MindMap Gallery AI/ML
This is a clear mind map involving the type of data, how to build AI, pre-processing techniques, etc. It elaborates on how to handle different types of data, how to build artificial intelligence systems, and how to use pre-processing techniques to optimize data. This is very important for data scientists and AI researchers.
Edited at 2024-06-12 12:07:12This is a clear mind map involving the type of data, how to build AI, pre-processing techniques, etc. It elaborates on how to handle different types of data, how to build artificial intelligence systems, and how to use pre-processing techniques to optimize data. This is very important for data scientists and AI researchers.
Personal development plan refers to the long-term or short-term goals set for oneself, as well as the planning and efforts made to achieve these goals. Developing a personal development plan has multiple functions: firstly, it can help us clarify our career or life goals, making us no longer confused. With direction, we will have self motivation. Secondly, personal development plans can drive our continuous learning and growth, whether it is professional knowledge, skills, or other soft power. In this rapidly changing era, if we don't advance, we will retreat. Personal development plans encourage us to keep up with the pace of the times. Finally, it can enhance our self-management ability, improve work efficiency, and more effectively achieve our goals. In summary, a personal development plan is crucial for a person's growth and development. This is a mind map about personal development plans. The map contains four main branches, namely: Data Engineering, Data Analytics, AI with Gen AI, and Data Science. Suitable for people interested in personal development plans.
BEPEC (Basic Engineering, Production and Event Control) is a foundational course that involves data engineering, data analysis, and data science. This course aims to provide students and practitioners with a basic understanding and skills in the fields of data science and engineering, including data collection, cleaning, processing, storage, querying, analysis, and visualization. In this course, students will learn how to apply various tools and techniques for data engineering, such as database management, data warehousing, data lakes, etc. At the same time, students will also learn the basic concepts and methods of data analysis, such as descriptive analysis, predictive analysis, and normative analysis, as well as how to use statistical analysis, machine learning, and artificial intelligence technologies for data mining and prediction. In addition, the course will also introduce the basic principles and methods of data science, including data preprocessing, feature engineering, model selection and evaluation, as well as how to apply data science knowledge to solve practical problems. This is a mind map about BEPEC. The map contains three main branches, namely: Data Engineering, Data Analytics, Data Science, and AI with Gen AI. Except for Data Analytics, all other main branches have detailed descriptions of multiple branches. Suitable for people who are interested in this.
This is a clear mind map involving the type of data, how to build AI, pre-processing techniques, etc. It elaborates on how to handle different types of data, how to build artificial intelligence systems, and how to use pre-processing techniques to optimize data. This is very important for data scientists and AI researchers.
Personal development plan refers to the long-term or short-term goals set for oneself, as well as the planning and efforts made to achieve these goals. Developing a personal development plan has multiple functions: firstly, it can help us clarify our career or life goals, making us no longer confused. With direction, we will have self motivation. Secondly, personal development plans can drive our continuous learning and growth, whether it is professional knowledge, skills, or other soft power. In this rapidly changing era, if we don't advance, we will retreat. Personal development plans encourage us to keep up with the pace of the times. Finally, it can enhance our self-management ability, improve work efficiency, and more effectively achieve our goals. In summary, a personal development plan is crucial for a person's growth and development. This is a mind map about personal development plans. The map contains four main branches, namely: Data Engineering, Data Analytics, AI with Gen AI, and Data Science. Suitable for people interested in personal development plans.
