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MindMap Gallery AI Top 30 Course Mind Map

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

Navaneeth Malingan

Recent works View more works>>

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

AI Top 30 Course Mind Map

Navaneeth Malingan

Recent works View more works>>

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

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AI Top30

Programming Languages

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