The Master’s Program in Artificial Intelligence has been designed specifically for professionals who want to become experts in Python, Machine Learning & AI or who want to transform their careers altogether. There is an increasing need for intelligent and accurate decision-making across industries. This has led to an exponential growth in the adoption of AI and ML technologies, and they are expected to remain relevant in years to come. Students may study mechanics such as engineering and robotics while delving into courses that explore principles of logic, programming, and intelligence. Each course typically gives students in-depth knowledge of an aspect of machine learning and teaches them to create and program unique projects for the artificial intelligence field. Many skills may be developed during a master’s program that could lead to high-paying jobs and career advancements in the future. Students may develop critical-thinking and technology skills that help them excel in their career field, and they may also learn crucial problem-solving abilities. Start your ML & AI journey with DASVM’s Master program today.!

Learning Path:

1. Python
2. Machine Learning
3. Artificial Intelligence

Course content

Core Python

Getting Started

• History
• A Python Q&A Session
• How Python Runs Programs
• How You Run Programs

Introduction to Python:

• What is Python?
• Why Python?
• Python Applications in real life
• Brief history of Python
• Versions of Python
• Installing Python
• Using IDLE
• First Python Program
• Getting help from Python Docs

Basic Syntax

• Basic syntax
• Commenting
• Indentation
• Python keywords
• Strings
• String values
• String Operations
• String slicing
• Built in string methods
• Formatted printing
• Simple Input and Output handling

Variables

• Variables
• Data type
• Number
• string
• List
• Tuple
• Dictionary

Types and Operations

• Introducing Python Object Types
• Numeric Types
• The Dynamic Typing Interlude
• Strings
• Lists and Dictionaries
• Tuples, Files and Everything Else

Variables Data types

• Intro to dynamic typing
• Variables in Python
• Naming conventions
• Basic Data types (representation of strings, integer, floats)

Decision Making – Loops 

• While loop, if loop and nested loop
• Number type conversion – int (), long (). Float ()
• Mathematical functions, Random function, Trigonometric function

Language Building blocks

• Control statements, the if, elif, else
• True and False
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Bitwise Operators
• While loop
• Usage of pass, break and continue
• For each loop

String

• Strings- Escape char, String special Operator, String formatting Operator
• Build in string methods – center (), count () decode (), encode ()

List

• Python List – Accessing values in list, delete list elements, Indexing slicing & Matrices
• Built in Function – cmp(), len(), min(), max()

Dictionary

• Dictionary – Accessing values from dictionary, Deleting and updating elements in Dict.
• Properties of Dist., Built in Dist functions & Methods.
• Date & time -Time Tuple, calendar module and time module

Collections

• Lists
• Tuples
• Sets
• Dictionaries
• Sorting collections
• Operations on collections
• Discussion on real life application of above collections

Functions

• Introduction to functions
• Built in functions
• User defined functions
• Function parameters
• Variable arguments, args and kwargs
• Positional and named arguments
• Discussion scope of variables with respect to functions and namespace
• Passing function to another function

Files

• Files in Python- Reading keyboard input, input function
• Opening and closing files. Syntax and list of modes
• Files object attribute- open, close . Reading and writing files, file Position.
• Renaming and deleting files

Directories

• mkdir methid, chdir () method , getcwd method , rm dir

File Handling

Exception Handling

• Exception handling – List of exceptions – Try and exception
• Try- finally clause and user defined exceptions

Modules

• Introduction to modules
• Introduction to standard modules
• OS module
• path module
• Sys module
• sub process module
• Argument parsing using argparse module
• .csv file parsing using csv module
• .jason file paring using Jason module
• Xml file parsing using xml module
• Introduction to logging module

Object Oriented Programming

• Introduction to Classes and Objects
• Principles of OOP
• Instance methods
• Special methods
• Encapsulation
• Inheritance
• Polymorphism

Regular Expressions

• Introduction to regular exceptions
• Introduction to re module
• Simple character matches
• Match function
• Searching function
• Regular expression patterns
• Patterns in Regex
• Search And Replace

GUI Programming

• Introduction
• Tkinter programming
• Tkinter widgets

My SQL

• Data base connectivity
• Methods- MySQL, Oracle, how to install MYSQL, DB connection
• Create, insert, update and delete operation, Handling erros

Multi-Threading

• Into Multi-Threading
• Threading module
• creating thread
• Synchronizing threads
• Multithreaded Priority Queue

Optional I (For testers)

• Introduction to testing using Python
• Introduction to test automation
• Introduction to Selenium web deriver
• Web testing using selenium

Option II (   Developers)

• Generators
• Decorators
• Iterators and iterator protocol
• Debugging using PDB

Options III (Web programming)

• Introduction to web programming using Python
• Introduction to Django/Flask
• Introduction to Restful API’s using Python

Option IV (Data Science)

• Introduction to data science using python
• Introduction to panda’s module
• Introduction to data visualization using matplotlib
• Introduction to NumPy
• Introduction to SciPy

Machine Learning

Python for Machine Learning

• Programming Basics
• Python Data Types
• Structures and conditional statements
• Python core packages
• Introduction to Jupyter Notebook
• Introduction to Numpy and Pandas
• Data filtering and selecting
• Find duplicates and treating missing values
• Concatenate and transform data

Setting up and installations

• Installing python
• Setting up Python environment for development
• Installation of Jupyter Notebook
• How to access our course material
• Write your first program in python

 Python object and data structures operations

• Introduction to Python objects
• Number objects and operations
• Variable assignment and keywords
• String objects and operations
• Print formatting with strings

