Introduction
Accreditations and tie-ups are important affiliations and partnerships that a company or institution can have to enhance its credibility, expand its reach, and offer value-added services. Here's a brief explanation of accreditations and tie-ups:.
Key Accreditations & Tie-Ups
- Indian Startup by the Department of Industrial Policy and Promotion
- National Apprenticeship Training Scheme(NATS)
- Registered with All India Council for Technical Education
- Certiport Authorized Testing Centre for Microsoft Exams
- IBM DC Delivery Partner- eLearning, Certifications
Key Tie-Ups
- HCL, Authorized Partner to hire for HCL Client
- Team Computer, Authorized Hiring Partner
Key Government Clients
- National Power Training Institute, Ministry of Power, Govt. of India
- Indian Railway
- Indian Navy
- Indian Post
- NIC
Key Corporate Clients
- HCL Technologies Ltd
- Independent News Services Private Ltd.(INDIA TV)
- NIIT Technologies Ltd
- Team Computers Pvt Ltd
- V Serv Infosystems Pvt Ltd.
- Substratal Solutions Pvt Ltd.
- Fox Trading Solutions
- Smart Brains Engineers & Technologist Pvt Ltd.
- Risk Management Solutions, San Francisco, California
- Absolutdata Holdings, Inc., Delaware , USA
- Intecco Technical Services
- What is a project
- Project Execution Methodologies
- Agile Deep Dive
- Scrum – Deep Dive
- Scrum Artifacts
- Scrum Ceremonies
- Scrum Sprint Planning
- Scrum Metrics
- Additional Info
- evOps Fundamentals
- DevOps Usecase
- Advanced DevOps
- Introduction to DevOps on IBM Cloud
- Introduction to Python
- Introduction
- Installation
- variables, Operators and Strings
- Deep Dive into Python
- Input Output functions
- Loops, List, dictionaries, tuples
- File Handler
- Python Libraries
- Pandas
- Series and Data Frames
- Grouping, aggregating, and applying
- Error Handling
- Dealing with syntax errors
- Exceptions
- Handling exceptions with try/except
- Advance
- Regression
- Correlation Matrix
- Linear Regression
- Machine Learning Algorithms
- Model Evaluation: Overfitting & Underfitting
- Importing Datasets
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
- M Cleaning and Preparing the Data
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization Sets
- Binning
- Indicator variables
- Summarizing the Data Frame
- Descriptive Statistics
- Basic of Grouping
- ANOVA
- Correlation
- More on Correlation
- Model Development
- Simple and Multiple Linear Regression
- Model Evaluation Using Visualization
- Polynomial Regression and Pipelines
- R-squared and MSE for In-Sample Evaluation
- Prediction and Decision Making
- Model Evaluation
- Model Evaluation
- Over-fitting, Under-fitting, and Model Selection
- Ridge Regression
- Grid Search
- Model Refinement
- Python Basics
- Your first program
- Types
- Expressions and Variables
- String Operations
- Python Data Structures
- Lists and Tuples
- Sets
- Dictionaries
- Python Programming Fundamentals
- Conditions and Branching
- Loops
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- Working with Data in Python
- Reading files with open
- Writing files with open
- Loading data with Pandas
- Working with and Saving data with Pandas
- Working with Numpy Arrays and Simple APIs
- Numpy 1D Arrays
- Numpy 2D Arrays
- Simple APIs
- API Setup
- Understanding Database Concepts
- Understanding Database Storage
- Entities and Relationships
- The Relational Data Model
- Normalization
- Database Design and Performance Tuning
- Creating Database Objects
- Manipulating Data
- JDBC As The Fundamental Java API
- JPA as the JAVA ORM API
- Database Security
- Understanding Database Backup And Restore
- Introduction To Mysql
- What is Big Data and Data Analytics
- Overview about HDP
- Introduction to Apache Ambari
- Hadoop and the Hadoop Distributed File System (HDFS)
- MapReduce and YARN
- Apache Spark
- Overview on Data File Formats, HBase, Pig, Hive, R and Python
- ZooKeeper, Slider, and Knox
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- DataPlane Service
- Stream Computing
- Module 1 – Supervised vs Unsupervised Learning
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
- Module 2 – Supervised Learning I
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
- Module 3 – Supervised Learning II
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
- Module 4 – Unsupervised Learning
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters – Single Linkage Clustering
- Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
- Density-Based Clustering
- Module 5 – Dimensionality Reduction & Collaborative Filtering
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
