Self Paced

Artificial Intelligence

By Learnnex Categories: Self Paced

About Course

Introducing our Artificial Intelligence Program: Unlock the Power of AI

Dive into the world of Artificial Intelligence (AI) with our comprehensive program designed to equip you with the skills and knowledge needed to excel in this cutting-edge field. Over the course of eight weeks, you’ll delve deep into AI foundations, data preparation, deep learning techniques, and advanced applications, all while gaining hands-on experience through practical projects and coding exercises.

In the first part of the program, you’ll lay the groundwork by exploring the fundamentals of AI and data exploration. Learn how to analyze and visualize data using Python libraries, and master essential data preprocessing techniques to ensure your data is clean and ready for analysis.

Next, immerse yourself in the world of neural networks and machine learning, where you’ll gain a solid understanding of the backpropagation algorithm and build your first neural network from scratch. Then, advance to convolutional neural networks (CNNs) and discover their applications in image classification and semantic segmentation.

As you progress through the program, you’ll also delve into natural language processing (NLP), where you’ll learn how to process and analyze text data using techniques like tokenization and word embeddings. Explore advanced NLP techniques such as sentiment analysis and machine translation, and harness the power of sequence-to-sequence models to tackle real-world NLP tasks.

In the final weeks of the program, you’ll put your newfound knowledge into practice by working on a hands-on project of your choice. From data collection and preparation to model training and evaluation, you’ll have the opportunity to showcase your skills and creativity as you develop and present your own AI project.

By enrolling in our AI program, you’ll not only gain valuable technical skills but also position yourself at the forefront of one of the most exciting and rapidly evolving fields in technology. Whether you’re looking to kickstart a career in AI or enhance your existing skill set, our program provides the perfect opportunity to take your knowledge and expertise to the next level.

Don’t miss out on this chance to unlock the potential of AI and propel your career forward. Enroll today and join us on a journey of discovery and innovation in the world of Artificial Intelligence.

Additional Notes:

Throughout the course, emphasize hands-on coding exercises, projects, and discussions.
Provide opportunities for students to explore different AI tools and libraries (e.g., TensorFlow, PyTorch).

Introduction to Artificial Intelligence
Problem-Solving and Search Algorithms
Knowledge Representation and Reasoning
Machine Learning Fundamentals
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Reinforcement Learning
Deep Learning and Neural Networks
Natural Language Processing (NLP)
Computer Vision
AI Ethics and Bias
AI Applications and Case Studies
Future Trends in Artificial Intelligence

Artificial Intelligence

About Course

What Will You Learn?

Course Content

Part I – AI Foundations and Data Preparation

Introduction & Data Exploration

Overview of Artificial Intelligence and its applications

Introduction to data in AI: types, importance, and real-world examples

Hands-on: Exploring and visualizing data using Python libraries (e.g., Matplotlib, Seaborn)

Introduction to data preprocessing techniques (cleaning, handling missing values)

Machine Learning & Neural Networks

Introduction to the Machine Learning landscape and its role in AI

Demystifying Neural Networks: Perceptrons, basic architecture

Understanding the Backpropagation algorithm for learning in neural networks

Hands-on: Implementing a simple neural network in Python

Part II – Deep Learning Techniques

Convolutional Neural Networks (CNNs) I

Introduction to CNN architecture and its advantages for image data

Understanding Convolutional layers: Forward and backward propagation

Exploring Pooling and Padding operations for efficient image processing

Introduction to Data Augmentation for improving model performance

Hands-on: Building a CNN for image classification

CNNs II & Advanced Techniques

Leveraging Transfer Learning in CNNs to accelerate development

Exploring applications of CNNs beyond image classification: Semantic Segmentation (pixel-level labeling)

Hands-on: Implementing a CNN for semantic segmentation

Introduction to Natural Language Processing (NLP)

Demystifying NLP: its applications and challenges in working with text data

Text Preprocessing techniques: Tokenization, Stemming, Lemmatization

Understanding Word Embeddings: Word2Vec, GloVe (representing words as vectors)

Hands-on: Implementing text preprocessing techniques in Python

Part III – Advanced Applications and Project Development

Advanced NLP Techniques

Utilizing Transfer Learning in NLP for faster development

Machine Translation using sequence-to-sequence models

Analyzing sentiment and emotions from text data (Sentiment Analysis)

Summarizing text data using Long Short-Term Memory (LSTM) networks

Hands-on: Implementing NLP algorithms for specific tasks

Practical Project Development

Selecting and defining a real-world AI project (using concepts learned)

Data collection and preparation specific to the chosen project

Building and training an AI model for the chosen application

Evaluating model performance and fine-tuning for optimal results

Project Presentation & Review

Presenting the completed AI project to classmates and instructors

Receiving feedback and suggestions for improvement on projects and code

Comprehensive review of key concepts and techniques covered throughout the course

Discussing future learning pathways and career opportunities in AI

Project

Image Recognition using CNNs: Implement a CNN model to classify images into different categories

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