Artificial Intelligence

Complete Overview Of The Course

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Topics Covered

Python (Internals, do’s and don’ts) Architecture, Data Structure
  • Installation of Anaconda Prompt
  • Jupyter Notebook-An Overview.
  • Shorcut Lkeys in Jupyter Notebook.
  • Data Types in Python
Python, Reading & Writing files in Python
  • Rules for Naming the Variables.
  • List, Tuple, Set, Dictionary.
  • Introduction to Files and Directories.
  • Introduction to the Command Prompt or
    Terminal Paths.
  • Text Files: Reading from a Text File (using
    with)
Loops and conditionals in python
  • If, Elif, and Else Conditions
  • For and While Loops
Data Analysis , Manipulation with numpy
  • Introduction to NumPy
  • Use Cases & Functions
Pandas Python data science package to manipulate, calculate and analyze data.
  • Creation of DataFrames
  • CRUD Operations
  • Functions & Uses of Pandas
Exploratory Data Visualization in Python with matplotlib
  • Data Visualization with Matplotlib and Seaborn
Statistical Thinking in Python (Part 1) Build the foundation
  • Thinking Statistically – Introduction
  • Measures of Central Tendency
  • Measures of Dispersion
  • IQR Statistics – Hands-On
Linear Regression
  • Introduction to Polynomial Regression
  • Code Implementation in Python
Regression using Scikit-Learning
  • Implementation of Various Regression Models
  • Addressing Overfitting with Lasso and Ridge
Introduction To Classification And KNN
  • Understanding K-Nearest Neighbors (KNN)
  • Code Implementation
Logistic Regression And Naive Bayes
  • Theories and Mathematics behind Logistic Regression and Naive Bayes
  • Code Implementation
SVM And Regression Tree, Random Forest
  • Theories and Mathematics behind Logistic Regression and Naive Bayes
  • Code Implementation
Polynomial Regression
  • Introduction to Polynomial Regression
  • Code Implementation in Python
Supervised Learning & Un-Supervised Learning
  • Introduction to Key Concepts in Unsupervised and Supervised Learning
  • k-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
Pre-processing for Machine Learning in Python
  • Introduction to Data Preprocessing
  • Exploratory Data Analysis (EDA)
  • Missing Values
  • Outliers
  • Standardizing Data / Scaling Techniques
  • Feature Scaling and Feature Selection
Tree Based Models Classification and Regression Tree
  • Decision Tree
  • Boosting Random Forest
  • Bagging
Fundamentals of Neural Network
  • Neural Network
SQL
  •  Basics of database schema
  • Importance SQL Clauses
  • SQL Joins
POWER BI
  •  Introduction to Power BI desktop
  • ETL pipeline in Power BI
  • Calculating fields with DAX
  • Visualising data with reports
  • AI functionalities of Power BI

Important Course Highlights

  • Pre – Recorded Content.
  • Live Group Q&A Session.
  • Course Community.
  • Unlimited Lifetime Access.
  • Internship Opprtunity.
  • Certificate Of Completion.
  • 00Hours
  • 00Minutes
  • 00Seconds

Course Description

This course is designed to immerse you in the ever-evolving world of Artificial Intelligence (AI). Whether you’re a complete beginner exploring the field for the first time or an experienced professional aiming to enhance your expertise, our program provides a comprehensive and structured learning journey. You will gain a strong understanding of fundamental AI concepts, machine learning techniques, and deep learning architectures, while also exploring real-world applications used across major industries. Through a blend of theoretical foundations, practical exercises, and industry-aligned case studies, you’ll learn how to navigate the complexities of modern AI systems and develop the skills needed to build intelligent, data-driven solutions. By the end of the course, you’ll be equipped with the knowledge, confidence, and hands-on experience required to thrive in today’s AI-powered landscape.

Course Content

Why Choose Us?

What You Will Learn By This Course

Machine Learning

Machine Learning

NLP & LLMs

NLP & LLMs

Python Programming

Python Programming

Certificates

Our Mentors

Our Programs

How Can We Help You

What are the prerequisites for enrolling in the Artificial Intelligence Course?

Basic programming knowledge in Python and a fundamental understanding of mathematics, particularly linear algebra and calculus, are recommended. No advanced prerequisites are required.

Who should take this Artificial Intelligence course?

This course is ideal for beginners wanting to enter the field of machine learning, data enthusiasts looking to deepen their knowledge, career changers transitioning into data science, and professionals seeking to enhance their skills in machine learning.

What topics are covered in Artificial Intelligence course?

Topics include Python programming, data manipulation with NumPy and Pandas, data visualization, regression models, classification algorithms, clustering, neural networks, and real-world applications of machine learning.

How long is this course

The course typically spans 6-8 weeks, depending on your pace, including lectures, assignments, and hands-on projects.

Will I recieve a certificate on completion of this course?

Yes, you will receive a certificate of completion and a Letter of Recommendation upon successfully finishing the course and all required assignments and projects.