AI & Machine Learning Training

Live Online & Classroom Enterprise Training

AI & Machine Learning provides a comprehensive foundation in algorithms, models, and techniques used to build intelligent systems that learn from data and make informed decisions.

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What is AI & Machine Learning Course about?

This course offers a comprehensive introduction to the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). You will learn how AI systems think, learn, and make decisions, and how machine learning algorithms are trained using real-world data. The course focuses on supervised, unsupervised, and reinforcement learning techniques, along with practical implementation using popular libraries and tools. It’s designed to provide both theoretical knowledge and hands-on experience.

What are the objectives of AI & Machine Learning Course ?

Understand the fundamentals of AI and its real-world use cases
Differentiate between AI, ML, and Deep Learning
Build and evaluate machine learning models (classification, regression, clustering)
Apply supervised and unsupervised learning techniques
Use Python libraries like Scikit-learn, Pandas, NumPy, and Matplotlib
Get introduced to neural networks and deep learning basics
Handle model training, testing, and performance evaluation
Understand ethical considerations in AI deployment

Who is AI & Machine Learning Course for?

Aspiring Data Scientists and AI Engineers
Software Developers and Analysts
Product Managers and Tech Enthusiasts
Students and Fresh Graduates in Computer Science
Business Professionals exploring AI-driven insights

What are the prerequisites for AI & Machine Learning Course?

Basic knowledge of mathematics (algebra, probability, statistics)
Familiarity with Python programming
Curiosity about data and decision-making processes AI & Machine Learning
No prior AI/ML experience required

Available Training Modes

Live Online Training

3 Days

Self-Paced Training

35 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to Artificial Intelligence

What is AI?

History and evolution of AI

AI vs. Machine Learning vs. Deep Learning

AI in business, healthcare, finance, and more

Module 2: Fundamentals of Machine Learning

Types of ML: Supervised, Unsupervised, Reinforcement

Data preprocessing and cleaning

Splitting datasets and model evaluation metrics

Module 3: Supervised Learning

Linear and logistic regression

Decision trees and random forests

Support Vector Machines (SVM)

Model accuracy, precision, recall, and confusion matrix

Module 4: Unsupervised Learning

K-means clustering

Hierarchical clustering

Principal Component Analysis (PCA)

Module 5: Introduction to Deep Learning

What is a neural network?

Layers, neurons, weights, and activation functions

Overview of TensorFlow and Keras frameworks

Module 6: Building ML Models with Python

Hands-on ML projects using Scikit-learn

Jupyter notebooks for experimentation

Data visualization with Matplotlib and Seaborn

Module 7: Ethics and Challenges in AI

Bias in AI models

Privacy and security concerns

AI in decision-making and its societal impact

Module 8: Capstone Project

Apply all learned techniques to a real-world dataset

Build, evaluate, and present your ML model

Optional: deploy using Flask or Streamlit

Who is the instructor for this training?

The trainer for this AI & Machine Learning Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Reviews

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Sony

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Stryker

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Heena Rajan

Mindtree