Introduction
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines and software that are designed to think, learn, and problem-solve like humans. With vast and rapidly expanding applications across industries, AI has emerged as a critical area of study in engineering and computer science. This course focuses on building intelligent systems capable of decision-making, perception, and learning — mimicking cognitive functions typically associated with the human mind.
About the Course
This course provides a comprehensive introduction to Artificial Intelligence, a dynamic sub-field of computer science focused on enabling machines to perform tasks that typically require human intelligence. It covers the foundational skills needed to begin working in AI and explores practical implementation using Python — one of the most widely used programming languages in AI development.
Through this course, students will gain a blend of theoretical understanding and practical experience in programming, data analysis, and machine learning, making them capable of building AI applications that solve real-world problems.
Course Objectives
By the end of this course, students will be able to:
- Understand and apply the fundamentals of Python programming.
- Work with essential data structures and libraries such as NumPy and Pandas.
- Perform data analysis, data cleaning, and data visualization.
- Understand the concepts of Machine Learning, including its models and algorithms.
- Implement machine learning algorithms for real-world applications.
- Explore the basics of deep learning, especially in the context of text and image data.
Course Highlights
- Joint certification with recognized industry partners.
- Emphasis on hands-on training and real-world application.
- Exposure to industry-standard practices in AI and ML.
- Designed to bridge the gap between academic knowledge and industry requirements.
Expected Outcomes
- Upon successful completion of the course, students will:
- Be industry-ready with applicable skills in AI and ML.
- Possess in-depth practical knowledge of AI tools and techniques.
- Have enhanced employability and job-readiness in data-driven roles.
Methodology
- Instructor-led live classes
- Instructor-led hands-on lab sessions.
- Assignments and Project Work
Course Contents
- Python Associate
- Programming with Python
Introduction to Python, Conditional Statements, Lists, Tuple, Dictionaries, Functions, OOPs Concept, Modules, Exception Handling, Input-Output, Database Connectivity, Introduction to GUI programming - Jupyter notebook – Installation & function
- Python – Operators, Expressions and Python Statements
- Conditional Statements and Loops
- Sequence Data Types – List, Tuple, set
- Input and Output in Python
- Dictionary, functions, Lambda Functions
- Modules and Functions in Python
- NumPy-arrays, indexing, slicing and iterating, reading csv into NumPy arrays
Data Analyst
Data Science Concepts
- Introduction and Installation of NumPy, Panda
and Matplotlib, Data Manipulation using Numpy & Panda, Data Visualization using Matplotlib, Introduction of GUI Programming with Tkinter, Implementation of Power BI - Advanced concepts in Numpy
- Pandas – Data frame, Series, EDA using python
- Reading and Writing data from Excel/CSV formats into Pandas
- Merging, Concatenating, Group by and aggregation on data frames
- Statistical Concepts and Functions
- Time Series Analysis and its models
- Data visualization using Matplotlib
- Grids, axes, plots, colors, fonts and styling
- Types of plots – bar graphs, pie charts, histograms, Scatterplot
- Web development using Flask
Artificial Intelligence & Machine Learning Expert
- Machine Learning – Categories of ML, Supervised, Unsupervised, Reinforcement, Semi Supervised.
- Regression, Classification, Naive Bayes, Support Vector Machines, Decision Trees, K-nearest Neighbors, Ensemble Methods of Classification, Machine Learning Evaluation Metrics, Overfitting and Under fitting, Cross Validation,
Unsupervised – What is Clustering & its Use Cases, K-means Clustering, K- means algorithm, Hierarchical clustering, Hierarchical Clustering algorithm, High-dimensional clustering, Dimension Reduction-PCA
Implementing different types of Supervised Learning algorithms
Evaluating model output, Dimensionality Output
Deep Learning
Introduction to Deep Learning, Implementation of NLP, Open CV, Spam Detection, Sentiment Analysis, Implementation of Tensor Flow and Keras
Neural Networks & Deep Learning Professional
Artificial Neural Networks – ANN structure, Feed Forward Neural network, Back Propagation.
Deep Learning Concepts, Convolutional Neural Network (CNN), Neural Network using Tensorflow.
Learning Algorithms, Error correction and Gradient Descent Rules, Perceptron Learning Algorithm. Keras and PyTorch elements
Computer Vision – Face Recognition and Detection with OpenCV, Face Recognizers, Training data, Prediction.
