Artificial Intelligence

What you'll learn

This comprehensive Artificial Intelligence course is designed to take you from foundational principles to advanced, real-world AI applications. You will explore how intelligent systems are built using machine learning, deep learning, computer vision, and natural language processing, while gaining practical experience with industry-standard tools and frameworks.

Throughout the program, you will work on hands-on projects such as image recognition, predictive modeling, recommendation systems, and conversational AI. The course emphasizes data preparation, model development, evaluation, and deployment so you can understand the complete lifecycle of AI solutions in real business environments.

By the end of this course, you will be confident in designing, developing, and deploying AI-powered applications from scratch, making you job-ready for roles such as AI Engineer, Machine Learning Engineer, Data Scientist, and Intelligent Systems Developer.

  • Fundamentals of Artificial Intelligence, Machine Learning, and Generative AI.
  • Data preprocessing, feature engineering, and model training techniques.
  • Working with supervised, unsupervised, and deep learning algorithms.
  • Evaluating and optimizing AI model performance.
  • Building applications using Generative AI and Large Language Models (LLMs).
  • Image generation, text generation, and prompt engineering techniques.
  • Applying Natural Language Processing (NLP) in real-world scenarios.
  • Deploying AI and GenAI solutions into production-ready applications.

Course Content

Intro Course content
  • What is Artificial Intelligence?
  • AI vs Machine Learning vs Deep Learning
  • IDE Setup (VS Code, Jupyter)
  • Real-world applications of AI
  • Tools & technologies overview
  • About Python
  • How to Python Work
  • Python setup & environment
  • Python Virtual Machine
  • Installing Python (Anaconda / Standard)
  • Running Python scripts
  • Python Output/print function
  • Syntax, Comments
  • Variables, Data Types, Built in Data Types
  • Type Casting & Type Checking
  • Python Literal, Operators, Keywords and Identifiers, User Input
  • If, Elif, Else
  • Nested Conditions
  • Loops (For, While)
  • Break, Continue, Pass
  • Comprehensions
  • String : String indexing, String slicing, Edit and delete a string, Operations on String, Common String functions
  • List : Create and access a add items, Edit items in a list, Deleting items from a list, Arithmetic, membership and loop operations on a List, Various List functions, List comprehension, Zip() function
  • Tuple : Create and access a tuple, Can we edit and add items to a tuple?, Deletion, Operations on tuple, Tuple functions, List vs tuple, Tuple unpacking, Zip () on tuple
  • Set : Create and access a set, Can we edit and add items to a set?, Deletion, Operations on set, set functions, Frozen set (immutable set), Set comprehension
  • Dictionary : Create dictionary, Accessing items, Add, remove, edit key-value pairs, Operations on dictionary, Dictionary functions, Nested comprehension
  • Defining & Calling Functions
  • Parameters & Arguments
  • Return Values
  • Default & Keyword Arguments
  • *args and **kwargs
  • Lambda Functions
  • Scope (local, global)
  • Nested functions
  • Introduction to OOP
  • Classes & Objects
  • Attributes & Methods
  • Constructor (__init__), self Keyword
  • Encapsulation, Abstraction, Inheritance, Polymorphism
  • Special Methods (__str__, __add__, etc.)
  • How objects access attributes
  • Iterators and Iterable Objects in Python
  • Decorators for Code Reusability and Abstraction
  • Generators for Efficient Data Processing
  • Text Processing Using Regular Expressions (re Module)
  • Package Installation (pip)
  • Importing Modules
  • Standard Library (os, sys, math, random, datetime)
  • Virtual Environments (venv, pipenv, conda)
  • Reading & Writing Files (txt, json)
  • File modes (r, w, a, rb, wb)
  • Exception Handling with Files
  • JSON Handling (json module)
  • Pickle
  • Try, Except, Finally
  • Raise Exceptions
  • Introduction to Arrays
  • List vs Arrays
  • Array Operations
  • What is Time Complexity?
  • Big-O Notation
  • Common Complexity Examples
  • Introduction to Numpy
  • Numpy Fundamentals
  • Numpy Basics
  • Array Indexing & Slicing
  • Array Operations
  • Mathematical Functions
  • Array Shape & Reshaping
  • Stacking & Splitting
  • Random Module
  • Advanced Numpy
  • Introduction to Pandas
  • Pandas Data Structures
  • Data Inspection
  • Data Selection & Indexing
