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The AI & Machine Learning course at Insta InfoTech® provides structured learning in AI technologies.
Track your progress with guided lessons, real-world applications, and advanced AI concepts.
Learn how to train, optimize, and deploy machine learning models.
Stay updated with the latest trends in AI, automation, and data-driven decision-making.
Perfect for AI enthusiasts, data scientists, and tech professionals.
Data Science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.
Data Science is usually the person who can do basic descriptive statistics, turns unstructured data into Structured Data, Reporting for Data Visualization With Data Analysis Process and communicate data points for conclusions.
Data Science must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data & get a clear idea of what to do & using that data to make critical ( more importantly, correct) excisions. Data analytics can be referred to as the necessary level of data science.
- Notation for writing computer programs
- Types of computer languages
- Cutting-edge coding skills.
- Ways of generating machine code,
- Set of instructions to generate logics
- machine-dependent (0s and 1s) programming language
- compiler or interpreter to translate the program into machine language
- Using a programming editor like IDE & VS etc.
- Object-Oriented Languages Importance
- Class, Objects & Library Files in C++
- Constructor & Destructor Concepts
- Inheritance Concepts
- Polymorphism Concepts
- Abstraction Concepts
- Encapsulation Concepts
- Data Binding & Integrations Concepts
- Input-Output Operations
- File Handling Concepts
- Python Fundamentals
- Python Data Types & Resources
- Library Reference for Python's Many Standard Libraries
- Language Reference for Python's Syntax.
- Input Output Operations
- Functions & Modules in Python
- Exceptions Handling
- Advanced Oops Concepts in Python with Various Data types
- Working with Set, List, Dict. , Tuple Data, Interfaces in Python
- Packages & Library with Pip
- Python Database Connectivity Configurations
- Data Management & Manipulations (Storage, Fetch, Retrieve, Maintain)
- Implementation Of All Concept In Project Preparations
- Powerful Graphical User Interface Toolkit for Python
- Drag-and-Drop GUI builder, with built-in hosting for Our Apps.
- Tk GUI toolkit shipped with Python.
- Specific Geometry Management methods
- Apply event triggers to the widgets.
- Create Admin. & User Panel to Respond to user interactions.
- PDBC - Python Database Connection Configuration with SQL.
- MySQL Functions
- MySQL - Import and Export data
- Organizes Data Structure into Data Tables Services
- Manage & Manipulate Database
- Database operations -create/manage/ access & search
- Updates, join, clause
- Retrieve Data information
- Introduction to SQL SERVER
- Storing and Retrieving Databases
- Sql Optimization and CRUD operations
- Data Import and Export from different sources
- SQL Operators, Join, Clause
- Deep Dive into SQL Functions
- Working with Subqueries, Views and Stored Procedure
- Linear Algebra Concepts.
- Matrix Operations Like Addition, Subtraction, Multiplication & Inversion
- Linear Models To Solve Real-world Problems
- Linear Regression to predict prices based on historical data.
- Linear Algebra For Data Science & Understand How To Implement It With Python.
- NumPy - Numerical Python Techniques .
- Single & Multi-Dimensional Arrays in NumPy
- NumPy Vectorization, Matrices, Indexing, and Slicing Concepts
- NumPy Mathematical, Arithmetic, Statistics Functions
- Random number generators in NumPy
- Linear Algebra & array computing
- Computing diff. Attributes of NumPy arrays.
- Use open source data analysis and manipulation tools
- Cleaning and Plotting data
- Working with data sets with Easy handling of missing data
- Flexible Merging, Joining, Reshaping and Pivoting of data sets
- Functions for analyzing, cleaning, exploring, and manipulating data.
- Powerful data structures for data analysis
- Time Series and Delta Attributes
- Slicing, Indexing & Sub- Setting of large data sets
- Time series-specific functionality
- Date Range Generation
- Frequency conversion
- Pandas Data Frame
- Introduction of Graphical Representation of Information
- Understand Visual Elements Like Charts, Graphs, And Maps,
- Discuss Data visualization Tools in Python
- Understand Trends, Outliers, and Patterns In Data.
- Quickly Identify Patterns, Trends & Correlations Within Data.
- Library For Creating Static, Animated & Interactive Visualizations
- Understand Trends, And Patterns, And To Make Correlations.
- Create Plot By Connecting The Points Defined By X & Y Values.
- Create Publication Quality Plots.
- Matplotlib Utilities Under The “pyplot” Submodule
- Interactive Format Environments Across Platforms
- Customize Visual Style And Layout.
- Creating static, animated, and interactive visualizations.
- Generate A Wide Range Of Plots, Including Line Plots, Scatter Plots, Bar Plots, Histograms, Pie Charts, And More.
- Functions For Optimization, Stats And Signal Processing.
- Algorithms For Optimization, Integration, Interpolation Queries
- Algebraic Equations, Differential Equations, Statistics
- Extends Numpy Providing Additional Tools
- Array Computing And Provides Specialized Data Structures
- Numerical Integration
- Image Manipulation & Optimization
- Signal Processing & Fast Transforms
- Including Object Detection, Face Recognition, And Tracking
- Computer vision, machine learning, and image processing
- Process images and videos to identify objects, faces of a human.
- GUI Features in OpenCV
- Techniques to work with videos like object tracking & Camera Calibration
- Object Detection (obj-detect module) techniques like face detection
- OpenCV Python binding.
- Fundamentals of Machine Learning Using Python’s Powerful Libraries
- Describe the various types of Machine Learning algorithms and when to use them
- Understand and identify patterns, make predictions, and make decisions based on data.
- Explore advanced data science challenges through sample data set
- Examine machine learning results and recognize under-fitting or over-fitting data.
- Multiple Apis (application Programming Interfaces).
- Create Deep Learning Models
- Using Wrapper Libraries To Processing Tensorflow Nodes
- Named Abstract Computation Input Output Attributes
- Tasks Focus On Training And Inference Of Deep Neural Networks
- Create Machine Learning Models For Desktop, Mobile, Web Cloud
- Ai Model- Uses The Flatten, Dense, And Dropout Layers.
- Tensor Edges Graph Representing For Multi-dimensional Data Arrays
- Linear Regression With Tensorflow
- Cloud-based service for software development and version control using Git, allowing developers to store and manage their code
- Storing, tracking, and collaborating on open-source projects.
- Data Scrubbing/Cleaning
- Exploratory Data Analysis
- Interactive Data Visualization
- Interactive Data Visualization
- Data Models To Wave Artificial Intelligence And Machine Learning
- Share the job description
- Practice mock interviews
- Analyze your performance
- Organize your documents - digital copy of CV, Update your social media profile, Showcase student's Accomplishments
- Preparation Tips for a phone interview video interview
- Interview rounds Preparations : HR Interview, Technical Interview, Managerial Interview.
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At Insta Infotech, we specialize in a wide array of courses across domains like:
Programming Languages: Java, Python, C++, .NET, and more.
Data Science & Analytics: Machine Learning, AI, Big Data.
Cloud Computing & DevOps: AWS, Azure, Docker, Kubernetes.
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