
● Working with Views, Temporary Tables, and Sub-Queries
Module 4: Working with SQL Functions and Operators
● Using SQL Functions for Data Manipulation
● Type Conversion and Flow Control
Module 5: Advanced SQL Topics
● Common Table Expressions (CTEs)
● Window Functions
● Stored Procedures and User-Defined Functions
Module 6: Capstone Project
● End-to-End SQL Data Analysis Project
● Peer Review and Feedback
POWER BI for Data Analysis & Visualization.
Module 1: Introduction to Power BI Desktop
● Overview of Power BI and Its Significance
● Walkthrough of Power BI Interface
● Setting Up Power BI Desktop
Module 2: Connecting & Shaping Data
● Connecting to Data Sources
● Working with Power Query Editor
Module 3: Creating a Data Model
● Understanding Data Modeling in Power BI
● Building Relationships in Power BI
● Optimizing Data Models
Module 4: Calculating Measures with DAX
● Introduction to Data Analysis Expressions (DAX)
● Working with DAX Functions
● Building Measure Tables and Quick Measures
Module 5: Visualizing Data with Dashboards
● Data Visualization Best Practices
● Creating and Formatting Visualizations
● Designing Interactive Dashboards
● Optimizing Dashboards for Mobile and Web
Module 6: Advanced Topics in Power BI
● Leveraging AI in Power BI
● Optimizing Power BI Performance
Module 7: Capstone Project
● End-to-End Power BI Dashboarding Project
● Peer Review and Feedback
PYTHON for Programming and Data Analysis
Module 1: Introduction to Programming with Python
● Why Learn Python for Data Analysis
● Setting Up the Python Environment
● Writing Your First Python Program
● Understanding Python Data Types
Module 2: Python I/O Operations
● File Operations in Python
● Working with Python Operators
Module 3: Control Flow in Python
● Working with Python Statements
● Nested Statements and Loops
Module 4: Python Functions and Modules
● Introduction to Python Functions
● Lambda Functions and Functional Programming
● Working with Modules and Packages
Module 5: Data Cleaning and Wrangling with Python
● Introduction to the Pandas Library
● Exploring and Understanding Your Data
● Data Selection and Manipulation
● Handling Data Quality Issues
● Data Transformation Techniques
● Advanced Data Wrangling
● Data Profiling and Troubleshooting
Module 6: Data Visualization with Python
● Introduction to Data Visualization with Matplotlib and Seaborn libraries
● Creating Basic Charts and Plots
● Creating Basic Charts and Plots
● Building and Formatting Visualizations
● Data Visualization Project
Module 7: Creating Interactive Dashboards with Python (Optional)
● Introduction to Interactive Dashboards
● Getting Started with Streamlit
● Working with Gradio
● Deploying Dashboards for Data-Driven Insights
● Interactive Dashboard Project
Module 8: Capstone Project
● End-to-End Data Analysis Project
● Peer Review and Feedback
Portfolio Project
● Project Integration
● Real-World Scenario
● Presentation
Intermediate
Data Analysis with MICROSOFT EXCEL
Module 1: Exploratory Data Analysis (EDA)
● Introduction to EDA
● Practical EDA with Excel
● Practical EDA with Python
Module 2: Data and Sampling Distributions
● Understanding Data Distributions
● Sampling Techniques and the Central Limit Theorem
● Practical Applications
Module 3: Statistical Experiments and Significance Testing
● Designing Statistical Experiments
● Conducting Significance Testing
● Practical Implementation
Module 4: Regression and Predictions
● Linear and Multiple Regression Analysis
● Logistic Regression for Classification
● Practical Implementation
Module 5: Classification Techniques
● Overview of Classification Techniques
● Practical Applications
● Evaluating Classification Models
Module 6: Statistical Supervised Machine Learning
● Introduction to Supervised Learning
● Practical Implementation
Module 7: Statistical Unsupervised Machine Learning
● Introduction to Unsupervised Learning
● Practical Applications
Module 8: Capstone Project and Practical Applications
● End-to-End Statistical Analysis Project
● Real-World Case Studies
Advanced SQL with Cloud Technologies
(Google Big Query, Redshift)
(Google Big Query, Redshift)
Module 1: SQL Refresher
● Fundamental SQL Concepts
● Working with Cloud Databases
Module 2: Common Table Expressions (CTEs)
● Introduction to CTEs
● Practical Applications of CTEs
Module 3: Advanced SQL Functions
● Window Functions
● Analytical and Aggregate Functions
Module 4: Performance Optimization in Cloud SQL
● Query Optimization Techniques
● Handling Large Datasets
Module 5: Advanced Data Modeling and SQL Techniques
● Data Modeling in Cloud Databases
● Nested and Repeated Data Structures
Module 6: Integrating SQL with Other Tools and Technologies
● Using SQL with BI Tools
● SQL and Scripting
Module 7: Security and Compliance in Cloud SQL
● Data Security and Access Control
● Compliance and Auditing
Module 8: Capstone Project
● End-to-End Cloud SQL Project
Advanced Python Programming with
Cloud Technologies for Big Data,
Data Pipelines, Building API’s.
