AI and Machine Learning Bootcamp with Job Guarantee [English]

• Overview of Python
• Why Choose Python for Programming?
• What is Python?
• Real-World Applications of Python
• Platform Dependent vs Independent Languages
• Key Features of Python
• Limitations of Python
• The Evolution and History of Python

• Installing Python
• Installing and Setting Up PyCharm
• Introduction to IDLE
• How Python Code is Executed
• Writing and Running the “Hello World” Program

• Python Statements, Indentation, and Comments
• How to Print in Python
• Getting User Input in Python
• Understanding Methods, Identifiers, and Variables
• Python Data Types and Variables
• Rules for Declaring Identifiers

• Input and Output Functions in Python
• Reading Data in a Single Line
• Using the print() Function
• Formatted Print Statements
• The Replacement Operator
• Printing with the format() Method

• Different Types of Operators in Python
• Bitwise Operators in Python
• Arithmetic and Assignment Operators
• Number System Conversion in Python
• Identity Operators

• Writing if-else Statements in Python
• Using if-elif Conditions
• Creating Pyramid Patterns with Control Statements
• Transfer Statements: Break and Continue
• Using the Pass Statement
• Introduction to Loops
• Types of Loops: while and for
• Using Nested Loops

• What Are Strings?
• Built-in String Functions
• String Class and Methods
• Common String Operations
• Type Casting in Python

• Understanding Collections in Python
• Working with Lists, Tuples, Sets, and Frozen Sets
• Using Dictionaries in Python
• Bytes and Bytearray Collections

• Understanding Functions in Python
• Types of Functions and Methods
• Creating Functions in Python
• Parameters and Arguments
• Function Arguments and Call by Value
• Types of Function Arguments
• Returning Values from Functions
• Passing Functions as Arguments
• Global Variables and the global Keyword
• Using the eval() Function

• Writing Recursive Functions
• Implementing Factorial with Recursion
• Reversing Numbers with Recursion
• Fibonacci Series with Recursion

• Anonymous Functions with lambda
• Using the filter() Function with lambda
• lambda with the map() Function

• Introduction to Arrays
• Arrays Operations
• Types and Concepts of Arrays
• Using Arrays in Python
• Array Methods in Python
• Creating Arrays with NumPy
• Comparing Arrays in Python
• Understanding Object References
• Difference Between View and Copy
• Exploring NumPy Array Dimensions and Attributes
• Working with Multidimensional Arrays
• Using Matrices in Python with NumPy

• Implementing Binary Search
• Writing a Bubble Sort Algorithm

• Procedural vs Object-Oriented Programming
• Key Concepts of OOP in Python
• Creating Classes and Objects
• Understanding the self Variable
• Using the __init__() Method
• Constructor Overloading in Python
• Exploring Polymorphism and Operator Overloading
• Relational Operator Overloading
• Overloading vs Overriding in Python
• Using Getters and Setters

• Static Variables and Methods
• Working with Inner Classes
• Understanding Is-A vs Has-A Relationships
• Implementing Inheritance and Using the super() Method
• Types of Inheritance in Python
• Role of Constructors in Multiple Inheritance
• Abstract Classes and Methods
• Creating Interfaces in Python

• Basics of Exception Handling
• Common Exception Types
• Using try, except, and finally Blocks
• Exception Handling with Practical Programs
• Using the finally Statement
• Working with Assertions
• Raising Exceptions in Python
• Writing Custom Exceptions

• Introduction to Files in Python
• File Modes (r+, w+, a+)
• Reading, Writing, and Appending Files
• Using with for File Handling
• Handling File Exceptions
• Writing Data to a File with Practical Programs
• Working with readlines() and writelines() Methods
• Counting Lines, Words, and Characters in a File
• Reading Files Character by Character
• Binary File Operations in Python
• Working with the Pickle Module
• Reading and Writing CSV Files
• Using tell() and seek() Methods

• Core Concepts of NumPy Arrays
• Creating Arrays in NumPy
• Comparing Arrays in NumPy
• Performing Arithmetic Operations on Arrays
• Data Analysis with Pandas
• Working with DataFrames in Pandas
• Inserting, Deleting, and Updating Data in Pandas DataFrames
• Creating Beautiful Graphs with Matplotlib
• Creating Pie Chart, Bar Graphs, Scatter Plots, Histogram, etc.
• Draw Plot using Seaborn
• Work on Bar, Histogram, Scatter and Heatmap Plots.

