Artificial Intelligence
Artificial Intelligence
What is Artificial Intelligence?
Artificial intelligence (AI) has come to define society today in ways we never anticipated. AI makes it possible for us to unlock our smartphones with our faces, ask our virtual assistants questions and receive vocalized answers, and have our unwanted emails filtered to a spam folder without ever having to address them.
These kinds of functions have become so commonplace in our daily lives that it’s often easy to forget that, just a decade ago, few of them existed.
Artificial Intelligence Course Eligibility
Freshers
BE/Bsc Candidates
Any Graduate
Any Post-Graduate
Key features of Artificial Intelligence Course
Flexible schedule
Most important step to land to a job is being prepared for the interview. Oytie provides the environment where one gets the platform to practice and improvise interview skills.
Interview Preparation
Mock Interview session practice from industry experts panel.
Resume Preparation
Resumes help employers make hiring decisions and help you get your first interview. That’s why it matters how you structure your resume and what information you decide to include.
Live Project Training
Live Project training is important to learn ethics, discipline and working environment of a Company.
Practice Course Material
Learning materials are important because they can significantly increase student achievement by supporting student learning. For example, a worksheet may provide a student with important opportunities to practice a new skill gained in class.
Syllabus of Artificial Intelligence Course
Introduction Artificial Intelligence
- Future and Market Trends in Artificial Intelligence
- Intelligent Agents – Perceive-Reason-Act
- Loop Search and Symbolic Search
- Constraint-based Reasoning
- Simple Adversarial Search (Game-Playing)
- Neural Networks and Perceptrons
- Understanding Feedforward Networks
- Boltzmann Machines and Autoencoders
Exploring - Backpropagation
Deep Networks and Structured Knowledge
- Deep Networks/Deep Learning
- Knowledge-based Reasoning
- First-order Logic and Theorem
- Rules and Rule-based Reasoning
- Studying Blackboard Systems
- Structured Knowledge: Frames, Cyc, Conceptual Dependency
- Description Logic
- Reasoning with Uncertainty
- Probability & Certainty-Factors
- What are Bayesian Networks?
- Understanding Sensor Processing
- Natural Language Processing
- Studying Neural Elements
- Convolutional Networks
- Recurrent Networks
Long Short-T
- Natural Language Processing
- Natural Language Processing in Python
- Natural Language Processing in R
- Studying Deep Learning
- Artificial Neural Networks
- ANN Intuition
- Plan of Attack
- Studying the Neuron
- The Activation Function
- Working of Neural Networks
- Exploring Gradient Descent
- Stochastic Gradient Descent
Artificial and Conventional Neural Network
- Understanding Artificial Neural Network
- Building an ANN
- Building Problem
- Description
- Evaluation of the ANN
- Improving the ANN
- Tuning the ANN
- Conventional Neural Networks
- CNN Intuition
- Convolution
- Operation
- ReLU Layer
- Pooling and
- Flattening
- Full Connection
- Softmax and Cross
- Entropy
- Building a CNN
- Evaluating the CNN
- Improving the CNN
- Tuning the CNN
- Recurrent Neural Network
- RNN Intuition
- The Vanishing Gradient Problem
- LSTMs and LSTM Variations
- Practical Intuition
- Building an RNN
- Evaluating the RNN
- Self-Organizing Maps
- SOMs Intuition
- Plan of Attack
- Working of Self-Organizing Maps
- Revisiting K-Means
- K-Means Clustering
- Reading an Advanced
- SOM
- Building an SOM
- Energy-Based Models (EBM)
- Restricted Boltzmann Machine
- Exploring Contrastive
- Divergence
- Deep Belief NetworksDeep
- Boltzmann Machines
- Building a Boltzmann Machine
- Installing Ubuntu on Windows
- Installing PyTorch
- AutoEncoders: An Overview
- AutoEncoders Intuition
- Plan of Attack
- Training an AutoEncoder
- Overcomplete hidden layers
- Sparse Autoencoders
Denoising - Autoencoders
Contractive - Autoencoders
Stacked Autoenc
PCA, LDA, and Dimensionality Reduction
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- PCA in Python
- PCA in R
- Linear Discriminant
- Analysis (LDA)
- LDA in Python
- LDA in R
- Kernel PCA
- Kernel PCA in Python
- Kernel PCA in R
- K-Fold Cross-Validation in Python
- Grid Search in Python
- K-Fold Cross-Validation in R
- Grid Search in R
- XGBoost
- XGBoost in Python
Batch Schedule
Students Reviews & Testimonials
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