Cloud Technologies for Big Data,
Data Pipelines, Building API’s.
Module 1: The Machine Learning Landscape
● Introduction to Machine Learning
● Setting Up Your Environment
Module 2: End-to-End Machine Learning Project
● Defining a Real-World Problem
● Building and Deploying the Model
● Using Notebooks in Practice
Module 3: Classification and Metrics
● Understanding Classification Algorithms
● Evaluation Metrics
● Improving Classification Models
Module 4: Regression and Model Training
● Introduction to Regression Models
● Model Training Techniques
● Hyperparameter Tuning
Module 5: Support Vector Machine (SVM)
● Understanding SVM
● Kernel Trick and Non-Linear SVM
● Improving SVM Performance
Module 6: Decision Trees and Ensemble Learning
● Decision Trees
● Ensemble Learning and Random Forest
● Advanced Ensemble Techniques
Module 7: Dimensionality Reduction
● Introduction to Dimensionality Reduction
● Implementing Dimensionality Reduction
Module 8: Unsupervised Learning Techniques
● Introduction to Unsupervised Learning
● Dimensionality Reduction for Unsupervised Learning
Module 9: Capstone Project
● End-to-End Machine Learning Project
Machine Learning and algorithms with Python
Module 1: Advanced Python Programming
● Object-Oriented Programming (OOP) with Python
● Python for Data Processing
● Working with Databases and ORMs
Module 2: Cloud Technologies for Big Data Processing
● Introduction to Cloud Platforms (AWS, GCP)
● Building Data Pipelines on AWS
● Building Data Pipelines on GCP
Module 3: API Development and Testing
● Building REST APIs with Flask
● Advanced API Development with FastAPI
● Testing APIs with Postman
Module 4: Building and Deploying Data Processing Pipelines
● Designing Data Processing Pipelines
● Deploying Pipelines in Cloud Environments
● Automation and Orchestration
Module 5: Security, Compliance, and Best Practices
● Securing Cloud-Based Applications
● Compliance and Data Governance
● Monitoring and Logging
Module 6: Capstone Project
● End-to-End Data Processing and API Development Project
Building and deploying end-to-end
Machine Learning solution
Machine Learning solution
Module 1: Introduction to Machine Learning Deployment
● Overview of Deployment in Machine Learning
Module 2: Local Deployment
● Building and Deploying Models Locally
● Testing and Monitoring Local Deployments
Module 3: Cloud Deployment on AWS
● Introduction to AWS for ML Deployment
● Deploying Models on AWS
Module 4: Cloud Deployment on GCP
● Introduction to GCP for ML Deployment
● Deploying Models on GCP
Module 5: Advanced Deployment Techniques
● Using Containerization with Docker
● CI/CD for Machine Learning
● MLOps Fundamentals
Module 6: Scaling and Managing ML Models in Production
● Optimizing Models for Production
● Monitoring and Managing ML Models in Production
Module 7: Capstone Project
● End-to-End Machine Learning Solution
Advance
Mathematics for Data Science
Module 1: Basic Math and Calculus Review
● Fundamental
● Concepts Application in Data Science
Module 2: Linear Algebra for Data Science
● Vectors and Matrices
● Applications in Data Science
Module 3: Probability and Statistics
● Probability Theory
●Descriptive and Inferential Statistics
● Applications in Data Science
Module 4: Linear and Logistic Regression
● Linear Regression
● Logistic Regression and Classification
Module 5: Advanced Topics in Mathematics for Data Science
● Generalized Linear Models (GLMs)
● Matrix Decomposition Techniques
Module 6: Practical Applications and Case Studies
● Mathematical Modeling in Data Science
● Integration with Python
Deep Dive to Machine Learning Algorithms
Module 1: Introduction to Machine Learning
● Understanding Machine Learning
● The Machine Learning Workflow
● Setting Up Your Environment
Module 2: Supervised Learning Essentials
● Linear Methods for Regression
● Linear Methods for Classification
● Model Regularization
● Model Assessment and Selection
Module 3: Advanced Regression Models
● Additive Models
● Generalized Linear Models
● Additive Regression Models
Module 4: Ensemble Learning and Tree-Based Models
● Introduction to Ensemble Methods
● Tree-Based Models
● Boosting Algorithms
● Advanced Ensemble Methods
Module 5: Support Vector Machines & Nearest-Neighbors
●Support Vector Machines (SVM)
● K-Nearest Neighbors (KNN)
Module 6: Unsupervised Learning Techniques
● Clustering Algorithms
● Dimensionality Reduction Techniques
● Association Rules
Module 7: Performance Optimization & Real-World Applications
● Hyperparameter Tuning & Model Optimization
● Model Interpretability
●Deploying Machine Learning Models
Module 8: Case Studies & Project