  • Data Cleaning
  • Data Transformation
  • Sorting & Ranking
  • Grouping & Aggregation
  • Combining & Merging Data
  • Reshaping & Pivoting
  • Advanced Pandas
  • Matplotlib : 2D Plot / 3D Plot (Line plot, Scatter plot, Bar plot, Stem plot, Step plot, Histogram plot, Box plot, Pie chart, Subplots, Violin plot, Stacked bar chart, Contour plot, Contourf plot, Heatmap)
  • Seaborn : Line plot, Scatter plot, Dis plot, Cat plot, Rel plot, Hist plot, Kde plot, Rug plot, Bar plot, Violin plot, Heatmap, Cluster map, Strip plot, Joint plot
  • Plotly : Plotly Go, Plotly Express, Dash, Line plots, Scatter plots, Bar charts, Pie charts, Donut charts, Bubble charts, Histogram, Box plot, Violin plot, Strip plot, Waterfall charts
  • Dashboards with Plotly : Basic Dash app structure, Linking Plotly figures to Dash, Advance Dash app
  • All functions of plots
  • All changing styles of plots
  • Project using Dashboard with Plotly
  • Requests
  • BeautifulSoup
  • Selenium
  • Introduction to Statistics
  • Types of Statistics
  • Basics of Data : Population vs. Sample, Raw data vs. Grouped data, Types of data, Frequency distribution
  • Measures of Central Tendency : Mean, Median, Mode, Weighted Mean, Trimmed Mean (robust measure)
  • Measures of Dispersion (Spread of Data) : Range, Standard Deviation, Variance, Coefficient of Variation, Interquartile Range (IQR)
  • Measures of Position (Relative Standing) : Percentiles, Quartiles (Q1, Q2, Q3), Z-scores (Standard scores)
  • Measures of Shape : Skewness
  • Data Visualization in Descriptive Statistics : Categorical – Categorical, Numerical – Numerical, Categorical – Numerical
  • Hypothesis Testing
  • Null and alternate hypothesis
  • Type I & Type II errors
  • p-value interpretation
  • One-tailed vs. Two-tailed tests
  • Z-test, T-test, Chi-square test
  • ANOVA
  • 2D density plots
  • Normal Distribution
  • Standard Normal Variate
  • Properties of Normal Distribution
  • Skewness
  • CDF of Normal Distribution
  • QQ plot
  • Uniform Distribution
  • Log-normal distribution
  • Power Transformer
  • Log Transform
  • Box-Cox Transform
  • Yeo-Johnson Transformation
  • Data Understanding
  • Data Collection & Import
  • Data Cleaning
  • Feature Engineering
  • Statistical Testing
  • Visualization & Reporting
  • Feature Transformation : Standardization, Normalization, Log transformation, Power transformations, K-Nearest Neighbors
  • Feature Selection : Filter methods, Wrapper methods, Embedded methods
  • Feature Extraction : PCA, t-SNE
  • Handling Missing Values : Mean, Median, Mode, KNN imputer, Handling duplicates
  • Encoding Categorical Variables : One-Hot Encoding, Label Encoding, Target Encoding
  • Outlier detection & treatment : IQR, Z-Score, Percentile
  • Project
  • Introduction to Machine Learning
  • Types of ML
  • ML vs AI vs DL vs Data Science
  • Regression Algorithms : Linear Regression, Multiple Linear Regression, Polynomial Regression, Lasso Regression (L1 Regularization), Ridge Regression (L2 Regularization), Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, XGBoost, CatBoost
  • Classification Algorithms : Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machines (SVM), Gradient Boosting Classifier, XGBoost, CatBoost, AdaBoost, Bagging
  • Clustering : K-Means Clustering, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM)
  • Dimensionality Reduction : Principal Component Analysis (PCA), t-SNE (t-distributed Stochastic Neighbor Embedding)
  • Self-Training
  • Label Propagation
  • Introduction to RL
  • Markov Decision Process (MDP)
  • Value-Based Methods
  • Regression: MSE, RMSE, MAE, R²
  • Classification: Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix & Cross-Validation
  • Data collection
  • Overfitting and Underfitting
  • What is Git?
  • What is VCS/SCM?
  • How Git works?
  • Installing git
  • Creating and Cloning repository
  • add, commit, add, gitignore
  • seeing commits
  • Nonlinear Development (Branching)
  • Merging branches
  • Working with a remote repository
  • Introduction to Deep Learning : What is Deep Learning?, Difference between ML, DL, AI, Perceptron & Biological Neuron Analogy
  • Neural Network Fundamentals : Perceptron Model, Activation Functions, Loss Functions, Forward Propagation, Backpropagation Algorithm, Gradient Descent
  • Training Deep Neural Networks : Weight Initialization, Optimization Algorithms, Learning Rate Scheduling, Dropout (Regularization)
  • Deep Learning Frameworks : TensorFlow, Keras
