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EdrawMindAI Top 30 Course Mind Map
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AI Top 30 Course Mind Map
AI Top 30 Course Mind Map Main branches of this map: Programming Language Math for Machine Learning Machine Learning Data Preprocessing Datasets Algorithms
Edited at 2021-08-05 12:09:52- Recommended to you
- Outline
AI Top30
Programming Languages
R
Juila
Python
Programming
Hello World
Data Types
Flow Control
Functions
File Handling
Objects and Classes
Date and Time
Data Manipulation
Libraries
Numpy
Array
Pandas
Series
DataFrame
Visualization
Libraries
Seaborn
Plotly
Matplotlib
Folium
Types
Line
Area
Histogram
Bar
Pie
Box
Scatter
Bubble
Wordclouds
Map
Math for Machine Learning
Linear Algebra
Matrices
Determinants
Eigen Values and Vectors
Transpose
Symmetric Matrix
Inverse
Solving linear equation
Calculus
Derivatives
Chain Rule
Minima and Maxima
Gradient Descent
Probability and Statistics
Distributions
Variance
Covariance
Fundamantals
Bias and Variance
Overfitting and Underfitting
Cross Validation
Test Train Split
Data Distribution
Basic ML Steps
Import Libraries
Import Data
Data Cleaning
Null Value
Remove
Impute
Mean, Median
Frequency, Mode
Model -> Function Mapping
X1 -> X2
Outliers Removal
Data Type Setting
Feature Selection
Correlation and find dominant features
Range -1 to 1
ANOVA
Covariance
Range - infy to + infy
Feature Engineering
Continuous
Normalization
0 to 1
Min Max Scalar
(x - min)/(max - min)
Divide by Max Number
x/max
Why?
Equal Importance to all features
Model Converges Quickly (Gradient Descent)
Computational complexity reduced
Standardization
mean centering
Binning
Continuous to Categorical
age (10 to 90) -> Y, A, E, O
Categorical
Label Encoding
Integer Encoding
One-Hot Encoding
Based on Data Type
Date
Day of a week - Weekend
Month - Festival
String
Recepie - Chicken, Tomato, Cream
Is_chicken
Polynomial Feature Transformation
x1, x2 -> x1^2, x2^2, X1*X2, x1, x2
PCA
Dimensionality reduction
Memory save
Avoids Underfitting/Underfitting
Visualization
Model Selection
Spot Checking ML Algorithms
Compare models using cross validation
Test Train Split
Model Training
Hyperparameter Tuneing
Cross-Validation
GridSearch
Evaluation Results
Evaluation Metrics
Regression
MSE
MAE
RMSE
R-Squared Score
Classification
Confusion Matrix
TP, TN, FP, FN
Accuracy
Sub Topic
Precision
Sensitivity/Recall
Specificity
F1-score (aka F-Score / F-Measure)
ROC
AUC
Deployment
Pickle/Flask
MLflow
TF Serve
Datasets
sklearn
Iris
Cancer
public
Police violation
Medical Appointment
Car Dataset
Old Car Price Dataset
Salary Prediction
House Price Prediction
Kaggle
Titanic
Data Preprocessing
Handling Mission Values
Remove rows with null values
Impute with mean or median
Encoding
One-Hot
Integer Encoding
Label Encoding
Binning (continuous to categorical)
Feature Scaling
Normalization
Standardization
Test and Train Split
Dimensionality Reduction
Machine Learning
Types
Supervised
Linear Regression
Uivariate
Multivariate
Polynomial
Logistic Regression
Binary Classification
Multi-class Classification
Tree Based Algorithms
Decision Trees
Types
ID3
C4.5
CART
Building Tree
Gini Impurity
Gini Gain
Entrophy
Information Gain
Root, Internal, Leaf Nodes
Decision Node
Yes/No
Numeric (Sort and Average of Consecutive Values)
Ranked Data
Multiple Choice Data
Calc Impurity for each one and combination
Decision Tree Regressor
Random Forest
Bagging
Bootstraped Dataset
Randomly choose variables for decision nodes impurity
Aggregate the Results
Out of Bag Dataset/Error
Parallel Tree Building Peocess
Ensemble MEthods
Boosting
AdaBoost
Weighted Dataset
Learns from previous mistake
Sequential Tree Building PRocess
Decision Stumps
Weak Learners
Gradient Boost
1. Root Node, 2. Calc Residuals, 3. Build Tree using Residuals, 4. Model + (LR * Tree_Op)
XGBoost
Same as Gradient Boost
Unique Tree Building Processs
Cache Optimization
Parallelisation
DMatrix
SVM
Choose Edge Vectors
Max-Margin Classifier
Soft-Margin Classifier
Kernel Trick
Linear Kernel
Polynomial Kernel
Sigmoid Kernel
RBF (RAdial BAsis Function)
Tunable Params { C, gamma }
Naive Bayes
Bayes Theorem
Conditional PRobability
K - Nearest Neighbours
Unsupervised
K-Means
DBSCAN
Resilent to Noise
Hierarchical Clustering
Dendrogram
Dimensionality REduction
PCA
LDA
Reinforcement
Deep Learning
Intro
Tensorflow
Feed Forward NN/Shallow Networks
Deep NEural Networks
CNN
AlexNet
VGGNet
ResNet
Residual Blocks/Skip connections
Inception NEt
Inception Block
Transfer Learning
Entire Model
Feature Extration/Retrain
Datasets
Fashion MNIST
Digit MNIST
Imagenet
Custom Images
Sequence Models
RNN
LSTM
GRU
Vanishing GRadient
Input -> One word at a time
Cannot use GPU/Parrallelize
Encoder Decoder Model
Language TRanslation
Attention
Transformers
Positional Encoding
Multi Headed Self Attention
Easily PArallize
Input -> Entire Sentence
Hugging Face
NLP
Bag of words
Tokenizer
Word2Vec
Data Cleaning
Stemming
lem
stop word removal
n-grams
Adv Computer Vision
Object Localization
1 class
Object Detection
Multi Class
Types
RCNN
Fast RCNN
Faster RCNN
SSD
YOLO
Segmentation
Instance
Semantic
UNET
Mask RCNN
Libraries
Algorithms