Assured Certification
Rating (1207)
4.5/5
AI & ML Using Python
(English)
Starting at ₹14 Per Day
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Assured Certification
Rating (1207)
4.5/5
Course Highlights
Course Highlights
- Learn Python, the Essential Programming Language For AI and ML Applications.
- Understand And Apply Statistical Concepts That Form The Foundation Of AI And ML.
- Dive Deep Into Data Manipulation Using Python Libraries Like Pandas And Numpy.
- Create Insightful Visualizations With Matplotlib, Seaborn, And Plotly.
- Grasp Core Machine Learning Concepts, With Practical Applications Of Linear And Logistic Regression.
- Explore Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), And Recurrent Neural Networks (RNN), And Understand Their Real-World Applications.
- Learn How Machines Can Understand And Process Human Language.
- Master The Art Of Narrating Compelling, Data-Driven Insights To Diverse Audiences.
About the Course
16 hrs
21 Modules
20 Assessments
3 Projects
555 Subscribers
12 Months Access
This comprehensive course is designed to equip you with essential skills for working with Artificial Intelligence (AI) and Machine Learning (ML) applications. You will begin by mastering Python, the primary programming language for AI and ML, and progress to understanding key statistical concepts that form the foundation of these technologies.
The course provides hands-on experience with data manipulation using powerful Python libraries like Pandas and Numpy, and teaches you how to create insightful visualizations with tools such as Matplotlib, Seaborn, and Plotly. You will delve into core machine learning techniques, including practical applications of Linear and Logistic Regression.
As you advance, you’ll explore advanced topics in AI, such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), along with their real-world applications. Additionally, you’ll learn how machines can understand and process human language, and how to communicate data-driven insights effectively to diverse audiences.
By the end of this course, you will have a strong foundation in AI and ML, along with the practical skills to apply them to real-world challenges.
Course Structure
- Introduction to AI-ML 12.17
- Natural Intelligence vs Artificial Intelligence 6.09
- Artificial Intelligence Agents 10.50
- Types of Agents 12.49
- PEAS Discriptors 6.41
- Artificial Intelligence Components 15.09
- Faetures of Python 5.03
- Python Installation 3.50
- Visual Studio Code Installation 8.11
- Python Variables 14.48
- Python Strings 15.44
- Python Data Types 15.45
- Input & Output In Python 12.32
- Arithmetic Operators 8.57
- Relational Operators 7.20
- Logical Operators 5.35
- Assignment & Membership 3.57
- If-Else Statement 4.58
- Python For Loop 10.09
- Python While Loop 3.57
- Break, Continue & Pass Statement 6.14
- Match Case 3.30
- Introduction: Python Function 7.39
- Functions Arguments 8.57
- Keyword & Non-Keyword Arguments 6.56
- Recursive Function 5.49
- Lambda Functions 7.57
- Introduction: Python List 17.00
- List Access Using Loop 12.08
- Python Tuple 11.19
- List Sorting 5.03
- List Comprehension 9.50
- Built-In String Manipulation Functions 13.25
- Python Dictionary 10.45
- Python Sets 4.38
- Union, Intersection and Differences In Sets 6.56
- Python Map Method 5.58
- Python Filter Method 6.23
- Python Reduce Method 5.23
- Python Exception Handling 6.45
- Pythin Built-In Except 7.31
- Python User-Defined Exceptions 6.35
- Reading Data From Text File 8.05
- Reading & Writing To Text Files In Python 7.07
- Reading Data From CSV Files 5.09
- Python OOPs Concepts 8.46
- Python Classes and Objects 9.46
- Init In Python 6.48
- Static Variable In Python 6.42
- Inheritance Concepts 8.52
- Polymorphism In Python 9.31
- Python Suprer Method 4.56
- Multilevel and Multiple Inheritance 6.41
- Numpy Library 10.10
- Pandas Series and Dataframe 34.14
- Read / Write CSV 9.33
- Data exploration using Pandas 28.02
- Case study Dataset ‘Toyota.csv” 15.43
- Handling Missing Value from “Toyota.csv” 11.26
- Interpolate function to fill missing values 5.28
- Project 1 – Student Record Management 43.56
- Project 2 – Number Guessing Game 15.40
- Project 3 – Hangman Game
- Project 4 – Create Password Tracker
- Statistics Introduction 14.52
- Data Introduction and Collection techniques 12.31
- Statistics and It’s Types 13.50
- Percentiles and Quartiles 8.43
- Linear Algebra 9.40
- How to describe Categorical data 6.34
- Introduction to Machine Learning Its types 12.21
- Linear & Multiple Regression 11.50
- Linear Regression Practical 5.04
- Linear vs Logistic Regression 9.50
- Logistic Regression Practical 14.46
- Multi Logistic Regression with Practical 11.17
- Confusion matrix 9.02
- AI and Deep Learning introduction 9.11
- Artificial Neural Network (ANN) 6.46
- Convolutional Neural Net (CNN) 8.08
- Recurrent Neural Networks (RNN) 8.21
- Introduction to Data Visualization 10.16
- Python Library Matplotlib 16.45
- Python Library Seaborn 15.06
- Python Library Plotly 12.32
- Case Study 8.55
- Text Preprocessing 14.06
- Regular Expression 11.18
- Text Representation 19.44
- Text Classification 9.52
- Part of speech tagging : Spacy Library 6.19
- SMS Spam Detection 9.50
- Story telling overview 8.40
- How to create stories ? 12.54
- Stories during the steps of predictive analysis 8.20
- Exploratory Data Analysis using IPL Dataset 19.44
Your Instructor
Dr. Pooja Bijlani is an experienced IT Trainer with over 15 years in academia and research. She specializes in programming languages like C, C++, Python, Java, React, and advanced topics in Artificial Intelligence, Machine Learning, and Data Science. Known for her excellent teaching skills, Dr. Bijlani makes complex concepts accessible and engaging for her learners.
Beyond teaching, she has worked on numerous end-to-end projects in Natural Language Processing, Computer Vision, Python Desktop Applications, and Web Design. Her practical experience enriches her teaching, providing students with real-world insights. As a Subject Matter Expert, she combines academic knowledge with industry expertise to help students succeed in the tech field.
Dr. Pooja Bijlani
IT Trainer (Subject Matter Expert)
Course FAQs
This course is designed for anyone interested in exploring Artificial Intelligence, Machine Learning, and Python, whether you’re a beginner or have some technical experience.
Basic knowledge of programming and mathematics is recommended but optional.
This course will provide you with a strong foundation in Artificial Intelligence and Machine Learning using Python. You will learn how to apply key machine learning algorithms, work with essential Python libraries, and gain hands-on experience in data analysis and visualization. By the end of the course, you’ll have the skills to solve real-world problems and make data-driven decisions using AI and ML techniques.
Upon completing the payment process, you will receive an email confirmation from our team within 5 minutes. Then, you can use your login credentials to access the course on the Dashboard, where you can learn at your own pace and convenience.
Upon completing the course, you will receive a certificate of completion which you can download from your dashboard.
Earn a Certificate
After finishing the course, you will get a Certificate of Completion.
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