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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.
- Python Fundamentals
- Python Data Types & Resources
- Library Reference for Python's Many Standard Libraries
- Language Reference for Python's Syntax.
- Set Python Virtual Environment Setup
- 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
- Working With Large Databases and Tables Dataset
- 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.
- 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 & How To Implement It With Python.
- 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
- 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
- Advanced Techniques Like Natural Language Processing and Reinforcement Learning.
- Data Set Using Evaluation Metrics & Clustering Technique
- Reinforcement Learning, Supervised Learning, And Unsupervised Learning,
- Automating the Learning Process Without Any Human Assistance.
- Machine Learning Algorithms and When to Use Them
- Classification Techniques - Decision Trees & Regression Trees
- Multiclass Prediction, Support Vector Machines & Logistic Regression
- Making Predictions Based on Historical Data or Information,
- Speech Recognition, Email Filtering, Auto-Tagging
- 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
- Create Interactive Business Intelligence Dashboards
- Connect, Visualize, and Share Data
- Create & Distribute an Interactive & Shareable Dashboard
- Transform raw data into interactive dashboards & visualizations
- Well-designed graphics & properties
- Real-time data analysis
- Query translation into visualization
- Data Blending & Data Collaboration
- Fundamentals of Machine Learning Using Python’s Powerful Libraries
- Describe the various types of Machine Learning algorithms & Uses
- Understand and identify patterns
- Supervised & unsupervised Learning
- Make predictions, and make decisions based on data.
- Explore advanced data science challenges through sample data sets
- Examine machine learning results and recognize under-fitting or
- over-fitting data.
- 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.
- 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.
- Projects Include:
- Data Scrubbing/Cleaning
- Exploratory Data Analysis
- Interactive Data Visualization
- Cluster the Information in A Given Dataset
- Data Models To Wave Artificial Intelligence And Machine Learning.
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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.
Cybersecurity: Ethical Hacking, Network Security.
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Database Management: SQL, PL/SQL, MongoDB, Oracle.
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