AI and ML Training Course

Master AI and ML training with hands-on projects, earn certification, and launch your career in the AI field. Start your journey today!

Want to build intelligent systems and models like a pro? Our AI and ML training equips you with essential skills in AI and machine learning, guided by expert instructors. Gain hands-on experience with real-world data and boost your career with our comprehensive AI and ML training.

Course Highlights for AI and ML Training

Comprehensive Curriculum
Real-Time Project Work
Expert Developer Mentorship
Training Completion Certification
Up-to-Date Angular Practices
Flexible Learning Duration

Key Highlights

Practical, Hands-On Training Sessions

Real-World Project Applications

Training Aligned with Industry Requirements

Flexible Training Options: Online or On-Site

AI and ML Training Syllabus

Introduction to AI and ML

Overview of AI and ML
AI vs Machine Learning
Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning
Basics of data science and its importance in AI
Practice/Exercise

Python for AI and ML

Python libraries for ML (NumPy, Pandas, Matplotlib, Scikit-learn)
Data preprocessing and cleaning
Data visualization techniques
Statistical analysis for machine learning
Practice/Exercise

Supervised Learning Algorithms

Linear regression and logistic regression
Decision trees and random forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Practice/Exercise

Unsupervised Learning Algorithms

K-Means clustering
Hierarchical clustering
Dimensionality reduction (PCA)
Anomaly detection
Practice/Exercise

Deep Learning and Neural Networks

Introduction to neural networks
Activation functions and backpropagation
Deep neural networks (DNNs) and Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Practice/Exercise

Model Evaluation and Tuning

Cross-validation techniques
Hyperparameter tuning
Evaluating model performance using metrics like accuracy, precision, recall, F1 score
Handling overfitting and underfitting
Practice/Exercise

Natural Language Processing (NLP)

Text processing and tokenization
Sentiment analysis
Text classification models
Word embeddings (Word2Vec, GloVe)
Practice/Exercise

AI and ML in Practice

Real-world use cases in AI and ML (Healthcare, Finance, Retail, etc.)
Implementing models on large datasets
Using cloud platforms for AI/ML (Google Colab, AWS, Azure)
Building and deploying an AI/ML model
Final project

FAQs

What is AI and Machine Learning?

AI refers to the simulation of human intelligence in machines, while machine learning is a subset of AI that uses data and algorithms to make predictions or decisions.

What can I build using AI and ML?

With AI and ML, you can build predictive models, recommendation systems, chatbots, image recognition applications, and much more.

Do I need prior programming experience for AI and ML?

Some basic programming knowledge (especially Python) is recommended, but we cover Python from the ground up in this course.