Artificial intelligence Online Training
Overview
Artificial Intelligence is the study on how human brain think, learn, decide and work and to make a computer or product to think and respond similarly. Deals with development of algorithms and techniques to simulate or re-create human mind capabilities. Helps in producing intelligent machines that work and react like humans. AI is the best field for dreamers to play around. It must be evolved from the thought that making a human-machine is possible. AI has three types: ANI (Artificial Narrow Intelligence), AGI (Artificial General Intelligence), ASI (Artificial Super Intelligence).
Training Objective (What you will learn)
Rather than mastering tools or environments emphasis is on teaching AI fundamentals to introduce basic principles, techniques and applications. Introduce Rational Intelligent Agent and other different types of Agents to solve problems. Impart basic proficiency in representing difficult real life problems in a state space representation so as to solve them using AI techniques like searching and game playing. To introduce advanced topics of AI such as planning, Bayes network.
Prerequisites
Strong hold on mathematics
good experience in programming languages
strong data analytic skills
good knowledge of Discrete mathematics.
Market Demand
At present number of Artificial intelligence technology experts is limited. Increasing demand for Artificial intelligence in different market segments rising demand for Artificial intelligence professionals across industries..
Artificial intelligence Course Content
- Introduction to Artificial intelligence
- Why use Python for data science
- Installation of Python using Anaconda in local system along with Jupiter notebook
- Flowchart for representing logic
- Data Structures in python
- Conditional statements & loops in python
- Object-Oriented Programming paradigm in python
- Exception handling in python
- Multiprocessing & Multithreading in python
- File Handling in python
- Asymptotic notations
- Google Cloud Colab
- Introduction to Numpy
- Creating Arrays
- Initial Placeholders
- Saving & loading on disk
- Saving & loading Text Files
- Inspection of Array
- Arithmetic operations
- Comparison operations
- Aggregate Functions
- Copying Arrays
- Sorting Arrays
- Subsetting, Slicing & Indexing
- Array Manipulations
- Introduction to Pandas
- Pandas Data structures (Series & DataFrame)
- Input & Output operations using pandas
- Selection operations
- Reading & Writing to SQL Query or Database Table
- Dropping
- Sort & Rank
- Retrieving Series/ DataFrame Information
- Applying Functions
- Data Alignment
- Data Preprocessing using pandas
- Preparing the data
- Creating the plot
- Plotting Routines
- Customizing the Plot
- Saving the Plot
- Displaying the Plot
- Areas of Math essential to machine learning (Probability, Statistical Inference,
- Linear Algebra, Calculus)
- Importance of Math in Machine Learning
- The concept of probability
- Probability spaces
- Axioms of Probability
- Types of probability spaces (Discrete & Continuous spaces)
- Example of discrete probability space
- Example of continuous probability space
- Probability Distributions (Discrete & Continuous distribution)
- Random Variables
- Multivariate probability distributions
- Example of Multivariate distribution
- Marginal & Conditional Probability
- Example of Marginal Probability
- Example of Conditional Probability
- Continuous Multivariate Distribution
- Expected value of a function
- Example of Expected value of a function
- Expected Value in Continuous Space
- Mean
- Variance
- Covariance
- Pearson Correlation Coefficient
- Complement rule for occurence of an event
- Product rule for co-occurence of events
- Rule of total probability
- Indepence of event occurrence
- Bayes Rule with example
- Probabilities: When to add, When to multiply