● Real-World Case Studies
● Capstone Project
Deep Dive to Deep Learning Algorithms
Module 1: Introduction to Deep Learning
● Foundations of Deep Learning
● Deep Learning Frameworks
Module 2: Artificial Neural Networks (ANNs)
● Understanding Neural Network
● Training Neural Networks
● Practical Considerations
Module 3: Optimization Techniques for Neural Networks
● Advanced Optimization Methods
● Model Performance Optimization
Module 4: Deep Computer Vision with Convolutional Neural Networks (CNNs)
● Introduction to CNNs
● Advanced CNN Techniques
Module 5: Natural Language Processing with RNNs, Transformers, and Attention Mechanisms
● Recurrent Neural Networks (RNNs)
● Transformers and Attention Mechanisms
● Advanced NLP Models
Module 6: Generative Models: Autoencoders, GANs, and Diffusion Models
● Autoencoders and Variational Autoencoders (VAEs)
● Generative Adversarial Networks (GANs)
● Diffusion Models
Module 7: Reinforcement Learning (RL)
● Foundations of Reinforcement Learning
● Advanced RL Techniques
Module 8: Deep Learning for Graphs: Deep Graph Learning Models
● Introduction to Graph Neural Networks (GNNs)
● Applications of GNNs
Optional Module: Operational Research with Deep Learning
● Optimization Problems in Operational Research
● Real-World Applications and Challenges
Generative AI
Module 1: Introduction to Large Language Models (LLMs)
● Foundations of Generative AI and LLMs
● LLM Architectures and Their Applications
Module 2: LLMs in Practice
● Introduction to Prompting
● Embeddings for LLMs and Vector Databases
● Introduction to LangChain and LlamaIndex
Module 3: Retrieval-Augmented Generation (RAG)
● Understanding RAG
● Advanced Retrieval-Augmented Generation
Module 4: Agents in Generative AI
● Introduction to AI Agents
● Advanced Agent Design and Deployment
Module 5: Fine-Tuning and Customization
● Fine-Tuning LLMs
● Evaluation and Optimization
Module 6: Deployment of LLMs
● Model Quantization
● Model Distillation and Pruning
● Deploying LLMs in Production with Cloud-GPU
● LLMs Observability, Monitoring
Module 7: Capstone Project
● End-to-End LLM
● Application Development
Natural Language Processing
Module 1: Introduction to NLP and Basic Tools
● Introduction to NLP
● Basics of Natural Language Toolkit (NLTK)
● N-grams in NLTK and Tokenization in spaCy
● POS Tagging Using NLTK
● Semantic Analysis and Word Vectors Using spaCy
Module 2: Language Modeling and Word Embeddings
● Introduction to Language Modeling
● Word Embeddings Techniques
Module 3: Deep Learning for NLP
● Understanding Recurrent Networks (RNNs)
● Advanced RNNs and Attention Mechanisms
● Sentiment Analysis and Text Classification
Module 4: Transformer Models and Advanced NLP
● Understanding Transformers
● Advanced Transformer Techniques
● State-of-the-Art Models
Module 5: Applied NLP: Real-World Use Cases and Challenges
● Preview of Language AI Use Cases
● Multimodal Large Language Models
Module 6: Building and Deploying NLP Systems
●Creating Language AI Models and Systems
● Fine-Tuning and Customization
● Deploying NLP Models in Production
Module 7: Capstone Project
● End-to-End NLP System Development
Recommendation Systems
Module 1: Introduction to Recommendation Systems
● Overview of Recommendation Systems
● Types of Recommendation Systems
Module 2: Content-Based Recommendation Systems
● Fundamentals of Content-Based Filtering
● Challenges and Enhancements
Module 3: Collaborative Filtering Based Recommendation Systems
● User-Based and Item-Based Collaborative Filtering
● Advanced Collaborative Filtering Techniques
Module 4: Knowledge-Based Recommendation Systems
● Understanding Knowledge-Based Recommenders
● Applications of Knowledge-Based Recommenders
Module 5: Deep Learning-Based Recommendation Systems
● Deep Learning and Feature Interaction
● Graph Representation Learning and Recommender Systems
● Sequential Recommender Systems
● Recommender Systems Combined with Knowledge Graphs
● Reinforcement Learning-Based Recommendation Algorithms
Module 6: Practical Recommendation Systems
● Building and Deploying Recommender Systems
● Evaluation and Metrics
Module 7: Application Challenges for Recommendation Systems
● Scalability and Performance Optimization
● Responsible Recommendations
Module 8: Capstone Project
● Comprehensive Recommender System Development
Machine Learning System Design
Module 1: Introduction to System Design
● Understanding System Design
● Machine Learning System Design Landscape
Module 2: Deep Learning Model Architectures
● Fundamentals of Deep Learning Architectures
● Advanced Model Architectures
