Course Detail

Course Description

Artificial Intelligence

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Artificial intelligence is a theory and development of computer systems that can perform tasks that normally require human intelligence. Speech recognition, decision-making, visual perception, for example, are features of human intelligence that artificial intelligence may possess.

Syllabus

• Introduction to Data Science
• Understanding Data Science
• The Data Science Life Cycle
• Understanding Artificial Intelligence (AI)
• Overview of Implementation of Artificial Intelligence
• Machine Learning
• Deep Learning
• Artificial Neural Networks (ANN)
• Natural Language Processing (NLP)
• How Python connected to Machine Learning
• Python as a tool for Machine Learning Implementation
• Introduction to Python
• What is Python and history of Python
• Python-2 and Python-3 differences
• Install Python and Environment Setup
• Python Identifiers, Keywords and Indentation
• Comments and document interlude in Python
• Command line arguments and Getting User Input
• Python Basic Data Types and Variables
• List, Ranges & Tuples in Python
• Understanding Lists in Python
• Understanding Iterators
• Generators, Comprehensions and Lambda Expressions
• Understanding and using Ranges
• Python Dictionaries and Sets
• Introduction to the section
• Python Dictionaries and More on Dictionaries
• Sets and Python Sets Examples
• Input and Output in Python
• Reading and writing text files
• Appending to Files
• Writing Binary Files Manually and using Pickle Module
• Python functions
• Python user defined functions
• Python packages functions
• The anonymous Functions
• Loops and statement in Python
• Python Modules & Packages
• Python Exceptions Handling
• What is Exception?
• Handling an exception
• try….except…else
• try-finally clause
• Argument of an Exception
• Python Standard Exceptions
• Raising an exceptions
• User-Defined Exceptions
• Python Regular Expressions
• What are regular expressions?
• The match Function and the Search Function
• Matching vs Searching
• Search and Replace
• Extended Regular Expressions and Wildcard
• Useful additions
• Collections – named tuples, default dicts
• Debugging and breakpoints, Using IDEs
• Data Manipulation using Python
• Understanding different types of Data
• Understanding Data Extraction
• Managing Raw and Processed Data
• Wrangling Data using Python
• Using Mean, Median and Mode
• Variation and Standard Deviation
• Probability Density and Mass Functions
• Understanding Conditional Probability
• Exploratory Data Analysis (EDA)
• Working with Numpy, Scipy and Pandas
• Understanding Machine Learning Models
• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model
• More on Models
• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting
• Understanding Machine Learning Algorithms
• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Exploring Supervised Learning Algorithms
• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA
• Exploring Un-Supervised Learning Algorithms
• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means
• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering
• Exploring Hierarchical Clustering
• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering
• Understanding Dimensionality Reduction
• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)
• Understanding Hypothesis Testing
• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing
• Basics of Hypothesis
• Normalization
• Standard Normalization
• Parameters of Hypothesis Testing
• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test
• Overview Reinforcement Learning Algorithm
• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff
• Hands on Projects