Learn AI with Python
Enroll in our Python AI course and build intelligent applications that interact with the world around you.
In this course, dive into AI programming with Python, from setting up your AI environment to implementing neural networks, NLP, and speech recognition. Master machine learning (ML) algorithms for classification, regression, and clustering. Then apply them to real-world problems.
Learn to construct AI agents, automate tasks with logic programming, and even build object recognition models using Convolutional Neural Networks (CNNs).
What You`ll Learn on This Course
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- Build & Deploy AI Models: Create machine learning and deep learning applications using Python.
- ML Algorithms: Implement classification, regression, and clustering for real-world problem-solving.
- Neural Networks Development: Design and train ANNs (Artificial Neural Networks) and CNNs for image recognition.
- NLP Automation: Process and analyze text using Natural Language Processing (NLTK) techniques like tokenization and sentiment analysis.
- Speech Recognition: Build AI systems that understand and respond to spoken language.
- Reinforcement Learning: Develop intelligent agents that learn from environments using Python.
- AI Solutions Optimization: Fine-tune models with performance metrics, regularization, and advanced architectures like YOLO and R-CNN.
This course comes with:
Curriculum
- 2 Sections
- 0 Lessons
- 26 Weeks
Expand all sectionsCollapse all sections
- Lesson Plan
1. Preface
- Preface
2. Introduction to AI and Python
- Introduction
- Introduction to Artificial Intelligence (AI)
- Why to Learn AI?
- Understanding Intelligence
- Various Fields of Study in AI
- Applications of AI in Various Industries
- How Does Artificial Intelligence Learn?
- AI Agents and Environments
- AI and Python – How Do They Relate?
- Python3 – Installation and Setup
- Conclusion
- Questions
3. Machine Learning and Its Algorithms
- Introduction
- Understanding Machine Learning (ML)
- The Landscape of Machine Learning Algorithms
- Components of a Machine Learning Algorithm
- Different Learning Styles in Machine Learning Algorithms
- Popular Machine Learning Algorithms
- Questions
4. Classification and Regression Using Supervised Learning
- Introduction
- Classification
- Various Steps to Build a Classifier Using Python
- Lazy Learning versus Eager Learning
- Performance Metrics for Classification
- Regression
- Various Steps to Build a Regressor Using Python
- Performance Metrics for Regression
- Conclusion
- Questions
5. Clustering Using Unsupervised Learning
- Introduction
- Clustering
- Various Methods to Form Clusters
- Important ML Clustering Algorithms
- Conclusion
- Questions
6. Solving Problems with Logic Programming
- Introduction
- Logic Programming
- Building Blocks of Logic Programming
- Useful Python Packages for Logic Programming
- Implementation Examples
- Conclusion
- Questions
7. Natural Language Processing with Python
- Introduction
- Natural Language Processing (NLP)
- Installing Python's NLTK Package
- Understanding Tokenization, Stemming, and Lemmatization
- Understanding Chunking
- Understanding Bag-of-Words (BoW) Model
- Understanding Stop Words
- Understanding Vectorization and Transformers
- Some Examples
- Conclusion
8. Implementing Speech Recognition with Python
- Introduction
- Basics of Speech Recognition
- Building a Speech Recognizer
- Conclusion
- Questions
9. Implementing Artificial Neural Network (ANN) with Python
- Introduction
- Understanding of Artificial Neural Network (ANN)
- Optimizers for Training the Neural Network
- Regularization
- Installing Useful Python Packages for ANN
- Examples of Building Some Neural Networks
- Conclusion
- Questions
10. Implementing Reinforcement Learning with Python
- Understanding Reinforcement Learning
- Markov Decision Process (MDP)
- Building Blocks of Reinforcement Learning
- Constructing an Environment Using Python
- Constructing an Agent Using Python
- Conclusion
- Questions
11. Implementing Deep Learning and Convolutional Neural Network
- Introduction
- Understanding Deep Learning
- Elucidation of Convolutional Neural Networks
- The Architecture of Convolutional Neural Network
- Localization and Object Recognition with Deep Learning
- Image Classification Using CNN in Python
- Conclusion
- Questions
0 - Hands-on LAB Activities
Curriculum may vary
Module 1: Introduction to Artificial Intelligence
- What is Artificial Intelligence?
- History and evolution of AI
- AI vs Human Intelligence
- Real-world applications of AI
- AI agents and environments
Module 2: Python Foundations for AI
- Why Python for AI?
- Python basics (syntax, data types, control structures)
- Working with functions and modules
- Introduction to Jupyter Notebooks
- Installing and managing Python packages
Module 3: Data Handling and Analysis with Python
- Introduction to NumPy
- Data manipulation with pandas
- Data cleaning and preprocessing
- Basic data visualization concepts
Module 4: Introduction to Machine Learning
- What is Machine Learning?
- Types of machine learning (Supervised, Unsupervised, Reinforcement)
- ML workflow and components
- Introduction to scikit-learn
Module 5: Supervised Learning – Classification and Regression
- Understanding classification problems
- Popular classification algorithms
- Understanding regression problems
- Linear and polynomial regression
- Model evaluation and performance metrics
Module 6: Unsupervised Learning – Clustering
- What is clustering?
- Clustering use cases
- K-means clustering
- Hierarchical clustering
- Evaluating clustering results
Module 7: Introduction to Deep Learning
- Limitations of traditional ML
- What is Deep Learning?
- Biological inspiration of neural networks
- Introduction to TensorFlow and PyTorch
Module 8: Artificial Neural Networks (ANN)
- Structure of neural networks
- Activation functions
- Loss functions and optimizers
- Training and evaluating neural networks
- Overfitting and regularization
Module 9: Natural Language Processing with Python
- Introduction to NLP
- Text preprocessing techniques
- Tokenization, stemming, and lemmatization
- Bag-of-Words and vectorization
- Using NLTK for NLP tasks
Module 10: Speech Recognition with Python
- Basics of speech recognition
- Speech-to-text concepts
- Building a simple speech recognizer
- Applications of speech recognition
Module 11: Reinforcement Learning Fundamentals
- What is Reinforcement Learning?
- Agents, environments, and rewards
- Markov Decision Process (MDP)
- Simple reinforcement learning examples
Module 12: Convolutional Neural Networks (CNN)
- Introduction to CNNs
- Convolution and pooling layers
- CNN architectures
- Image classification with CNNs
Module 13: Capstone Projects and Hands-on Practice
- Building an ML model end-to-end
- Image classification project
- Text classification or NLP project
- Model evaluation and optimization
Conclusion
- Future trends in AI and Deep Learning
- Ethical considerations in AI
- Career paths in AI and ML
Points to Remember
- AI is the umbrella field
- ML enables learning from data
- Deep Learning handles complex, large-scale problems
- Python is the core language for AI development
Questions
- What differentiates AI, ML, and Deep Learning?
- Why is Python preferred for AI development?
- Which problems are best suited for Deep Learning?
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