Module 3: Framing Business Problems as ML Problems & Data Processing
● Defining the ML Objective
●Data Collection and Preparation
●Data Processing Pipelines
● Feature Engineering and Generation
● Cloud Integration and Deployment
Module 4: Model Selection, Training, and Serving
● Model Selection and Training
● Model Serving and Deployment
Module 5: Real-World ML System Design Case Studies
● Visual Search System
● YouTube Video Search and Recommendation
● Harmful Content Detection
Module 6: Advanced System Design Applications
● Event Recommendation System
● Ad Click Prediction on Social Platforms
● Similar Listing on Vacation Rental Platforms
● Personalized News Feed
● People You May Know
Module 7: Capstone Projects and Best Practices
● Designing and Implementing a Comprehensive ML System
● Best Practices in ML System Design
Advanced Deployment with
Full-Stack Machine Learning system
Full-Stack Machine Learning system
Module 1: Introduction to Full-Stack Machine Learning Deployment
● Understanding Full-Stack Deployment
● Setting Up the Development Environment
Module 2: Frontend Development for ML Systems
● Introduction to Frontend Technologies
● Frontend Framework
● Integrating Frontend with Backend
● Visualization of ML Model Outputs
Module 3: Backend Development and Cloud Integration
● Data Processing and Pipeline Building
● ML Model Training and Inference
● Cloud Integration
Module 4: MLOps in Action: Automation and Scaling
● Containerization with Docker
● Scaling Containers with Kubernetes
● Continuous Integration and Continuous Deployment (CI/CD)
Module 5: Observability and Monitoring in Production
● Monitoring ML Models
● Logging and Error Tracking
● Managing Environments with Kustomize
Module 6: Advanced Topics and Real-World Applications
● Advanced Deployment Scenarios
● Security and Compliance
● Case Studies and Industry Applications
Module 7: Capstone Projects
● Designing and Deploying a Full-Stack ML System
Module 1: Excel Basics and Data Handling
● Introduction to Excel for Data Analysis
● Data Entry and Management
● Formulas and Functions
Module 2 :Cleaning and Wrangling Data using Excel
● Data Importing and Validation
● Data Entry and Management
● Data Wrangling Best Practices
Module 3:Analyzing Data using Excel
● Data Filtering and Sorting
● Advanced Excel Functions
● Pivot Tables and Slicers
● What-IF Analysis
● Cohort Analysis
Module 4: Visualizing and Dashboarding using Excel
● Data Visualization Techniques
● Interactive Dashboard Creation
Module 5: Capstone Project
● End to End Data Analysis Project
● Peer Review and Feedback
Module 1: Introduction to SQL
● Overview of SQL and Its Importance in Data Analysis
● Understanding Data Types and SQL Statements
● Walkthrough of MySQL Workbench Interface
Module 2: Working with SQL on Single Table
● Introduction to Scenario and Scope of Data Analysis Work
● Basic SQL Queries
● Filtering and Sorting Data
● Advanced Data Selection Techniques
Module 3: Working with SQL on Multiple Tables
● Understanding Database Normalization and Relationships
● Keys and Schema Design
● Table Joins and Unions
● Working with Views, Temporary Tables, and Sub-Queries
Module 4: Working with SQL Functions and Operators
● Using SQL Functions for Data Manipulation
● Type Conversion and Flow Control
Module 5: Advanced SQL Topics
● Common Table Expressions (CTEs)
● Window Functions
● Stored Procedures and User-Defined Functions
Module 6: Capstone Project
● End-to-End SQL Data Analysis Project
● Peer Review and Feedback
Module 1: Introduction to Power BI Desktop
● Overview of Power BI and Its Significance
● Walkthrough of Power BI Interface
● Setting Up Power BI Desktop
Module 2: Connecting & Shaping Data
● Connecting to Data Sources
● Working with Power Query Editor
Module 3: Creating a Data Model
● Understanding Data Modeling in Power BI
● Building Relationships in Power BI
● Optimizing Data Models
Module 4: Calculating Measures with DAX
● Introduction to Data Analysis Expressions (DAX)
● Working with DAX Functions
● Building Measure Tables and Quick Measures
Module 5: Visualizing Data with Dashboards
● Data Visualization Best Practices
● Creating and Formatting Visualizations
● Designing Interactive Dashboards
● Optimizing Dashboards for Mobile and Web
Module 6: Advanced Topics in Power BI
● Leveraging AI in Power BI
● Optimizing Power BI Performance
Module 7: Capstone Project
● End-to-End Power BI Dashboarding Project
● Peer Review and Feedback
Module 1: Introduction to Programming with Python
● Why Learn Python for Data Analysis
● Setting Up the Python Environment
● Writing Your First Python Program