Natural Language Processing – Basics of text processing, Lexical processing, NLP tasks in syntax, semantics, and pragmatics. Applications like Automatic Summarization, Sentiment Analysis and Text Classification, NLTK toolkit
Reference Books/Study Material
1. Python Programming-A modular Approach (with Graphics, database, Mobile and Web Applications by Sheetal Taneja and Naveen Kumar, Pearson.
2. Beginning Programming with Python Dummies by John Paul Meuller.
3. Machine Learning an algorithmic Perspective by Stephen Marshland
4. Introduction to Machine Learning with python by Andreas CMuller, Sarah Guido.
Practical Assignments
Assignment 1. Create a numpy array with following columns: hindi, English, science, math and
commerce with data type int32.
i. Insertatleast10rowsintheabovearray.
ii. Display size and shape of the array.
iii. Print sum of each column.
iv. Print maximum element from each column.
v. Print sum of 1, 4, 5 row.
Assignment 2.
1. Create two array of size (3,3)and print their sum and multiplication.
2. Create an array of size10and calculate square root and standard deviation.
3. Print size and dimension of above arrays.
Assignment 3.
1. Write a Python program to create and display a series of data using Pandas module.
2. Create a pandas series of 10 elements and specify their index as 101to 110.
3. Printbottom5 elements of the series created in question 2.
4. Insert3new elements in above series on index111,112and 113.
5. Delete the elements atindex-103, 104,107,111inabovelist.
Assignment 4.
Write a Pandas program to create and display a Data Frame from a specified dictionary data which
has the index labels. Sample Python dictionary data and list labels:
1. exam_data={‘name’:[‘Ankita’,’Dia’,’Kapil’,’Jayesh’,’Esha’,’Mayank’,Ravi,’Lata’, ‘Kamal’, ‘Jatin’],
2. ‘score’:[12.5, 9, 16.5,15,9, 20,14.5, 17.5, 8, 19],
3. ‘attempts’:[1, 3, 2,3, 2, 3,1, 1, 2, 1],
4. ‘qualify’:[‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]}
Assignment 5.
Create a data frame using dictionary.
1. Dictionary(‘id’:[P101,P102,P103,P104,P105],‘Price’:[256,340,540,260, 470])
2. Print the price of product id– p102.
3. Print values of Price column.
4. Rename the column id to Product_Id and Price to Base_Price.
Assignment 6.
Create a new data frame with three columns– Product_Name, Cost, Sales.
1. Add 10 values in data frame.
2. Add a new column named quantity with 10 values.
3. Add a new column named: Profit and total_profit and fill values.
4. Insert a new column named location after Product_Name column with 10cities.
(New Delhi, Lucknow, Kolkata, Lucknow, New Delhi, Bengaluru, Chennai, Chennai, Kolkata,
Bengaluru)
Assignment 7.
Solve sample Machine Learning Regression problem.
Assignment 8.
Solve sample Machine Learning classification problem
Sample Questions
Q1Who is known as Father of Artificial Intelligence ?
a) Alan Turing
b) Charls Babbage
c) John Mccarthy
d) None of the Above
Q2 Which of the following is the common language for Artificial Intelligence?
a) Python
b) Java
c) Lisp
d) PHP
Q3 WhatisArtificialintelligence?
a) Putting your intelligence into Computer
b) Programming with your own intelligence
c) Making a Machine intelligent
d) Playing a Game
Q4 Which of the following is the advantage of AI?
a) Faster decision
b) 24/7 Support
c) Reduce the Risk
d) All of the above
Q5 Which of the following is the branch of Artificial Intelligence?
a) Machine Learning
b) Cyber forensics
c) Full-Stack Developer
d) Network Design
Q6 Identify the type of learning in which labeled training data is used.
a) Reinforcement learning
b) Supervised Learning
c) Unsupervised Learning
d) None of the above
Q7 What is the term known as on which the machine learning algorithms build a model
based on sample data?
a) Data Training
b) Training Data
c) Transfer Data
d) None of the above
Q8 Which one of the following statement is true for machine learning?
a) In Machine learning Input data along with the out put is fed in to the machine.
b) We would feed input data along with well written and tested program into machine to
generate output
c) In traditional programming input data along with the output is fed in to the machine.
d) None of the above
Q9 In we do prediction in the format of number or continuous value
a) Classification
b) Regression
c) Cluster
d) Association
Q10 Among the following option identify the one which is not a type of learning
a) Semi Unsupervised Learning
b) Supervised Learning
c) Unsupervised learning
d) None of the above
Course Fees :: 18,000.00 – Individual Student
Batch of 10 Students – (Single Student Fees) :: 11,500.00
Teaching :: Online / Classroom
Duration :: Regular 6 Months / Fast track 3 Months