AI USING PYTHON

AI USING PYTHON

4.5 out of 5 based on 3200 reviews

Join the program and get the opportunity to learn under the guidance of an ai using python specialist.

AI USING PYTHON

Expertise

prof trained

80000+

Professionals Trained
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50+

Industry Expert Trainers
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8 Branches

In NCR
corporate

2500+

Corporate Served

Course Duration

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Certificate

Yes

Live Project

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Training Mode

Classroom / Online

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BEST AI USING PYTHON COURSE in DUCAT

4.8 out of 5 based on 5412 Votes

AI Using Python Overview

Have you ever wondered how machines can learn and make decisions? That's the magic of Artificial Intelligence (AI), and Python is the key to unlocking its potential! An "AI Using Python" training course equips you with the tools to build intelligent systems.

Imagine creating chatbots that answer your questions, or recommending movies you'll love – that's the power you'll gain. These courses are designed for students with no prior AI knowledge. They start with the basics of Python, a beginner-friendly programming language, and then introduce core AI concepts like machine learning.

Here's what you can expect:

  • Learn by Doing: You'll get hands-on experience through projects, like building a program that recognizes handwritten digits or predicts stock prices.
  • Master the Tools: Python libraries like TensorFlow and NumPy become your secret weapons for wrangling data and creating AI models.
  • Unlock Real-World Applications: The course explores how AI is used in fields like healthcare, finance, and entertainment, giving you a glimpse into exciting career possibilities.

By the end, you'll have a solid foundation in AI and Python, making you a valuable asset in the ever-evolving tech landscape. So, if you're ready to embrace the future and create intelligent systems, an "AI Using Python" training course is your perfect launchpad!

Why learn AI using Python?

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Learn The Essential Skills

Learn The Essential Skills

Earn Certificates And Degrees

Earn Certificates And Degrees

Get Ready for The Next Career

Get Ready for The Next Career

Master at Different Areas

Master at Different Areas

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What will you learn?

  • Fundamentals of AI: You'll grasp the foundational concepts of artificial intelligence, including machine learning algorithms, neural networks, and natural language processing, using Python as the primary programming language.
  • Data Manipulation and Preprocessing: Gain proficiency in handling and cleaning diverse datasets, performing feature engineering, and preparing data for AI model training, leveraging Python libraries like Pandas and NumPy.
  • Model Development: Learn to design, implement, and fine-tune AI models such as regression, classification, clustering, and deep learning networks using Python frameworks like TensorFlow, Keras, and Scikit-learn.
  • Evaluation and Optimization: Understand techniques for assessing model performance, optimizing hyperparameters, and mitigating issues like overfitting or underfitting through cross-validation, grid search, and regularization methods
  • Deployment and Integration: Explore strategies for deploying AI models into production environments, creating APIs, and integrating them into software applications, utilizing frameworks like Flask or Django for web deployment and cloud platforms like AWS or Azure.

Skill you will gain

  • Python Programming Proficiency

  • AI Concepts and Fundamentals

  • Machine Learning Algorithms

  • Deep Learning and Neural Networks

Explore Modules of this course

Module 1: Introduction to AI and Python

Understanding the basics of Artificial Intelligence and its applications. Setting up the Python environment for AI development.

Module 2: Python Programming Fundamentals

Learning the basics of Python programming, including data types, loops, and functions.

Module 3: Machine Learning Basics

Introduction to machine learning concepts, algorithms, and applications. Overview of supervised and unsupervised learning.

Module 4: Regression and Classification

Understanding and implementing regression and classification algorithms. Practical applications of predictive modeling.

Module 5: Neural Networks and Deep Learning

Basics of neural networks, activation functions, and backpropagation. Building and training deep learning models using libraries like TensorFlow or PyTorch.

Module 6: Natural Language Processing (NLP)

Processing and analyzing text data using techniques like tokenization and sentiment analysis. Building NLP models for tasks like text classification and named entity recognition.

Module 7: Computer Vision

Working with visual data, including image recognition and object detection. Implementing computer vision algorithms and models.

Module 8: Model Evaluation and Optimization

Techniques for evaluating and fine-tuning machine learning and deep learning models. Hyperparameter tuning for improved model performance.

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