● Understanding Python Data Types
Module 2: Python I/O Operations
● File Operations in Python
● Working with Python Operators
Module 3: Control Flow in Python
● Working with Python Statements
● Nested Statements and Loops
Module 4: Python Functions and Modules
● Introduction to Python Functions
● Lambda Functions and Functional Programming
● Working with Modules and Packages
Module 5: Data Cleaning and Wrangling with Python
● Introduction to the Pandas Library
● Exploring and Understanding Your Data
● Data Selection and Manipulation
● Handling Data Quality Issues
● Data Transformation Techniques
● Advanced Data Wrangling
● Data Profiling and Troubleshooting
Module 6: Data Visualization with Python
● Introduction to Data Visualization with Matplotlib and Seaborn libraries
● Creating Basic Charts and Plots
● Creating Basic Charts and Plots
● Building and Formatting Visualizations
● Data Visualization Project
Module 7: Creating Interactive Dashboards with Python (Optional)
● Introduction to Interactive Dashboards
● Getting Started with Streamlit
● Working with Gradio
● Deploying Dashboards for Data-Driven Insights
● Interactive Dashboard Project
Module 8: Capstone Project
● End-to-End Data Analysis Project
● Peer Review and Feedback
● Project Integration
● Real-World Scenario
● Presentation
Module 1: Exploratory Data Analysis (EDA)
● Introduction to EDA
● Practical EDA with Excel
● Practical EDA with Python
Module 2: Data and Sampling Distributions
● Understanding Data Distributions
● Sampling Techniques and the Central Limit Theorem
● Practical Applications
Module 3: Statistical Experiments and Significance Testing
● Designing Statistical Experiments
● Conducting Significance Testing
● Practical Implementation
Module 4: Regression and Predictions
● Linear and Multiple Regression Analysis
● Logistic Regression for Classification
● Practical Implementation
Module 5: Classification Techniques
● Overview of Classification Techniques
● Practical Applications
● Evaluating Classification Models
Module 6: Statistical Supervised Machine Learning
● Introduction to Supervised Learning
● Practical Implementation
Module 7: Statistical Unsupervised Machine Learning
● Introduction to Unsupervised Learning
● Practical Applications
Module 8: Capstone Project and Practical Applications
● End-to-End Statistical Analysis Project
● Real-World Case Studies
Module 1: SQL Refresher
● Fundamental SQL Concepts
● Working with Cloud Databases
Module 2: Common Table Expressions (CTEs)
● Introduction to CTEs
● Practical Applications of CTEs
Module 3: Advanced SQL Functions
● Window Functions
● Analytical and Aggregate Functions
Module 4: Performance Optimization in Cloud SQL
● Query Optimization Techniques
● Handling Large Datasets
Module 5: Advanced Data Modeling and SQL Techniques
● Data Modeling in Cloud Databases
● Nested and Repeated Data Structures
Module 6: Integrating SQL with Other Tools and Technologies
● Using SQL with BI Tools
● SQL and Scripting
Module 7: Security and Compliance in Cloud SQL
● Data Security and Access Control
● Compliance and Auditing
Module 8: Capstone Project
● End-to-End Cloud SQL Project
Module 1: Advanced Python Programming
● Object-Oriented Programming (OOP) with Python
● Python for Data Processing
● Working with Databases and ORMs
Module 2: Cloud Technologies for Big Data Processing
● Introduction to Cloud Platforms (AWS, GCP)
● Building Data Pipelines on AWS
● Building Data Pipelines on GCP
Module 3: API Development and Testing
● Building REST APIs with Flask
● Advanced API Development with FastAPI
● Testing APIs with Postman
Module 4: Building and Deploying Data Processing Pipelines
● Designing Data Processing Pipelines
● Deploying Pipelines in Cloud Environments
● Automation and Orchestration
Module 5: Security, Compliance, and Best Practices
● Securing Cloud-Based Applications
● Compliance and Data Governance
● Monitoring and Logging
Module 6: Capstone Project
● End-to-End Data Processing and API Development Project
Module 1: The Machine Learning Landscape
● Introduction to Machine Learning
● Setting Up Your Environment
Module 2: End-to-End Machine Learning Project
● Defining a Real-World Problem
● Building and Deploying the Model
● Using Notebooks in Practice
Module 3: Classification and Metrics
● Understanding Classification Algorithms
● Evaluation Metrics
● Improving Classification Models
Module 4: Regression and Model Training
● Introduction to Regression Models
● Model Training Techniques
● Hyperparameter Tuning
Module 5: Support Vector Machine (SVM)
● Understanding SVM
● Kernel Trick and Non-Linear SVM
