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AI

AI

Adobe Illustrator is a versatile vector graphics editor developed and marketed by Adobe Inc. It’s widely used by graphic designers, artists, and illustrators to create scalable graphics for various purposes such as logos, icons, drawings, typography, and complex illustrations.

Here are some key features and functionalities of Adobe Illustrator:

  1. Vector Graphics: Illustrator is based on vector graphics, which use mathematical equations to define shapes and lines. This allows for infinitely scalable designs without losing quality, making it perfect for creating logos and illustrations that need to be resized frequently.

  2. Drawing Tools: Illustrator provides a wide range of drawing tools including the Pen tool, Pencil tool, Shape tools, and more, allowing users to create precise and complex shapes with ease.

  3. Typography: With powerful text tools, Illustrator enables users to create and manipulate text in various ways, including adjusting font size, style, kerning, tracking, and more. Users can also convert text to outlines for further customization.

  4. Color and Effects: Illustrator offers extensive color management tools, including gradients, swatches, and color libraries. Additionally, users can apply various effects such as shadows, glows, and 3D effects to enhance their designs.

  5. Integration: Illustrator seamlessly integrates with other Adobe Creative Cloud applications such as Photoshop, InDesign, and After Effects, allowing for smooth workflow and easy sharing of assets between different programs.

AI Course

Machine Learning

  • Introduction to AI and Machine Learning
    1. Emergence of A.I
    2. AI in Practice
    3. Recommender Systems
    4. Definition and Features of Machine learning
    5. Machine learning Approaches
    6. Applications of Machine Learning
  • Supervised Learning
    1. Real-life Scenario
    2. Understanding the algorithm
    3. Supervised Learning Flow
    4. Types of Supervised Learning
    5. Types of Classification algorithms
    6. Types of Regression Algorithms
    7. Accuracy Metrics
    8. Cost Function
    9. Evaluating Coeffecients
    10. Logistic Regression
    11. Sigmoid Probability
    12. Accuracy Matrix
    13. Use Case: Survival of Titanic Passengers
  • Feature Engineering
    1. Feature Selection
    2. Factor Analysis
    3. Principal Component Analysis(PCA)
    4. Feature Reduction
  • Supervised Learning Classification
    1. Overview of classification
    2. Classification Algorithms
    3. Decision Tree Classifier
    4. Decision Tree Examples
    5. Random Forest Classifier
    6. Performance Measures:Confusion Matrix
    7. Performance Measures:Cost Matrix
    8. Practice:Loan Risk Analysis
    9. Naïve Bayes Classifier
    10. Steps to Calculate Posterior Probability
    11. Support Vector Machines:Linear Seperability
    12. Support Vector Machines:Classification Margin
    13. Linear SVM:Mathematical Representation
    14. Non-linear SVM’s
  • Non-Supervised Learning
    1. Example and Applications
    2. Clustering
    3. Hierarchical Clustering
    4. Practice:Customer Segmentation
    5. K-Means Clustering
    6. Optimal Number of Clusters
  • Ensemble Learning
    1. Overview
    2. Ensemble Learning Methods
    3. AdaBoost algorithm
    4. Gradient Boosting
    5. Model Selection
    6. Cross Validation
  • Recommendation System in Python using Collaborative Filtering
  • Association Rule Mining

Deep Learning

  • Fundamentals of Deep Learning
    1. What is Deep Learning
    2. Applications
    3. Weights and Activation functions
    4. Perceptron
    5. Data Preprocessing
  • Neural Networks
    1. Neural Networks
    2. Applications
    3. Loss function
    4. Backpropagation
    5. MNIST example for Neural Networks
  • Convolutional Neural Network
    1. Convolutional Neural Network with Python
    2. Convolution in Keras
    3. Pooling
    4. Dropout Technique
  • Introduction to Recurrent Neural Networks(RNN)
    1. What  are Recurrent Neural Networks (RNNs)?
    2. Long Short-Term Memory(LSTM)
    3. Implementation of RNN in Keras

Natural Language Processing

  1. What is Natural Language Processing?
  2. NLTK
  3. Tokenization
  4. Stemming and Lemmatization
  5. POS Tagging and Stopwords
  6. Text “Features” and TF-IDF Classification
  7. Named Entity Recognition (NER)
  8. Sentiment Analysis
  9. Word2vec

Computer Vision(OpenCV)

  1. Getting Started with Images
  2. Basic Image Manipulation 
  3. Accessing the Camera
  4. Read and Write Videos
  5. Image Filtering and Edge Detection
  6. Image Features and Image Alignment
  7. Object Tracking
  8. Face Detection
  9. Object Detection