Diploma Artificial Intelligence

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Diploma Artificial Intelligence

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Course Key learnings:

• The course provides the entire toolbox you need to become a data Analyst.
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations.

• Build an AI
• Understand the theory behind Artificial Intelligence
• Solve Real World Problems with AI
• Master the State of the Art AI models
• Q-Learning
• Deep Q-Learning
• Deep Convolutional Q-Learning
• A3C

Course Outline

Module1:Introduction

•  The Various Data Science Disciplines
• Connecting Data Science Discipline
• Benefits of Discipline
• Popular Data Science Techniques
• Poular Data Science Tools
• Carrers in Data Science
• Debunking Common misconception

Module2: Probability

• Probability Combinations
• Bayesians Inference
• Distributions
• Probability in Other Fields

Module3: Statics

• Descriptive Statics
• Inferential Statics Fundamentals
• Inferential Statics:Confidentals Intervals
• Practical Examples

Module4: Python

• Introduction To Python
• Variables & Data Types
• Basic Python Syntax
• Python Operators
• Conditional Statement
• Python Functions
• Sequence
• Iterations
• Advanced Python Tools
• Advanced Staticals method in Python

Method5:Advanced Statistical Methods in Python

• Advanced Statistical Methods-Liner Regression with Stats Model
• Advanced Statistical Methods-Multiple Liner Regression with Stats Model
• Advanced Statistical Methods-Liner Regression with sklearn
• Advanced Statistical Methods-Practical Example
• Advanced Statistical Methods-Logistic Regression
• Advanced Statistical Methods-Cluster Analysis
• Advanced Statistical Methods-Other Types of Clustering

Module6: Mathematics

• What is a Matrix
• Scalars and Vector
• Scalars and Vector
• Linear Algebra and Geometr
• Linear Algebra and Geometr
• Arrays in Python – A Convenient Way To Represent Matrice
• What is a Tensor
• What is a Tensor
• Addition and Subtraction of Matrice
• Addition and Subtraction of Matrice
• Errors when Adding Matrice
• Transpose of a Matri
• Dot Produc
• Dot Product of Matrice
• Why is Linear Algebra Useful?

Module7: Deep Learning

Module8:Fundamentals of Reinforcement Learning