● Improving SVM Performance
Module 6: Decision Trees and Ensemble Learning
● Decision Trees
● Ensemble Learning and Random Forest
● Advanced Ensemble Techniques
Module 7: Dimensionality Reduction
● Introduction to Dimensionality Reduction
● Implementing Dimensionality Reduction
Module 8: Unsupervised Learning Techniques
● Introduction to Unsupervised Learning
● Dimensionality Reduction for Unsupervised Learning
Module 9: Capstone Project
● End-to-End Machine Learning Project
Module 1: Introduction to Machine Learning Deployment
● Overview of Deployment in Machine Learning
Module 2: Local Deployment
● Building and Deploying Models Locally
● Testing and Monitoring Local Deployments
Module 3: Cloud Deployment on AWS
● Introduction to AWS for ML Deployment
● Deploying Models on AWS
Module 4: Cloud Deployment on GCP
● Introduction to GCP for ML Deployment
● Deploying Models on GCP
Module 5: Advanced Deployment Techniques
● Using Containerization with Docker
● CI/CD for Machine Learning
● MLOps Fundamentals
Module 6: Scaling and Managing ML Models in Production
● Optimizing Models for Production
● Monitoring and Managing ML Models in Production
Module 7: Capstone Project
● End-to-End Machine Learning Solution
Module 1: Basic Math and Calculus Review
● Fundamental
● Concepts Application in Data Science
Module 2: Linear Algebra for Data Science
● Vectors and Matrices
● Applications in Data Science
Module 3: Probability and Statistics
● Probability Theory
●Descriptive and Inferential Statistics
● Applications in Data Science
Module 4: Linear and Logistic Regression
● Linear Regression
● Logistic Regression and Classification
Module 5: Advanced Topics in Mathematics for Data Science
● Generalized Linear Models (GLMs)
● Matrix Decomposition Techniques
Module 6: Practical Applications and Case Studies
● Mathematical Modeling in Data Science
● Integration with Python
Module 1: Introduction to Machine Learning
● Understanding Machine Learning
● The Machine Learning Workflow
● Setting Up Your Environment
Module 2: Supervised Learning Essentials
● Linear Methods for Regression
● Linear Methods for Classification
● Model Regularization
● Model Assessment and Selection
Module 3: Advanced Regression Models
● Additive Models
● Generalized Linear Models
● Additive Regression Models
Module 4: Ensemble Learning and Tree-Based Models
● Introduction to Ensemble Methods
● Tree-Based Models
● Boosting Algorithms
● Advanced Ensemble Methods
Module 5: Support Vector Machines & Nearest-Neighbors
●Support Vector Machines (SVM)
● K-Nearest Neighbors (KNN)
Module 6: Unsupervised Learning Techniques
● Clustering Algorithms
● Dimensionality Reduction Techniques
● Association Rules
Module 7: Performance Optimization & Real-World Applications
● Hyperparameter Tuning & Model Optimization
● Model Interpretability
●Deploying Machine Learning Models
Module 8: Case Studies & Project
● Real-World Case Studies
● Capstone Project
Module 1: Introduction to Deep Learning
● Foundations of Deep Learning
● Deep Learning Frameworks
Module 2: Artificial Neural Networks (ANNs)
● Understanding Neural Network
● Training Neural Networks
● Practical Considerations
Module 3: Optimization Techniques for Neural Networks
● Advanced Optimization Methods
● Model Performance Optimization
Module 4: Deep Computer Vision with Convolutional Neural Networks (CNNs)
● Introduction to CNNs
● Advanced CNN Techniques
Module 5: Natural Language Processing with RNNs, Transformers, and Attention Mechanisms
● Recurrent Neural Networks (RNNs)
● Transformers and Attention Mechanisms
● Advanced NLP Models
Module 6: Generative Models: Autoencoders, GANs, and Diffusion Models
● Autoencoders and Variational Autoencoders (VAEs)
● Generative Adversarial Networks (GANs)
● Diffusion Models
Module 7: Reinforcement Learning (RL)
● Foundations of Reinforcement Learning
● Advanced RL Techniques
Module 8: Deep Learning for Graphs: Deep Graph Learning Models
● Introduction to Graph Neural Networks (GNNs)
● Applications of GNNs
Optional Module: Operational Research with Deep Learning
● Optimization Problems in Operational Research
● Real-World Applications and Challenges
Module 1: Introduction to Large Language Models (LLMs)
● Foundations of Generative AI and LLMs
● LLM Architectures and Their Applications
Module 2: LLMs in Practice
● Introduction to Prompting
● Embeddings for LLMs and Vector Databases
● Introduction to LangChain and LlamaIndex
Module 3: Retrieval-Augmented Generation (RAG)
● Understanding RAG
● Advanced Retrieval-Augmented Generation
Module 4: Agents in Generative AI
● Introduction to AI Agents
