Artificial Intelligence Machine Learning

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI.

Prerequisites for Artificial Intelligence

There are no technical prerequisites for Machine Learning & artificial intelligence. knowledge of python and Numpy, Sklearn, Pandas and Matplotlib.

• Course Duration: 50 hours class room program, 8 Weekends
• Lab: Execises on Multiple Algorithims in ML, DL and NN using Python and Tensor Flow

Who can learn Machine Learning/ Artificial Intelligence training?

Robotics Engineer
Data Scientist
Business Analysts
Hadoop Developers
Python for Data Science
College Graduates

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Artificial Intelligence Machine Learning Syllabus

1. Artificial Intelligence
- An Introduction to Artificial Intelligence
- History of 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

2. 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-Term Memory (LSTM) Networks

3. Machine Learning and Hacking
- Machine learning
- Reprise: Deep Learning
- Symbolic Approaches and Multiagent Systems
- Societal/Ethical Concerns
- Hacking and Ethical Concerns
- Behaviour and Hacking
- Job Displacement & Societal Disruption
- Ethics of Deadly AIs
- Danger of Displacement of Humanity
- The future of Artificial Intelligence

4. Natural Language Processing
- 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
- Exploring Backpropagation

5. Artificial and Conventional Neural Network
- Understanding Artificial Neural Network
- Building an ANN
- Building Problem Description
- Evaluation 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