  • ANN : Biological neuron vs Artificial neuron, Perceptron model, Inputs, Weights, Bias, Weighted sum, Activation function, Single-layer perceptron, Multi-layer perceptron (MLP), Input layer, Hidden layers, Output layer, Fully connected layers, Loss / Cost Functions, Backpropagation, Improving ANN Performance, Mathematical Foundations, Training ANN Models
  • CNN : Convolution layers, Convolution Operation, Pooling layers, Activation & Loss Functions,Training CNN Models, Regularization Techniques, Model Evaluation, Fully connected layers
  • RNN : RNN cell, Types of RNN, Mathematical Foundations, Backpropagation Through Time, Training RNN Models
  • LSTM : LSTM Architecture Components, Gates in LSTM, LSTM Cell Equations, Types of LSTM Architectures, Training LSTM Models, Data Preparation for LSTM, LSTM for Speech & Audio, Advanced LSTM Concepts, Model Evaluation
  • GRU : Introduction to GRU, GRU Architecture, GRU Mathematical Formulation, Forward Pass in GRU, Training GRU Models
  • Introduction to Databases & SQL : What is a Database?, CRUD operations, Types of Database, DBMS
  • SQL : Database, Table, Row, Column concepts, SQL Data Types, Creating & Dropping Databases, Creating & Dropping Tables, Altering Tables
  • CRUD Operations : INSERT INTO, SELECT, UPDATE, DELETE
  • Filtering & Sorting : WHERE, Logical Operators, Comparison Operators, Sorting Data, Limiting Results
  • SQL Functions : Aggregate Functions, String Functions, Date & Time Functions, Mathematical Functions
  • Grouping Data, GROUP BY, HAVING
  • SQL Joins : INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, SELF JOIN
  • SQL Subqueries : Subquery in SELECT, Subquery in WHERE, Nested Queries
  • SQL Set Operations : UNION vs. UNION ALL, EXCEPT, INTERSECT
  • Keys : Primary Key, Foreign Key, Unique Key, Candidate Key, Super Key, Composite Key, Surrogate Key
  • Triggers : What are Triggers?, BEFORE & AFTER Triggers
  • Advanced Functions : Window Functions, Pivot, Case Statements
  • SQL Projects
  • Introduction to NLP
  • Text Preprocessing : Tokenization, Stopword Removal, Lowercasing, normalization, Stemming, Lemmatization, Handling Punctuation, Numbers, Case Normalization, Handling Emojis & Special Characters, Remove URLs, Spelling correction, Keyword extraction
  • Text Representation (Feature Engineering) : Bag of Words, One Hot Encoding, N-grams/Bi-grams/Tri-grams, TF-IDF, Word2Vec
  • Classical NLP Algorithms : Text Classification, Language Modeling, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA)
  • Sequence Modeling : Recurrent Neural Networks (RNNs), LSTMs, GRUs, Seq2Seq Models
  • Model evaluation : Accuracy, Precision, Recall, F1-score, Confusion Matrix
  • NLTK
  • Advanced Architectures : Encoder-Decoder models, Sequence-to-sequence learning, Speech-to-text, Text-to-speech
  • NLP Project
  • Introduction to Power BI
  • Data Sources & Connectivity
  • Data Cleaning with Power Query
  • Data Modeling
  • DAX (Data Analysis Expressions)
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Power BI Project
  • Introduction to Data Visualization & Tableau
  • Data Connections
  • Data Preparation
  • Data Modeling
  • Calculations in Tableau
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Tableau Project
  • Introduction to FastAPI
  • Background Processing
  • Creating your first FastAPI app
  • Path parameters
  • Query parameters
  • Request body
  • Response models
  • GET, POST, PUT, DELETE
  • Flow Diagram
  • Databases
  • Request Handling
  • Testing FastAPI
  • Introduction to MongoDB
  • MongoDB Basics : Database, collection, document structure, CRUD operations
  • Querying Documents
  • Data Modeling in MongoDB
  • Aggregation
  • Advanced MongoDB
  • All Topics Interview Questions & Answers
  • All Topics Coding Practice
  • Real-world Problem Solving
  • Foundations of Generative AI
  • Large Language Models (LLMs)
  • LangChain
  • LangGraph
  • Hugging Face
  • Ollama
  • Retrieval-Augmented Generation (RAG)
  • AI Agents
  • LLMOps
  • Text Generation Systems
  • Image Generation
  • All Topics Interview Questions & Answers
  • All Topics Coding Practice
  • Real-world Problem Solving
  • Total Duration : 450+ hours
  • Lectures : 200+
  • Skill Level : Expert
  • Language : Hindi
  • Industry Based Course Curriculum
  • Weekdays & Weekend Classes
  • Resume & LinkedIn Profile Building
  • Regular Assessments
  • Quiz
  • Hands-on Practice & Projects
  • Globally Recognized Certification
  • Interview Preparation & Mock Interview Session
  • 100% Job Assistance
  • 24/7 Student Support
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