Artificial Intelligent course

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Data Science / Analytics

Artificial Intelligent

Couse Completed

105 Data Science

Upcomming Batch

15 Sept, 2024

Review

About Course

This Artificial Intelligence (AI) course provides a comprehensive introduction to the concepts and applications of AI, combining fundamental programming skills with advanced techniques in machine learning, deep learning, natural language processing, and reinforcement learning. The course is designed to equip learners with practical skills and theoretical knowledge required to build and deploy AI solutions. Participants will explore Python programming basics, fundamental AI algorithms, and cutting-edge technologies, with opportunities to work on real-world projects and case studies.

Course Objectives

Understand Core AI Concepts: Gain a solid understanding of the fundamental principles of AI, including its history, applications, and ethical considerations.
Master Python Programming: Develop proficiency in Python programming, focusing on syntax, data structures, and libraries essential for AI and data science.
Learn Machine Learning Fundamentals: Acquire knowledge in supervised and unsupervised learning, including key algorithms, evaluation metrics, and model optimization techniques
Explore Deep Learning Techniques: Study neural networks, convolutional networks, and recurrent networks, and understand how to implement and train deep learning models.
Apply Natural Language Processing: Learn techniques for processing and analyzing text data, including text representation, sentiment analysis, and modern NLP models.
Understand Reinforcement Learning: Explore the principles of reinforcement learning, including algorithms and applications for autonomous decision-making and optimization.
Implement AI Solutions: Gain experience in deploying AI models, integrating them into applications, and utilizing cloud services and platforms for scalable AI solutions
Address Advanced Topics and Future Trends: Investigate advanced AI topics such as explainable AI, ethical considerations, and emerging trends in the field.
Develop Practical Skills Through Projects: Apply theoretical knowledge to real-world projects, including end-to-end AI solutions, case studies, and industry applications

Course Curriculum

Overview of AI and its Applications

History and Evolution of AI

AI vs Machine Learning vs Deep Learning

Ethical Considerations and AI Governance

Tools and Technologies for AI Development

Introduction to Python Programming

Basic Syntax and Data Structures (Lists, Tuples, Dictionaries)

Control Structures: Conditional Statements and Loops

Functions and Modules in Python

Introduction to Python Libraries for Data Science: NumPy, Pandas

Introduction to Machine Learning

Supervised Learning: Regression and Classification

Unsupervised Learning: Clustering and Dimensionality Reduction

Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

Model Selection and Hyperparameter Tuning

Introduction to Neural Networks

Fundamentals of Deep Learning: Perceptrons and Multi-layer Perceptrons

Convolutional Neural Networks (CNNs) for Image Processing

Recurrent Neural Networks (RNNs) and LSTM for Sequence Data

Transfer Learning and Pre-trained Models

Introduction to NLP and Text Processing

Text Representation: Bag of Words, TF-IDF, Word Embeddings

Named Entity Recognition and Sentiment Analysis

Sequence-to-Sequence Models and Attention Mechanisms

Modern NLP Models: Transformers and BERT

Introduction to Reinforcement Learning (RL)

Key Concepts: Agents, Environments, Rewards

Basic RL Algorithms: Q-Learning and SARSA

Policy Gradient Methods and Deep RL

Applications of RL: Game Playing and Robotics

AI Model Deployment and Serving

Integrating AI Models into Applications

Introduction to AI Platforms and Cloud Services (e.g., AWS, Azure, Google Cloud)

AI Model Monitoring and Maintenance

Case Studies of AI in Industry: Healthcare, Finance, and Robotics

Explainable AI (XAI) and Interpretability

AI for Big Data: Handling Large-Scale Data

Ethical AI and Bias Mitigation

Future Trends in AI: AGI and Beyond

Project: Developing an End-to-End AI Solution

Ratings & Reviews

4.5

Rated 4 out of 1 Rating

5 star

82%

4 star

30%

3 star

15%

2 star

6%

1 star

10%

Featured review

Selvi

2 weeks ago

The AI course provided a deep dive into machine learning and neural networks. The hands-on projects and real-world examples helped solidify my understanding of AI concepts.

Helpful?

Raji

2 weeks ago

This course balanced theoretical foundations with practical implementation. I appreciated the detailed explanations and coding exercises that brought AI algorithms to life.

Helpful?

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