Advanced Artificial Intelligence: Mechanisms and Implementations

Delve into the technical depths of AI with this advanced course, focusing on cutting-edge AI technologies such as neural networks, deep learning, and reinforcement learning. Engage with practical applications and interactive projects to master TensorFlow, PyTorch, and AI optimization techniques.


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Live Classes

Engage in interactive sessions led by experienced mentors.

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Beginner to Advance

No prior experience required; suitable for learners at all levels.

Project Based Learning

Learn through hands-on experience through projects

Industry Expertise

Engage in interactive sessions led by experienced mentors.

Quick Course

Join a comprehensive program spanning 12 weeks

Course Highlights

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Course content

  • Machine Learning, its applications
  • Data types
  • Variables
  • Control flow
  • Functions
  • Virtual Environment
  • How to install anaconda
  • How to use anaconda
  • What is pip?
  • Google colab as alternative
  • File permissions
  • Process management
  • Package management
  • Package managers
  • Basic networking commands
  • file manipulation
  • text processing with Unix tools
  • shell scripting
  • basic system administration tasks
  • What is git?
  • How to use GitHub
  • GitLab
  • Bitbucket
  • Branching
  • Merging
  • Resolving conflicts
  • Working with remote repositories
  • Version control with git?
  • What is parallel computing?
  • GPU Acceleration
  • CUDA Basics
  • CUDA C/C++ Programming
  • GPU Memory Management
  • Thread Synchronization
  • Thread Divergence and Optimization
  • CUDA Libraries
  • Unified Memory
  • CUDA Profiling and Debugging
  • Multi-GPU Programming
  • Advanced CUDA Topics
  • Arrays
  • Array Creation
  • Array Operations
  • Indexing and Slicing
  • Broadcasting
  • Array Manipulation
  • Universal Functions (ufuncs)
  • Linear Algebra
  • Random Number Generation:
  • File I/O
  • Polynomials
  • Introduction to Pandas
  • Series and DataFrames
  • Data Indexing and Selection
  • Data Cleaning and Preprocessing
  • Data Aggregation and Grouping
  • Reshaping and Pivoting Data
  • Merging and Joining Data
  • Input and Output with Pandas
  • Handling Missing Data
  • Data Visualization with Pandas
  • Advanced Pandas Techniques
  • Introduction to Matplotlib
  • Basic Plotting
  • Customizing Plots
  • Subplots and Multiple Axes
  • Annotations and Text
  • 3D Plotting
  • Saving and Exporting Plots
  • Interactive Plots
  • Styling and Aesthetics
  • Integration with Matplotlib
  • Introduction to Seaborn
  • Basic Plotting with Seaborn
  • Statistical Visualizations
  • Categorical Plots
  • Distribution Plots
  • Regression Plots
  • Pair Plots and Joint Plots
  • Heatmaps
  • Introduction to scikit-learn
  • Data Preprocessing
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Model Evaluation and Selection
  • Feature Selection
  • Pipeline and Composite Estimators
  • Hyperparameter Tuning
  • Text Feature Extraction and Transformation
  • Working with Imbalanced Data
  • Introduction to PyTorch
  • Tensor Basic
  • Tensors and Operations
  • Neural Network Building Blocks
  • Training Neural Networks
  • Optimizers and Loss Functions
  • Transfer Learning
  • GPU Acceleration
  • Model Deployment
  • Visualizing Models and Results
  • Introduction to TensorFlow
  • TensorFlow
  • Training Models
  • Optimizers and Loss Functions
  • Transfer Learning
  • Deployment and Production
  • GPU Acceleration
  • Distributed Training
  • Model Visualization and Interpretability
  • Types of Machine Learning: Supervised, unsupervised, and reinforcement learning
  • Data Preprocessing: Data cleaning, feature scaling, and handling missing values
  • Feature Engineering Techniques
  • Handling Imbalanced Data
  • Handling Categorical Data
  • Gradient Descent
  • Model Evaluation: Learning Curves
  • Bias-Variance Trade-off
  • Regularisation
  • Ensemble Methods: Bagging and Boosting
  • Understanding Model Complexity: Overfitting and Underfitting of Training Data
  • Performance metrics
  • Hyperparameter Tuning
  • Grid Search and Random Search
  • Cross-validation techniques
  • Cross-Validation
  • Gradient Descent
  • Hyperparameter Tuning
  • Applications of Supervised Learning
  • Applications of Unsupervised Learning
  • Introduction to Reinforcement Learning
  • Linear Regression
  • Classification Algorithms: Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests
  • Model Evaluation: Metrics (accuracy, precision, recall, F1-score)
  • Support Vector Machines (SVMs)
  • Ensemble Methods
  • Clustering: K-Means, Hierarchical clustering, DBSCAN
  • Dimensionality Reduction
  • Advanced Clustering Techniques
  • Dimensionality Reduction: Principal Component Analysis (PCA)
  • Advanced Dimensionality Reduction Techniques
  • Neural Network
  • Biological Neuron vs. Artificial Neuron
  • Activation Functions and Loss Functions
  • Layers in Neural Networks
  • Multilayered Perceptron (MLP) and Backpropagation
  • Convolutional Deep Learning
  • Pooling and Subsampling in CNNs
  • Inception Network
  • Applications of Supervised Learning
  • Applications of Unsupervised Learning
  • Computer Vision Applications
  • OpenCV Basics
  • Image Processing
  • Image Filtering and Enhancement
  • Image Classification
  • Feature Detection and Matching
  • CNN in Computer Vision
  • Convolutional Neural Networks (CNNs)
  • Advanced Clustering Techniques
  • Object Detection
  • Object Localization
  • Object Tracking
  • Image Segmentation
  • NLP Foundations (Challenges in NLP, NLP Applications)
  • Text Preprocessing and Tokenization
  • Text Representation
  • Text Classification
  • Sequence Modeling
  • NLP for Information Extraction
  • Language Models and Transformers
  • Recursive Neural Networks (RNNs)
  • Advanced Computer Vision (OpenCV, Object Recognition and Scene Understanding, Image Captioning, Visual Question Answering (VQA), Object Detection and Tracking in Video, Generative Adversarial Networks (GANs) for Image Generation3D Computer Vision)
  • Advanced Natural Language Processing

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