● Advanced Agent Design and Deployment
Module 5: Fine-Tuning and Customization
● Fine-Tuning LLMs
● Evaluation and Optimization
Module 6: Deployment of LLMs
● Model Quantization
● Model Distillation and Pruning
● Deploying LLMs in Production with Cloud-GPU
● LLMs Observability, Monitoring
Module 7: Capstone Project
● End-to-End LLM
● Application Development
Module 1: Introduction to NLP and Basic Tools
● Introduction to NLP
● Basics of Natural Language Toolkit (NLTK)
● N-grams in NLTK and Tokenization in spaCy
● POS Tagging Using NLTK
● Semantic Analysis and Word Vectors Using spaCy
Module 2: Language Modeling and Word Embeddings
● Introduction to Language Modeling
● Word Embeddings Techniques
Module 3: Deep Learning for NLP
● Understanding Recurrent Networks (RNNs)
● Advanced RNNs and Attention Mechanisms
● Sentiment Analysis and Text Classification
Module 4: Transformer Models and Advanced NLP
● Understanding Transformers
● Advanced Transformer Techniques
● State-of-the-Art Models
Module 5: Applied NLP: Real-World Use Cases and Challenges
● Preview of Language AI Use Cases
● Multimodal Large Language Models
Module 6: Building and Deploying NLP Systems
●Creating Language AI Models and Systems
● Fine-Tuning and Customization
● Deploying NLP Models in Production
Module 7: Capstone Project
● End-to-End NLP System Development
Module 1: Introduction to Recommendation Systems
● Overview of Recommendation Systems
● Types of Recommendation Systems
Module 2: Content-Based Recommendation Systems
● Fundamentals of Content-Based Filtering
● Challenges and Enhancements
Module 3: Collaborative Filtering Based Recommendation Systems
● User-Based and Item-Based Collaborative Filtering
● Advanced Collaborative Filtering Techniques
Module 4: Knowledge-Based Recommendation Systems
● Understanding Knowledge-Based Recommenders
● Applications of Knowledge-Based Recommenders
Module 5: Deep Learning-Based Recommendation Systems
● Deep Learning and Feature Interaction
● Graph Representation Learning and Recommender Systems
● Sequential Recommender Systems
● Recommender Systems Combined with Knowledge Graphs
● Reinforcement Learning-Based Recommendation Algorithms
Module 6: Practical Recommendation Systems
● Building and Deploying Recommender Systems
● Evaluation and Metrics
Module 7: Application Challenges for Recommendation Systems
● Scalability and Performance Optimization
● Responsible Recommendations
Module 8: Capstone Project
● Comprehensive Recommender System Development
Module 1: Introduction to System Design
● Understanding System Design
● Machine Learning System Design Landscape
Module 2: Deep Learning Model Architectures
● Fundamentals of Deep Learning Architectures
● Advanced Model Architectures
Module 3: Framing Business Problems as ML Problems & Data Processing
● Defining the ML Objective
●Data Collection and Preparation
●Data Processing Pipelines
● Feature Engineering and Generation
● Cloud Integration and Deployment
Module 4: Model Selection, Training, and Serving
● Model Selection and Training
● Model Serving and Deployment
Module 5: Real-World ML System Design Case Studies
● Visual Search System
● YouTube Video Search and Recommendation
● Harmful Content Detection
Module 6: Advanced System Design Applications
● Event Recommendation System
● Ad Click Prediction on Social Platforms
● Similar Listing on Vacation Rental Platforms
● Personalized News Feed
● People You May Know
Module 7: Capstone Projects and Best Practices
● Designing and Implementing a Comprehensive ML System
● Best Practices in ML System Design
Module 1: Introduction to Full-Stack Machine Learning Deployment
● Understanding Full-Stack Deployment
● Setting Up the Development Environment
Module 2: Frontend Development for ML Systems
● Introduction to Frontend Technologies
● Frontend Framework
● Integrating Frontend with Backend
● Visualization of ML Model Outputs
Module 3: Backend Development and Cloud Integration
● Data Processing and Pipeline Building
● ML Model Training and Inference
● Cloud Integration
Module 4: MLOps in Action: Automation and Scaling
● Containerization with Docker
● Scaling Containers with Kubernetes
● Continuous Integration and Continuous Deployment (CI/CD)
Module 5: Observability and Monitoring in Production
● Monitoring ML Models
● Logging and Error Tracking
● Managing Environments with Kustomize
Module 6: Advanced Topics and Real-World Applications
● Advanced Deployment Scenarios
● Security and Compliance
● Case Studies and Industry Applications
Module 7: Capstone Projects
● Designing and Deploying a Full-Stack ML System
NOT SURE ABOUT WHERE TO START???