 Python statements

• Introduction to Python statements
• If, else-if and else statements
• Comparison operators
• Chained comparison operators
• What are loops?
• For loops
• While loops

File and exception handling

• Process files using python
• Read/write and append file object
• File functions
• File pointer and operations
• Introduction to error handling
• Try, except and finally

Basic Statistics for Machine Learning

• Basic Statistics and Exploratory Analysis
• Descriptive summary statistics with Numpy
• Summarize continous and categorical data
• Outlier analysis

Introduction to Machine Learning

• Overview of Supervised and Unsupervised Machine Learning
• Linear Regression
• Clustering with K-means
• Naive Bayes Classification
• Introduction to Neural Networks

 Data Processing for Machine Learning

• Advanced Data Mugging
• Outlier Analysis
• Treating for missing values
• Normalization vs Standardization of data

 Machine Learning Algorithms

• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchial Clustering concept and application

 Building and Training Machine Learning models

• Setting up the project with ML workflow.
• Data Preprocessing and statistical exploration
• Building , Training and evaluation of Machine Learning Model

Artificial Intelligence

Introduction to Python

• Concepts of Python programming
• Configuration of Development Environment
• Variable and Strings
• Functions, Control Flow and Loops
• Tuple, Lists and Dictionaries
• Standard Libraries

Data Science Fundamentals

• Introduction to Data Science
• Real world use-cases of Data Science
• Walkthrough of data types
• Data Science project lifecycle

 Introduction to NumPy

• Basics of NumPy Arrays
• Mathematical operations in NumPy
• NumPy Array manipulation
• NumPy Array broadcasting

Data Manipulation with Pandas

• Data Structures in Pandas-Series and Data
• Frames
• Data cleaning in Pandas
• Data manipulation in Pandas
• Handling missing values in datasets
• Hands-on: Implement NumPy arrays and
• Pandas Data Frames

Data Visualization in Python

• Plotting basic charts in Python
• Data visualization with Matplotlib
• Statistical data visualization with Seaborn
• Hands-on: Coding sessions using Matplotlib,
• Seaborn package

Exploratory Data Analysis

• Introduction to Exploratory Data Analysis (EDA) steps
• Plots to explore relationship between two variables
• Histograms, Box plots to explore a single variable
• Heat maps, Pair plots to explore correlations

Introduction to Machine Learning

• What is Machine Learning?
• Use Cases of Machine Learning
• Types of Machine Learning – Supervised to Unsupervised methods
• Machine learning workflow

Linear Regression

• Introduction to Linear Regression
• Use cases of Linear Regression
• How to fit a Linear Regression model?
• Evaluating and interpreting results from Linear Regression models
• Predict Bike sharing demand

Logistic Regression

• Introduction to Logistic Regression
• Logistic Regression use cases
• Understand use of odds & Logic function to perform logistic regression
• Predicting credit card default cases

Decision Trees & Random Forest

• Introduction to Decision Trees & Random Forest
• Understanding criterion (Entropy & Information Gain) used in Decision Trees
• Using Ensemble methods in Decision Trees
• Applications of Random Forest

Model Evaluation Techniques

• Introduction to evaluation metrics and model selection in Machine Learning
• Importance of Confusion matrix for predictions
• Measures of model evaluation – Sensitivity, specificity, precision, recall & f-score
• Use AUC-ROC curve to decide best model

Dimensionality Reduction using PCA

• Introduction to Curse of Dimensionality
• What is dimensionality reduction?
• Technique used in PCA to reduce dimensions
• Applications of Principle component Analysis (PCA)
• Optimize model performance using PCA on SPECTF heart data

KNearestNeighbours

• Introduction to KNN
• Calculate neighbours using distance measures
• Find optimal value of K in KNN method
• Advantage & disadvantages of KNN

Naive Bayes Classifier

• Introduction to Naïve Bayes Classification
• Refresher on Probability theory
• Applications of Naive Bayes Algorithm in Machine Learning
• Classify spam emails based on probability

K-means Clustering

• Introduction to K-means clustering
• Decide clusters by adjusting centroids
• Understand applications of clustering in Machine Learning
• Segment hands in Poker data

Support Vector Machines

• Introduction to SVM
• Figure decision boundaries using support vectors
• Identify hyper plane in SVM
• Applications of SVM in Machine Learning

Time Series Forecasting

• Components of time series data
• Interpreting autocorrelation & partial autocorrelation functions
• Introduction to Time Series analysis
• Stationary vs non stationary data
• Stationary data and implement ARIMA model

Apriori Algorithm

• Applications of Apriori algorithm
• Understand Association rule
• Developing product recommendations using association rules
• Analyse online retail data using association rules

Recommendation Systems

• Introduction to Recommender systems
• Types of Recommender systems – collaborative, content based & Hybrid
• Types of similarity matrix (Cosine, Jaccard, Pearson correlation)
• Build Recommender systems on Movie data using KNN basics

Linear Discriminant Analysis

• Recap of dimensionality reduction concepts
• Types of dimensionality reduction
• Dimensionality reduction using LDA
• Apply LDA to determine Wine Quality

Anomaly Detection

• Introduction to Anomaly detection
• How Anomaly detection works?
• Types of Anomaly detection: Density based, Clustering etc. NET Based Commands
• Detect anomalies on electrocardiogram data

Ensemble Learning

• Introduction to Ensemble Learning
• What are Bagging and Boosting techniques?
• What is Bias variance trade off?
• Predict wage (annual income) classes from adult census data

Optimization

• Optimization techniques: Linear Programming using Excel solver
• Optimization in ML
• Neural networks
• Workflow of a Neural network & analogy with biological neurons

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