BEPEC (Basic Engineering, Production and Event Control) is a foundational course that involves data engineering, data analysis, and data science. This course aims to provide students and practitioners with a basic understanding and skills in the fields of data science and engineering, including data collection, cleaning, processing, storage, querying, analysis, and visualization. In this course, students will learn how to apply various tools and techniques for data engineering, such as database management, data warehousing, data lakes, etc. At the same time, students will also learn the basic concepts and methods of data analysis, such as descriptive analysis, predictive analysis, and normative analysis, as well as how to use statistical analysis, machine learning, and artificial intelligence technologies for data mining and prediction. In addition, the course will also introduce the basic principles and methods of data science, including data preprocessing, feature engineering, model selection and evaluation, as well as how to apply data science knowledge to solve practical problems. This is a mind map about BEPEC. The map contains three main branches, namely: Data Engineering, Data Analytics, Data Science, and AI with Gen AI. Except for Data Analytics, all other main branches have detailed descriptions of multiple branches. Suitable for people who are interested in this.
AI/ML
Types of AI
Narrow AI
Learn
General AI
Learn, Think, Invent & Solve Complex
Super AI
Intelligence above Human Intelligence
How to build AI
Data-Driven
Artificial Neural Networks
Machine Learning
Unsupervised Learning
No Label Data
Similarity based Math Equation
Supervised Learning
Specific Label Data
Regression
IF Ouput is Continuous
Loss Functions: MSE, MAE, RMSE, R2 Score
Plot: Residual Plot
Classification
IF Output is Discrete
Loss Function: Cross- Entropy, Hinge Loss
Plot: Confusion MAtrix, Classification Reports, Accuracy Score, ROC Curve, AUC Values
Self-Supervised Learning
If we give input data, it can generate output data
Reinforcement Learning
Continuous Learning - Feedback
Semi-Supervised Learning
Is the Combination of Unsupervised & Supervised
Deep Learning
Study of Artificial Neural Networks
Deep Learning Algorithm Architecture
1. Model Architecture
Feed Forward
Regression
Classification
Convolution Neural Networks
Regression
Classification
Recurrent Neural Networks/LSTM
Regression Classification
GANs - Image based Gen AI
It can generate Image
AutoEncoder - Unsupervised Learning - Image based Gen AI
It can generate image
Encoder-Decoder
it can generate text
Attention Based Models
IT can generate text
Transformer Model
BERT
GPT
T5 Models
Diffusion Model - Image based Gen AI
IT can generate Image
2. Optimizer
3. Activation Function
4. Loss Function
Deep Learning Model Performance Parameters
Learning Rate
Batch Size
Iterations
Pre-Processing Techniques
Text Analytics
NLP
Natural Language Processing
Natural Language Understanding
Natural Language Generation
ML/DL
Type of Data
Table Data/Structured/ Excel Data
ML Models
Feed Forward Neural Networks
1797*64 - Input
1797 * 1 - Output
Discrete
Yes/No, 1,2,3, Dog or Cat, Male or Female
Classification
Continuous
Price of the Vehicle, Price of the home, How much loan he/she is eligible
Regression
Image - Matrix, Array - 3D - Tensor
28,28,1, 8,8,1, 8,8,3, 28,28,3 32,32,3
1797 = 8*8*1
Learning Sequence
1. Python
Python Libraries
Pandas
Numpy
Matplotlib
Seaborn
Plotly
Scikit - Learn
TensorFlow
Keras
Streamlit/Gradio
Tools
Github Copilot/ Google Lab - Automated Code Suggestion
Sagemaker Studio
Sagemaker Canvas
Azure AI Stuido
Google Vertex AI Python
2. Statistics
Descriptive
Inferential
3. Machine Learning
Unsupervised
Supervised Learning
Regression
Classification
Model Accuracy Techniques
4. NLP
Supervised Learning/ML
5. Deployment
using Streamlit/Gradio
Deep Learning
Feed Forward - Tensorflow
Regression
Classification
Convolution Neural Network - computer Vision
LSTM/RNNs
MLOps - Azure/AWS
Transfer Learning
Image Based Gen AI
GANS
Autoencoders
EBM
NFM
Diffusion Models
Text Based Gen AI
Encoder-Decoder
Attention
Transformers
Prompt Engineering
RAG
Fine-Tuning
Pre-Training
Reinforcement Learning
Q-Learning
SARSA
Deep Q-Networks