Don’t worry, we are here to help you. If you don’t know what to learn in between beginner,intermediate or advanced then schedule a free call with the mentor and ask your doubts related to the training program.
Roadmap
To Become Successful In Data Science
Analysis with SQL
Learn to query, manipulate, and manage databases to extract and analyze data efficiently using SQL.
Visualization & Dashboarding
Master tools and techniques for creating visual representations of data to communicate insights clearly and effectively.
Statistics
Develop a strong understanding of statistical methods, probability theory, and their applications in analyzing and interpreting data.
Python Programming
Gain proficiency in Python for data manipulation, analysis, and building data-driven applications using libraries like Pandas and NumPy.
Machine Learning
Acquire expertise in designing, training, and deploying predictive models using algorithms and frameworks such as Scikit-Learn and TensorFlow.
Data Science
Integrate skills across domains to tackle complex data problems, derive actionable insights, and contribute to strategic decision-making.
Analysis with SQL
Visualization & Dashboarding
Statistics
Python Programming
Machine learning
Data Science
TOOLS
You Will Get To Learn Here
Certificate
Get Recognized With the Training Completion Certificates and Goodies.
Training completion certificate will be awarded to you on successful completion of training and scoring more than 70% in the given assignments.
This certificate will add on value to your LinkedIn profile That can grab attention of our hiring partners and top Big Data companies.
JOURNEY
Start Learning Data Science In Shortest Time Possible
Easy Registration
In less than 2 minutes, register yourself by filling your profile and basic details & schedule a call to get the details.
Consult Mentor
Talk about your requirements with the mentor and get a roadmap and training details in a 30 minutes zoom call.
Enroll for Training
Sign up in the training program designed by the mentor exclusively for you as soon aa p and start your journey.
Start Learning and Implementing
Start attending the training and solve real life industry based assignments to apply your learnings
ALUMINI
Our Students Makes Us Proud
TESTIMONIAL
Our Success Story Lies in Our Learners Success
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YOUTUBE VIDEOS
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FAQs
Frequently Asked Questions
It is designed for working IT professionals or people who want to upgrade their profiles/ start their tech career after a long break. However, the training could be taken by graduates and professionals from other domains provided they can put in an additional effort.
The main training program is LIVE with the mentor. It is either 121 or in a group of 4 depending upon what you choose for your training. Some recorded problem solving lectures will be provided along with the training program for your better understanding.
Your doubts will be addressed at the end of the lecture and in the weekly LIVE doubt classes.
Lectures will be of 3 hours duration and twice a week. 6-10 hours of dedicated time per week is needed to learn and complete the assignments.
- You will be trained on the latest trending technologies in the Data Science & AI industry. The Capstone project that you would be implementing towards the end of the program will make you confident enough to handle any project.
It is designed for working IT professionals or people who want to upgrade their profiles/ start their tech career after a long break. However, the training could be taken by graduates and professionals from other domains provided they can put in an additional effort.
The main training program is LIVE with the mentor. It is either 121 or in a group of 4 depending upon what you choose for your training. Some recorded problem solving lectures will be provided along with the training program for your better understanding.
Your doubts will be addressed at the end of the lecture and in the weekly LIVE doubt classes.
Lectures will be of 3 hours duration and twice a week. 6-10 hours of dedicated time per week is needed to learn and complete the assignments.
- You will be trained on the latest trending technologies in the Data Science & AI industry. The Capstone project that you would be implementing towards the end of the program will make you confident enough to handle any project.
Contact Us
Contact us
- Oasisstudio@info.com
- (914) 937-6019
- 345 William St Port Chester, New York(NY), 10573
