• String preprocessing (re)
  • Embedding & Word2Vec
  • RNN
  • LSTM & GRU
  • Text classification
  • LDA
  • Glove
  • Language Modeling
  • Text Generation
  • Auto correct
  • Text classification
  • LDA
  • ChatGPT

The post Natural Language processing appeared first on Egycet.

Introduction to Computer Vision
CNN Architecture
Padding & Stride Convolutions
Pooling Layers
Convolutional Neural Networks & Datasets
Multiclass classification
Object Detection
Non-max Suppression
YOLO Algorithm
Face Verification and Binary Classification
Transformers
Auto-Encoders
GANs

The post Computer vision appeared first on Egycet.

Difference between AI, DL, ML and ANN
Introduction to Neural Networks
Deep Learning with Pytorch
Linear Regression
Binary Classification
Logistic Regression
Gradient Descent
Deep layer neural network
Forward and Backward Propagation
Regularization and Dropout
Adam optimization algorithm
Tuning process
Multi Classification with Deep Learning
Transfer learning

The post Artificial Neural Networks appeared first on Egycet.

NumPy
o Create Numpy Array
o Indexing
o Arithmetic and Logic
o Universal Array Functions

• 
  Pandas
  o Series
  o Data Frames
  o Data Input & Output
  o Useful Methods
  o Apply function
  o Grouping data and aggregate functions
  o Merging, Joining and Concatenating
  o Pivoting
• OpenCV
  o Read images
  o Color spaces
  o Operations on images
  o Draw shapes
  o Edge detection
  o Cascade classifier
  o Video stream
  o Real time detection

The post Data Preprocessing & OpenCv appeared first on Egycet.

Module one: Python (24h)

Basics
o Why python?
o python with AI and ML
o Input & Output
o Variables
o Data types
o Boolean & Comparison and Logic
o If Conditions
o For Loops
o Built-in functions & Operators
o Numbers & Math
o Functions
o Variables Scope
o Modules
o Command Lines
o File Handling
Object-Oriented Programming (OOP)
o Strings
o Special Functions
o Classes
o Inheritance
o Regular expressions
o Working with files
o Python generators

Module Two: Data Preprocessing & OpenCv (24h)

NumPy
o Create Numpy Array
o Indexing
o Arithmetic and Logic
o Universal Array Functions

• 
  Pandas
  o Series
  o Data Frames
  o Data Input & Output
  o Useful Methods
  o Apply function
  o Grouping data and aggregate functions
  o Merging, Joining and Concatenating
  o Pivoting
• OpenCV
  o Read images
  o Color spaces
  o Operations on images
  o Draw shapes
  o Edge detection
  o Cascade classifier
  o Video stream
  o Real time detection

Module Three: Machine Learning (20h)

Introduction to ML
o The difference between ML, Big data, Data analysis and Deep Learning
o Linear Algebra and Statistics for ML
o Data preprocessing

Regression problem
o Linear Regression
o Multi-linear regression
o Polynomial regression
o K-nearest neighbor regression
o Decision tree regression
o Regression Evaluation Metrics

Classification problem
o Logistic Regression
o Naive Bayes
o K-nearest neighbor classifier
o Support vector machine (SVM)
o Decision tree classifier
o Ensemble learning
o Classification Evaluation Metrics
o Random Forests
o XGBoost

Clustering Problems
o Dimensionality reduction
o K-means
o DBSCAN
o hierarchical clustering
o Association Rules

Model Selection and evaluation
o Loss functions
o Gradient descent
o Bias-variance tradeoff
o Cross-validation
o Hyper parameter tuning

Module Four: Artificial Neural Networks (20h)

Difference between AI, DL, ML and ANN
Introduction to Neural Networks
Deep Learning with Pytorch
Linear Regression
Binary Classification
Logistic Regression
Gradient Descent
Deep layer neural network
Forward and Backward Propagation
Regularization and Dropout
Adam optimization algorithm
Tuning process
Multi Classification with Deep Learning
Transfer learning

Module Five: Computer vision (24h)

Introduction to Computer Vision
CNN Architecture
Padding & Stride Convolutions
Pooling Layers
Convolutional Neural Networks & Datasets
Multiclass classification
Object Detection
Non-max Suppression
YOLO Algorithm
Face Verification and Binary Classification
Transformers
Auto-Encoders
GANs

Module Six: Natural Language processing (36h)

• String preprocessing (re)
  • Embedding & Word2Vec
  • RNN
  • LSTM & GRU
  • Text classification
  • LDA
  • Glove
  • Language Modeling
  • Text Generation
  • Auto correct
  • Text classification
  • LDA
  • ChatGPT

The post Machine learning appeared first on Egycet.

History

why python?
python with AI and ML
Input & Output
Variables
Boolean & Comparison and Logic
If Conditions
For Loops
Data types

• Strings
• Lists
• Tuples
• Sets
• Dictionaries

Numbers & Math
Built-in functions & Operators (zip, enumerate, range, …)
Functions
Variables Scope
Anaconda Environment
Jupyter Notebook
Command Line
Git & GitHub
GPU And Google Colab
Object-Oriented Programming (OOP)

• Classes & Objects
• Data Hiding and Encapsulation
• Inheritance
• Exceptions

The post Python appeared first on Egycet.

Module one: Python

why python?
python with AI and ML
Input & Output
Variables
Data types
Boolean & Comparison and Logic
If Conditions
For Loops
Built-in functions & Operators
Numbers & Math
Functions
Variables Scope
Modules
Command Lines
File Handling
Anaconda Environment
Jupyter Notebook
GPU And Google Colab
Object-Oriented Programming (OOP)

Module Two: Artificial Neural Networks

Difference between AI, DL, ML and ANN

Introduction to Neural Networks

Binary Classification

Logistic Regression

Gradient Descent

Deep layer neural network

Forward and Backward Propagation

Regularization and Dropout

Adam optimization algorithm

Tuning process

Multi Classification with Deep Learning

Deep Learning with TensorFlow And Keras

Transfer learning

Projects

Module Three: Convolutional Neural Networks

Introduction to Computer Vision

CNN Architecture

Padding & Strided Convolutions

Pooling Layers

Convolutional Neural Networks & Datasets

Object Detection

Non-max Suppression

YOLO Algorithm

Face Verification and Binary Classification

Docker Container

The post Deep Learning appeared first on Egycet.