• 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.

Module one: Numpy & OpenCv (12h)

NumPy
o Create Numpy Array
o Indexing
o Array Attributes
o Arithmetic and Logic
o Aggregation Functions
o Universal Array Functions
o Linear Algebra with NumPy
o Array Manipulation
o Broadcasting and Vectorization
o Random Module

• OpenCV
  o Read images
  o Color spaces
  o Operations on images
  o Image resizing, cropping, and rotation.
  o Image thresholding and binarization.
  o Smoothing and blurring
  o Morphological operations
  o Draw shapes
  o Contour detection
  o Edge detection
  o Cascade classifier
  o Video stream
  o Real-time detection
  o Object tracking

Module Two: Artificial Neural Networks (8h)

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

Module Three: Deep learning Computer vision algorithms (60h)

Introduction to Computer Vision

• Overview of computer vision and its applications.
• Image representation (pixels, channels, and color spaces).
• Image transformations (resizing, rotation, flipping).
• Histogram equalization and image enhancement.

Convolutional Neural Networks (CNNs)

• CNN Architecture
• Padding & Stride Convolutions
• Pooling Layers
• Activation functions
• Convolutional Neural Networks & Datasets
• VGGNet, ResNet, Inception, MobileNet, and EfficientNet.
• Concepts of residual connections and depthwise separable convolutions.
• Transfer learning and pre-trained models.
• Data augmentation
• Overfitting handling
• Generalization techniques

Image Classification

• Binnary classification
• Multiclass classification
• Kaggle dataset and GPUs
• Building and training models for image classification.
• Evaluation metrics (accuracy, precision, recall, F1 score).
• Handling imbalanced datasets in classification tasks.
• Face Verification and Binary Classification
• Attention Mechanisms in Vision
• Self-attention and vision transformers (ViT).
• Attention-based models for image classification .

Object Detection

• Overview of object detection techniques.
• Region-based CNNs (R-CNN, Fast R-CNN, Faster R-CNN).
• Single-shot detectors (YOLO, SSD).
• Non-max Suppression
• YOLO Algorithm
• Transformer with object detection
• DETR algorithm
• Object detection datasets
• 3D object detection
• 3D object detection datasets
• Object detection trends

Semantic and Instance Segmentation

• Difference between semantic and instance segmentation.
• Architectures like U-Net, DeepLab, and PSPNet.
• Applications in medical imaging and autonomous vehicles.

Image Generation and Style Transfer

• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs).
• Neural style transfer techniques.
• CLIP (Contrastive Language-Image Pre-Training)

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

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
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
Environment
Jupyter Notebook
GPU And Google Colab
Anaconda
• Project  (Analyze SF Salaries dataset from Kaggle)
• Project  (Analyze Ecommerce Purchase dataset from Kaggle)

Module Two: Data Preprocessing

• Feature Engineering and Extraction
  o Domain knowledge features
  o Date and Time features
  o String operations
  o Web Data
  o Geospatial features
  o Work with Text
  • Feature Transformations
  o Data Cleaning or Cleansing
  o Work with Missing data
  o Work with Categorical data
  o Detect and Handle Outliers
  o Deal with Imbalanced classes
  o Split data to Train and Test Sets
  o Feature Scaling
  o Project (Preprocess Loan data)

Module Three: Machine Learning

Introduction to ML and Business cases
o The difference between ML, Big data, Data analysis and Deep Learning
o Linear Algebra and Statistics for ML
o Data preprocessing
Introduction to ML and Business cases
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 neighbour regression
o Decision tree regression
o Regression Evaluation Metrics
Project  (Ecommerce Expenses Prediction)
Project  (Kaggle Bike Demand Predictions)
Project  (Kaggle Black Friday Purchase Predictions)
Classification problem
o Logistic Regression
o Naive Bayes
o K-nearest neighbour classifier
o Support vector machine (SVM)
o Decision tree classifier
o Ensemble learning
o Classification Evaluation Metrics
o Random Forests
o XGBoost
Project  (Predict Loan Approval Problem)
Project  (Advertising Problem)
Project  (Sentiment Analysis Problem)
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 Hyperparameter tuning

• Project (Stock Market Prediction)

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.