• What is Statistics
• Importance in Data Science
• Understanding Data
• Types of Statistics: Descriptive vs. Inferential
• Types of Data: Qualitative vs. Quantitative
• Populations vs. Samples

• Central Tendency: Mean, Median, Mode
• Dispersion
• Range, Variance, Standard Deviation
• Coefficient of Variation

• Skewness and Kurtosis
• Understanding Normal Distribution
• Visualization Techniques

• Histograms
• Box Plots
• Bar Charts
• Pie Charts
• Scatter Plots
• Correlation

• Basics of Probability
• Axioms of Probability
• Conditional Probability
• Bayes’ Theorem
• Applications of Bayes’ Theorem

• Random Variables
• Discrete Variables
• Probability Distributions: PMF, PDF and CDF
• Binomial, Poisson, Exponential, Uniform, Log-Normal

• Sampling and Sampling Distributions
• Hypothesis Testing
• Defining Null and Alternative Hypotheses
• Type I and Type II Errors
• Using p-Value and Significance Levels
• Applying Z-test, T-test, Chi-Square Test, and ANOVA
• Confidence Intervals
• Margin of Error

• What is Regression and Its Types
• Root Mean Square Error
• K Nearest Neighbor Algorithm
• Support Vector Machine
• Random Forest Algorithm
• Clustering

• What is Machine Learning?
• How is it Different from AI?
• Types of Machine Learning
• Key ML Terminologies
• Working with NumPy, Pandas & Matplotlib
• Features and Labels in ML
• Training and Testing in ML
• Overfitting vs. Underfitting
• Mathematical Foundations
• Algorithm Survey & Use Cases
• The Machine Learning Workflow
• Popular Machine Learning Algorithms
• Reinforcement Learning

• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

• Machine Learning in Finance and Banking
• Machine Learning in Retail
• Machine Learning in Healthcare
• Machine Learning in Logistics and Supply Chain
• Machine Learning in the Technology Industry
• Machine Learning in Manufacturing
• Machine Learning in Agriculture

• Introduction to Regression
• Regression Real-World Use Cases
• Types of Regression Problems
• Linear Regression
• Evaluation Metrics
• Common Challenges with Regression
• Linear vs Polynomial vs Ridge vs Lasso Regression
• Regression Industry Applications

• What is Classification?
• Regression vs. Classification
• Classification Real-World Applications
• Types of Classification Problems
• Data Preparation for Classification
• Label Encoding vs One-Hot Encoding
• Feature Scaling in ML
• Train-Test Split in ML
• Handling Imbalanced Data in Classification
• Model Evaluation Metrics in Classification
• Accuracy in Classification
• Precision, Recall, F1-Score in Classification
• Confusion Matrix in Classification
• ROC Curve and AUC Score in Classification
• Overfitting & Underfitting in ML
• Regularization Techniques in Classification

• What is k-NN?
• How k-NN Works
• KNN Distance Metrics
• Choosing the Right ‘k’ in KNN
• Data Preparation for KNN
• Categorical Features Handling in KNN
• Strengths and Limitations of KNN
• Evaluation Metrics in KNN
• KNN Model Tuning
• Cross-Validation in KNN for Optimal ‘k’

• Introduction to Decision Tree
• Types of Decision Trees
• Components of a Decision Tree
• Decision Tree Splitting Criteria
• Gini Impurity in Decision Tree
• Entropy & Information Gain in Decision Tree N
• Advantages and Limitations of Decision Tree
• Pre-Pruning and Post-Pruning in Decision Tree
• Decision Tree Evaluation Metrics
• Decision Tree Hyperparameter Tuning
• Decision Tree Cross-Validation

• Introduction to Random Forest
• Why use Random Forest over a Single Tree?
• Concepts Behind Random Forest
• How Random Forest Works
• Random Forest Hyperparameters
• Random Forest Advantages and Limitations
• Random Forest Use Cases

• What is unsupervised learning?
• Supervised vs unsupervised learning
• Where to use Unsupervised learning
• Application of Unsupervised learning
• Popular Algorithms in Unsupervised Learning
• Real world use cases of Unsupervised learning
• Evaluation Metrics

• Introduction to Clustering Algorithms
• What is Clustering?
• Differences Between Supervised and Unsupervised Learning
• Classification vs Clustering
• K-Means Clustering
• K-means Clustering Real-world industry use cases
• Elbow method in K-means Clustering
• Hierarchical Clustering
• Agglomerative vs Divisive Approach in Hierarchical Clustering
• Hierarchical Clustering Real-world industry use cases
• Dimensionality Reduction

• Enterprise Applications Overview
• Predictive Modeling & Segmentation
• Personalization Engines
• Time Series & Anomaly Detection

• What is Scikit-Learn and Why is it Important?
• Features of Scikit-Learn for Machine Learning
• Installing Scikit-Learn and Setting Up the Environment
• Understanding the Machine Learning Workflow with Scikit-Learn

• Deep Learning vs Machine Learning
• Deep Learning Introduction
• Deep Learning Case Studies in Industry
• Why Deep Learning?
• Need for Deep Learning in Industry
• Why Deep Learning is in Demand
• Key Deep Learning Terminologies
• What is Artificial Neural Networks
• History of Deep Learning
• Applications of Deep Learning

• Convolutional Neural Networks
• Activation Functions in Deep Learning
• Optimizers in Deep Learning
• ResNet50
• Vanishing gradients
• Transfer Learning
• DenseNet121
• Recurrent Neural Networks
• ANN vs CNN vs RNN
• LSTM
• RNN vs LSTM
• LSTM in deep learning
• Deep learning architectures: perceptron, feedforward neural networks
• Activation functions and network initialization
• Backpropagation algorithm and training neural networks
• Optimization techniques for deep learning: Adam, RMSprop, etc.

