Artificial Intelligence and Machine Learning Training – Build Smart Systems with Advanced Algorithms

Santosh Pathai

Last Update December 12, 2024

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About This Course

The Artificial Intelligence and Machine Learning Training is a comprehensive course that explores the fundamentals of artificial intelligence and machine learning, focusing on practical applications and real-world problem-solving. Participants will gain hands-on experience building intelligent systems, implementing machine learning algorithms, and deploying AI models. From foundational concepts to advanced techniques, this course is designed to prepare learners for a successful career in AI and ML.

Learning Objectives

Understand the principles of artificial intelligence and machine learning.

Build predictive models using supervised and unsupervised learning techniques.

Explore neural networks and deep learning frameworks.

Solve real-world problems with AI tools and techniques.

Implement and deploy AI models using Python and TensorFlow/PyTorch.

Develop projects involving natural language processing (NLP) and computer vision.

Requirements

Basic knowledge of Python programming.
Familiarity with linear algebra, probability, and statistics.
Access to a computer with Python and AI-related libraries installed (guidance provided).

Target Audience

Aspiring AI and ML engineers.
Data scientists looking to expand their skill set.
Developers interested in building intelligent systems.
Professionals transitioning into AI and machine learning roles.

Curriculum

44 Lessons40h

Module 1: Introduction to Artificial Intelligence and Machine Learning

What is Artificial Intelligence?

Overview of Machine Learning and Its Applications

Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

AI and ML Tools and Frameworks

Lab: Setting Up Python Environment for AI and ML

Lab: Running Your First ML Program

Module 2: Data Preprocessing and Feature Engineering

Module 3: Supervised Learning

Module 4: Unsupervised Learning

Module 5: Introduction to Deep Learning

Module 6: Advanced AI Techniques

Module 7: AI Model Deployment

Your Instructors

Santosh Pathai

4.93/5

9 Courses

28 Reviews

0 Students

₹24,500.00

Level

Intermediate

Duration 40 hours

Lectures

44 lectures

Subject

Artificial Intelligence

Artificial Intelligence and Machine Learning Training – Build Smart Systems with Advanced Algorithms

About This Course

Tags

Learning Objectives

Requirements

Target Audience

Curriculum

Module 1: Introduction to Artificial Intelligence and Machine Learning

What is Artificial Intelligence?

Overview of Machine Learning and Its Applications

Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

AI and ML Tools and Frameworks

Lab: Setting Up Python Environment for AI and ML

Lab: Running Your First ML Program

Module 2: Data Preprocessing and Feature Engineering

Understanding Data for Machine Learning

Handling Missing Values and Outliers

Feature Scaling and Encoding

Dimensionality Reduction Techniques

Lab: Data Cleaning and Transformation with Pandas

Lab: Feature Selection Using Correlation

Lab: Applying PCA for Dimensionality Reduction

Module 3: Supervised Learning

Linear Regression and Logistic Regression

Decision Trees and Random Forests

Support Vector Machines (SVM)

Model Evaluation Metrics

Lab: Implementing Linear and Logistic Regression Models

Lab: Building and Evaluating Decision Tree Models

Lab: Hyperparameter Tuning with Grid Search

Module 4: Unsupervised Learning

Clustering Algorithms: K-Means, DBSCAN

Dimensionality Reduction with T-SNE and PCA

Anomaly Detection

Lab: Performing K-Means Clustering on Real Datasets

Lab: Visualizing High-Dimensional Data with T-SNE

Lab: Detecting Anomalies in Transaction Data

Module 5: Introduction to Deep Learning

Basics of Neural Networks

Activation Functions and Optimizers

Building Deep Learning Models with TensorFlow/Keras

Overfitting and Regularization

Lab: Implementing a Neural Network for Image Classification

Lab: Using Dropout and L2 Regularization to Prevent Overfitting

Module 6: Advanced AI Techniques

Natural Language Processing (NLP)

Computer Vision with Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs) and LSTMs for Time Series Analysis

Reinforcement Learning Basics

Lab: Building a Sentiment Analysis Model Using NLP

Lab: Developing a CNN for Object Detection

Lab: Time Series Forecasting with RNNs

Module 7: AI Model Deployment

Introduction to Model Deployment

Deploying Models with Flask and FastAPI

Containerizing AI Models Using Docker

Lab: Creating an API for a Machine Learning Model

Lab: Deploying AI Models on Cloud Platforms

Your Instructors

Santosh Pathai

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