POPULAR

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.

4.0

4033 ratings
60,458 students enrolled
₹13,000
₹18,000
27% off

Course Highlights

Live Classes

Engage in interactive sessions led by experienced mentors.

Placement Assistance

Gain exclusive access to Skolar's job portal for career opportunities.

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

Meet your mentors for this course

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

Ratings and Reviews

What do our students have to say about this course

4.7

4033 ratings

Recruiter’s feedback

What do founders/recruiters have to say about this course

Frequently Asked Questions

Skolar is a skill development and internship training platform that not only allows you to find relevant career opportunities but even comprehensively prepares you for placements
You can easily book a program at Skolar by choosing your preference. Whether you want placement training or skill development, you can browse our courses and pick your preferred course.
Skolar allows you to find courses such as Machine Learning, Web Development, Artificial Intelligence, Cyber Security, Digital Marketing, Finance, and more.
Skolar has the following placement training programs:
Course 1
Course 2
Course 3

Ready to start learning with Skolar?

Our wide database of learners includes candidates that have settled into major companies like Amazon.

Fill the form and we’ll get back to you with all the details