Parallaxnet

Keterangan

Kursus ini mengenalkan konsep dasar AI, sejarah, serta penerapannya di berbagai bidang seperti kesehatan, bisnis, dan pendidikan. Peserta akan mempelajari jenis-jenis AI, komponen utamanya (machine learning, neural networks, dan deep learning), serta subbidangnya seperti robotika, NLP, dan computer vision.

Kursus juga membahas alat dan bahasa pemrograman AI seperti Python dan TensorFlow, serta isu etika seperti privasi dan bias. Studi kasus nyata disertakan untuk menunjukkan bagaimana AI digunakan dalam dunia nyata.

1. Introduction to Python

   1. Introduction to Programming

      • Understanding the role of Programming in Technology
      • Programming Languages Overview.
      • Setting Up a Development Environment

   2. Basic Programming Concepts

      • Variables, Data Types, Operators and Expressions
      • Input and Output Data
      • Conditional Statement and Loops

   3. Functions

      • Definition and use of Function
      • Visibility or use of variables
      • Recursion

   4. Data Structures

      • List, Tuples and Arrays
      • Sets and Dictionaries
      • Introduction to Data Manipulation

   5. Algorithms

      • What are Algorithms?
      • Sorting and Searching algorithms
      • Algorithm Efficiency Analysis

   6. Object-Oriented Programming (OOP)

      • Classes and Objects
      • Understanding the principles of OOP
      • Inheritance and Polymorphism

   7. Handling Errors and Debugging

      • Common errors and debugging techniques
      • Exception Handling

2. Introduction to Artificial Intelligence (AI)

   1. Comprehensive Introduction

      • Definition and Basic Concepts of AI
      • Brief History and Evolution of Artificial Intelligence
      • Importance and Applications of AI in the Modern World

   2. Understanding the Fundamentals of AI

      • Types of AI: Narrow AI, General AI, and Superintelligent AI
      • Components of AI: Machine Learning, Neural Networks, and Deep Learning
      • Working Principles of AI

   3. Exploring Major Subfields of AI

      • Robotics: Understanding AI in Autonomous Systems
      • Natural Language Processing: How AI Understands and Generates Human Language
      • Computer Vision: How AI Interprets Visual Information
      • Speech Recognition: How AI Processes and Understands Human Speech

   4. AI Tools and Technologies

      • Introduction to AI Programming Languages: Python, R, Java, Lisp
      • Overview of AI Platforms and Tools: TensorFlow, Keras, PyTorch
      • Understanding AI Algorithms: Supervised Learning, Unsupervised Learning, Reinforcement Learning

   5. Ethical Considerations and the Future of AI

      • Ethics in AI: Bias, Privacy, and Transparency Issues
      • The Future of AI: Trends, Opportunities, and Challenges
      • Role of AI in Society: Impact on Jobs, Economy, and Daily Life

   6. Practical Application and Case Studies of AI

      • AI in Healthcare: Diagnosis, Treatment, and Patient Care
      • AI in Business: Customer Service, Marketing, and Decision Making
      • AI in Education: Personalized Learning, Intelligent Tutors, and Learning Analytics

   7. Further Resources and Studies on AI

3. Introduction to Machine Learning

   1. Introduction to Machine Learning

      • Definition and Explanation of Machine Learning
      • Brief History of Machine Learning
      • Importance and applications of Machine Learning in various sectors

   2. Types of Machine Learning

      • Supervised Learning
      • Unsupervised Learning
      • Supervised Learning vs. Unsupervised Learning
      • Reinforcement Learning

   3. Key Concepts in Machine Learning

      • Algorithms
      • Definition and significance of algorithms in Machine Learning
      • Types of Algorithms and their applications
      • Models
      • Explanation of what models are in the context of Machine Learning
      • The Process of training, testing, and deploying models
      • Evaluation
      • Understanding the concepts of Overfitting and Underfitting
      • Techniques used to evaluate the Machine Learning model’s performance
      • Bias and Variance in Machine Learning
      • Understanding the concepts of Bias and Variance
      • The trade-off between bias and variance

   4. Practical Implementation and Tools

      • Introduction to programming languages for Machine Learning (Python, R)
      • Overview of tools and libraries used in Machine Learning (Scikit-learn, TensorFlow)
      • Walkthrough of a simple Machine Learning project from data collection to model deployment

   5. Ethical Considerations in Machine Learning

      • Understanding data privacy and security
      • Bias and Fairness in Machine Learning
      • Accountability and Transparency in AI and Machine Learning

   6. Future Trends and Advancements in Machine Learning

      • Role of Machine Learning in Emerging Technologies (AI, IoT, Big Data)
      • Career prospects and opportunities in the field of Machine Learning

4. Linear Algebra for Data Science

   1. Introduction
   2. Scalars
   3. Matrixes
   4. Vectors
   5. Tensors

5. Practical Statistics for ML with Python

   1. Introduction

      • Areas of Statistics
      • Exploratory Data Analysis
      • Organization and Presentation of Data
      • Graphs
      • Bar Chart
      • Frequency Distribution
      • Histograms

   2. Measures of Central Tendency

      • Measures of Central Tendency
      • Geometric Interpretation of Measures of Central Tendency
      • Measures of Variability or Dispersion
      • Robust Measures: Percentiles
      • Box and Whisker Plots

   3. Exploratory Data Analysis

      • Exploratory Data Analysis with Seaborn
      • Check for null Values
      • Relationship between variables
      • Conclusion

   4. Key Concepts in Machine Learning

      • Introduction
      • Probability or Distribution Functions
      • Discrete Distribution
      • Binomial Distribution
      • Conclusion

   5. Normal Distribution

      • Introduction
      • Normal Distribution
      • Rule 68-95-99.7
      • Standard Normal Distribution
      • Standardization
      • Calculation of Probabilities
      • Confidence Intervals

   6. Linear Regression

      • Mathematical Model
      • Bias-variance Trade-off
      • Simple Linear Regression
      • Multiple Linear Regression
      • Mean Squared Error (MSE)
      • Linear Regression with Sklearn
      • R2

6. Machine Learning with Python

   1. Introduction
   2. The Machine Learning
   3. Virtual environment
   4. Data Treatment
   5. Data Collection and Exploration
   6. Data Normalization
   7. Data Encoding
   8. Training and Test Data
   9. Metrics for Classification
   10. Metrics for Regression
   11. Kmeans Clustering
   12. Hierarchical Clustering

7. Deep Learning with Python

   1. Neural Networks

      • Introduction
      • Hardware Requirements
      • Machine Learning vs Regular Programming
      • Simple Neuronal Network with Python and TensorFlow
      • The neural network. Rules and Concepts
      • Process followed by the Neural Network
      • How will the Network learn?
      • Let's program the Network
      • Training Result
      • Adding more Layers and Neurons
      • Closing

   2. Images Classifier with Python and TensorFlow

      • Introduction
      • Description of the Practical Case
      • Regression vs Classification
      • How do you give an image to a Neural Network?
      • What kind of Network will we use?
      • Limitations of the Regular Network
      • Hidden Layers
      • Activation Function
      • Leaving Linear Hell
      • Training Data
      • Can we program now?
      • Training and Testing in Colab
      • Limitations of this Image Classifier

   3. Final Project