Syllabus for Computer Vision:

# Image processing techniques (e.g., edge detection, noise reduction image segmentation feature extraction).

# Computer vision algorithms (e.g., object detection, face recognition, image segmentation, depth estimation, optical flow).

# Deep learning frameworks (e.g., TensorFlow, PyTorch, Caffe) and

# architectures (e.g., Convolutional Neural Networks, Autoencoders, Generative Adversarial Networks).

# Datasets commonly used for training and evaluating computer vision algorithms (e.g., MNIST, CIFAR-10, COCO).

# Techniques for tracking objects in video and generating new images from existing ones.

# Common computer vision applications (e.g., autonomous vehicles, surveillance systems, medical imaging).

Syllabus for Data Science:

# Introduction to data science and its applications

# Types of data (structured, semi-structured, unstructured, primary, and secondary)

# Data visualization tools (Tableau, Power BI, Excel, etc.)

# Data modeling techniques (regression analysis, decision trees, k-means clustering, etc.)

# Machine learning algorithms (linear regression, KNN, Naive Bayes, etc.)

# Supervised and unsupervised learning techniques (classification, regression, clustering, association rule mining, etc.)

# Data preprocessing techniques (normalization, feature scaling, handling missing data, etc.)

# Dimensionality reduction techniques (PCA, LDA, t-SNE, etc.)

# Big data technologies (Hadoop, Spark, Hive, etc.)

# Data mining techniques (classification, clustering, association rule mining, outlier detection, etc.)

# Database management systems (MySQL, Oracle, SQL Server, etc.)

# Data storage technologies (HDFS, MongoDB, Cassandra, etc.)

# Data warehouse tools (Redshift, Snowflake, Teradata, etc.)

# Data analysis techniques (regression analysis, time series analysis, hypothesis testing, etc.)

# Data visualization techniques (bar charts, line charts, histograms, etc.)

# Data science skills (programming, statistics, data visualization, communication skills, etc.)

# Data science process (data collection, data preprocessing, data cleaning, exploratory data analysis, feature engineering, modeling, evaluation, deployment, etc.)

Syllabus for Machine Learning :

# Supervised Learning algorithms

# Unsupervised Learning algorithms

# Evaluation metrics for classification models

# Decision boundaries

# Feature selection techniques

# Reinforcement Learning algorithms

# Regularization techniques to prevent overfitting

# Similarity measures between vectors

# Deep Learning frameworks

# Multiclass classification algorithms

# Clustering algorithms

# Dimensionality reduction techniques

# Deep Learning architectures for image recognition

# Synthetic data generation techniques

# Parameter estimation techniques for probabilistic models.

Syllabus for Web Development :

# HTML - Hypertext Markup Language

# CSS - Cascading Style Sheets, CSS frameworks such as Bootstrap

# Responsive design, JavaScripts,Creating animations with JavaScript

# Front-end frameworks such as React, Angular, and Vue.js

# HTTP methods, HTTP status codes

# Database management systems such as MySQL, MongoDB, and PostgreSQL

# SEO - Search Engine Optimization/li>

# Web browsers such as Chrome, Firefox, and Safari

# Content management systems such as WordPress

# Server-side scripting with PHP

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Syllabus for Computer Vision:

Syllabus for Data Science:

Syllabus for Machine Learning :

Syllabus for Web Development :

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