Become an Expert in AI and Machine Learning

Learn the fundamentals and advanced techniques of Artificial Intelligence and Machine Learning. Master algorithms, model building, data preprocessing, and deployment to solve real-world problems with AI-powered solutions.

AI & ML Course

Description

Comprehensive AI & ML Training

Urbancode's AI & ML Training Course is designed to provide participants with a deep understanding of artificial intelligence and machine learning principles. This course covers both foundational and advanced topics, emphasizing practical skills and real-world applications. From data preprocessing and model building to deployment and optimization, participants will gain hands-on experience and learn to develop intelligent systems capable of solving complex problems.

Why should you choose this course?

Career Opportunities
AI & ML professionals are in high demand across various industries, offering opportunities in tech companies, research institutions, and large enterprises. Mastery of AI & ML opens doors to diverse career paths and lucrative job prospects.

Hands-On Experience
The course focuses on practical application, providing real-world projects and exercises that simulate professional development environments. Participants will build a portfolio of projects to showcase their skills to potential employers.

Comprehensive Curriculum
Covering essential technologies and tools such as Python, TensorFlow, Keras, scikit-learn, and more, the course ensures a thorough understanding of AI & ML frameworks and methodologies.

Expert Guidance
Learn from industry experts who provide mentorship and guidance throughout the course, helping participants develop critical problem-solving and analytical skills.

Flexible Learning Options
Choose between flexible online and classroom training formats, enabling participants to learn at their own pace and balance their studies with professional or personal commitments.

This course includes

  • 60hrs Instructor-Led Training
  • Practice & Exercises for AI | ML | Data Science
  • Mentor Support and Code Review

Course Content

Module 1: Introduction to AI & ML

Foundations of AI

  • Overview of Artificial Intelligence
  • History and evolution of AI

Machine Learning Basics

  • Introduction to Machine Learning concepts
  • Supervised and unsupervised learning

Python for AI & ML

  • Python programming basics
  • Libraries for AI & ML (NumPy, pandas, scikit-learn)
Module 2: Data Preprocessing and Exploration

Data Cleaning and Preparation

  • Handling missing values
  • Data normalization and standardization

Exploratory Data Analysis (EDA)

  • Visualizing data distributions
  • Identifying patterns and correlations

Feature Engineering

  • Creating new features from existing data
  • Feature selection techniques
Module 3: Machine Learning Algorithms

Supervised Learning

  • Linear and logistic regression
  • Decision trees and random forests

Unsupervised Learning

  • K-means clustering
  • Principal component analysis (PCA)

Deep Learning

  • Neural networks and backpropagation
  • Introduction to TensorFlow and Keras
Module 4: Advanced Topics in AI & ML

Natural Language Processing (NLP)

  • Text preprocessing and feature extraction
  • Sentiment analysis and text classification

Computer Vision

  • Image processing techniques
  • Object detection and recognition

Reinforcement Learning

  • Introduction to reinforcement learning
  • Implementing Q-learning algorithms

AI & ML Deployment

  • Deploying models with Flask and Docker
  • Monitoring and maintaining AI systems
learn-ai-ml

AI & ML

Unlock the Power of Artificial Intelligence and Machine Learning

Embark on an exciting journey into the world of Artificial Intelligence and Machine Learning with Urbancode's extensive training program. Designed by leading industry professionals, this course offers in-depth knowledge and practical skills in AI & ML. From understanding fundamental concepts to mastering advanced algorithms, participants will learn to build intelligent systems and apply machine learning techniques to solve real-world problems.


Fundamentals of AI

Introduction to AI | History of AI | Basics of Machine Learning

Machine Learning

Supervised Learning | Unsupervised Learning | Neural Networks

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