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Python for AI
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Course Overview
Program Syllabus
Module 1
About This Course
Python Language – Use in AI is an 8-week course that explores Python’s pivotal role in the development of AI technologies. Intended for M.Tech, M.Sc, and MCA students, as well as professionals in various tech industries, the course offers in-depth instruction on Python coding, data handling, machine learning, deep learning, and natural language processing.
Module 2
Aim
This course aims to provide participants with a comprehensive understanding of Python as a foundational tool in artificial intelligence, equipping them with the skills to implement Python-based AI solutions effectively.
Module 3
Program Objectives
- Mastery of Python for AI: Deep understanding of Python as a critical tool in AI development.
- Practical Application Skills: Proficiency in applying Python to various AI domains, including machine learning and NLP.
- Innovative Problem Solving: Capability to tackle real-world problems with Python-driven AI solutions.
Module 4
Module 1: Overview of Pythonโs Role in AI
- Chapter 1: Introduction to Python for AI
- Lesson 1.1: Pythonโs Popularity and Role in AI
- Lesson 1.2: Key Benefits of Python in AI Projects
- Lesson 1.3: Why Python is Preferred for AI Development
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Python for AI
Professional Certification Program
๐ฅ
FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
Module 5
Module 2: Python Fundamentals for AI
- Chapter 2: Python Programming Essentials
- Lesson 2.1: Variables, Data Types, and Basic Operations
- Lesson 2.2: Control Flow: Conditional Statements and Loops
- Lesson 2.3: Functions and Modular Programming
- Lesson 2.4: Error Handling and Debugging in Python
- Chapter 3: Data Structures and Algorithms in Python
- Lesson 3.1: Overview of Lists, Tuples, and Dictionaries
- Lesson 3.2: Sets and their Use Cases in AI
- Lesson 3.3: Introduction to AI-Related Algorithms
- Lesson 3.4: Implementing Basic Search and Sort Algorithms in Python
Module 6
Module 3: Data Handling for AI
- Chapter 4: Working with NumPy for Numerical Computations
- Lesson 4.1: Introduction to NumPy Arrays
- Lesson 4.2: Array Manipulation and Broadcasting
- Lesson 4.3: Numerical Operations and Matrix Computations in NumPy
- Chapter 5: Data Manipulation and Analysis with Pandas
- Lesson 5.1: Overview of Pandas Data Structures
- Lesson 5.2: Data Cleaning and Preprocessing
- Lesson 5.3: Data Aggregation and Transformation
- Chapter 6: Visualization with Python
- Lesson 6.1: Introduction to Data Visualization
- Lesson 6.2: Using Matplotlib for Basic Visualizations
- Lesson 6.3: Advanced Visualizations with Seaborn
- Lesson 6.4: Real-World Examples of AI Data Visualization
Module 7
Module 4: Introduction to Machine Learning with Python
- Chapter 7: Fundamentals of Machine Learning (ML)
- Lesson 7.1: Basic Concepts and Terminology in ML
- Lesson 7.2: Types of Machine Learning: Supervised vs. Unsupervised
- Lesson 7.3: Understanding Overfitting and Underfitting
- Chapter 8: Machine Learning with Scikit-Learn
- Lesson 8.1: Introduction to Scikit-Learn
- Lesson 8.2: Implementing Supervised Learning Models
- Lesson 8.3: Unsupervised Learning: Clustering and Dimensionality Reduction
- Lesson 8.4: Model Evaluation Metrics: Accuracy, Precision, and Recall
Module 8
Module 5: Deep Learning with Python
- Chapter 9: Building Neural Networks
- Lesson 9.1: Introduction to Neural Networks
- Lesson 9.2: Implementing Neural Networks with TensorFlow
- Lesson 9.3: Understanding Deep Learning Architectures
- Chapter 10: Advanced Deep Learning Models
- Lesson 10.1: Convolutional Neural Networks (CNNs) for Image Recognition
- Lesson 10.2: Recurrent Neural Networks (RNNs) for Sequence Modeling
- Lesson 10.3: AI Project: Implementing Deep Learning Models for Image or Text Recognition
Module 9
Module 6: Natural Language Processing (NLP) with Python
- Chapter 11: Fundamentals of NLP
- Lesson 11.1: Tokenization, Stemming, and Lemmatization
- Lesson 11.2: Using NLTK for Text Processing
- Lesson 11.3: spaCy for Advanced NLP
- Chapter 12: Building NLP Applications
- Lesson 12.1: Sentiment Analysis with Python
- Lesson 12.2: Building a Chatbot with Python Libraries
Module 10
Module 7: AI for Data Science and Analytics
- Chapter 13: Data Science Essentials for AI
- Lesson 13.1: Exploring Large Datasets with Pandas and NumPy
- Lesson 13.2: Feature Engineering for AI Models
- Lesson 13.3: Real-World Case Study: Predictive Analytics
Module 11
Module 8: Advanced Python Techniques for AI
- Chapter 14: Object-Oriented Programming in Python
- Lesson 14.1: Object-Oriented Programming (OOP) Concepts
- Lesson 14.2: Implementing OOP for Scalable AI Models
- Chapter 15: Advanced Programming Techniques
- Lesson 15.1: Introduction to Multithreading in Python
- Lesson 15.2: Parallel Processing for Efficient Computations
Module 12
Module 9: AI Model Deployment and Integration
- Chapter 16: AI Model Deployment Strategies
- Lesson 16.1: Deploying AI Models with Flask
- Lesson 16.2: FastAPI for High-Performance AI Model Deployment
- Lesson 16.3: Introduction to Cloud Services for AI (AWS, Google Cloud)
Module 13
Module 10: AI Projects and Real-World Applications
- Chapter 17: Implementing AI-Driven Projects
- Lesson 17.1: AI in Finance: Building Predictive Models
- Lesson 17.2: AI in Healthcare: Analyzing Medical Data
- Lesson 17.3: AI in Social Media: Sentiment and Trend Analysis
Module 14
Module 11: Ethics in AI Development
- Chapter 18: Addressing Ethical Concerns in AI
- Lesson 18.1: Understanding AI Bias and Fairness
- Lesson 18.2: Ethical Responsibilities in AI Systems
- Lesson 18.3: Case Studies on Ethical AI Development
Module 15
Who Should Enrol?
- M.Tech, M.Sc, and MCA students in IT, Computer Science, and related fields.
- E0 & E1 level professionals in BFSI, IT services, consulting, and fintech IT services.
Module 16
Program Outcomes
- Enhanced Python Skills: Advanced proficiency in Python for developing AI applications.
- AI Implementation Expertise: Skills to implement and manage Python-based AI projects effectively.
- Strategic Insight: Understanding of how to strategically use Python in AI to drive business and technological innovation.
Module 18
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 19
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 20
Publication Opportunity
Get published in a prestigious open-access journal.
Module 21
Centre of Excellence
Become part of an elite research community.
Module 22
Networking & Learning
Connect with global researchers and mentors.
Module 23
Global Recognition
Worth โน20,000 / $1,000 in academic value.
Module 24
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