• Perceptron & Feedforward Designs
• Activation & Initialization
• Backpropagation & Optimizers

• Introduction to CNNs for image analysis
• Convolution and pooling layers
• Object detection and image segmentation
• Transfer learning with pre-trained CNNs

• RNN fundamentals: architecture, hidden states, and memory cells
• Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
• Sequence generation and language modeling
• Applications of RNNs: text generation, machine translation, speech recognition

• What is OpenCV?
• Installing OpenCV Using pip
• Setting Up OpenCV in PyCharm
• How to Install and Use OpenCV in PyCharm

• Using imread() and imwrite() Functions to Load and Save Images
• Getting OpenCV Suggestions in PyCharm
• Displaying Images in OpenCV
• Resizing Images with OpenCV
• Rotating Images in OpenCV
• Merging Multiple Images
• Flipping Images Using Bitwise NOT in OpenCV
• Changing Image Colors with cvtColor() in OpenCV

• Capturing Video with OpenCV
• Recording Video from Webcam
• Drawing Lines on Images and Videos
• Drawing Circles on Images
• Adding Text to Images in OpenCV
• Difference Between add() and addWeighted() Functions
• Working with Image Properties (Shape & Size)
• Cropping Images with OpenCV

• Using hconcat() and vconcat() for Image Concatenation
• Blurring Images in OpenCV
• Creating a Graphical Image Rotation App with Tkinter
• Showing Multiple Dynamic Images with Tkinter
• Dynamically Resizing Images Using Tkinter
• Basic Video Operations with Dialog Box in OpenCV
• Resizing Videos and Images with Specified Dimensions
• Drawing Various Shapes in OpenCV
• Applying Image Transformations in OpenCV
• Converting BGR Images to RGB in OpenCV
• Using Different Color Codes in OpenCV
• Detecting Edges Using Canny Edge Detection in OpenCV

• What is Natural Language Processing?
• Applications of NLP
• Text Preprocessing
• Text Classification
• Sentiment Analysis

• Introduction to Artificial Intelligence
• Introduction to Gen AI

• Agentic AI Essentials
• Historical evolution: From reactive systems to proactive agents
• Working with Agentic AI
• Applications of Agentic AI
• Agent vs. Model vs. Tool: Understanding the distinction
• Agentic AI vs AI Agents
• Multi Agent Systems
• Core characteristics of AI agents: autonomy, proactivity, adaptability
• Real-world use cases of AI agents
• Agent architectures
• Open-source agent frameworks

• What is DSA using Python?
• Why Python is a great choice for DSA
• Why DSA with Python
• Types of Data Structures

• Introduction to Stack in DSA Python
• Stack using List
• Stack using Collection Module in DSA Python
• Stack using Queue Module in DSA Python
• Convert Infix to Postfix Expression using Stack
• Convert Infix to Prefix in DSA

• What is a Queue in Data Structures?
• Queue using List in DSA Python
• Queue using Collection Module in DSA Python
• Linear Queue using Collection Module
• Circular Queue in DSA Python
• Insert, Display and Delete in Circular Queue
• D Queue in Data Structures
• Deque using Collection Module
• Priority Queue in DSA Python
• Priority Queue using List
• Priority Queue using Module

• What is Linked List in DSA Python?
• Create, Display, Insert and Delete in Linked List in DSA Python
• Searching in Linked List
• Sorting in Linked List
• Stack Linked List in DSA Python
• Queue Linked List in DSA Python

• What is a Circular Linked List?
• Create, Display, Insert and Delete in DSA Python

• What is Doubly Linked List in DSA Python?
• Create, Display and Reverse Display in Doubly Linked List in DSA Python
• Insertion and Display in Doubly Linked List in DSA Python

• What is Circular Doubly Linked List in DSA Python
• Creation and Display in Circular Doubly Linked List in DSA Python
• Insertion and Reverse Display in Circular Doubly Linked List in DSA Python
• Deletion in Circular Doubly Linked List in DSA Python

• What is Recursion in DSA Python?
• Types of Recursion
• Tail Recursion in DSA Python

• Linear Search in DSA Python
• Binary Search in DSA Python

• Linear Sort in DSA Python
• Bubble Sort in DSA Python
• Selection Sort
• Insertion Sort
• Radix Sort
• Shell Sort
• Quick Sort
• Two way Merge Sort
• Merge Sort

• What is Tree in Data Structures using Python?
• Binary Tree and Binary Search Tree in DSA Python
• Tree Traversals in DSA Python
• Tree Traversal Methods – Inorder, Preorder, Postorder
• Creation of Tree from Inorder to Preorder in DSA Python
• Creation of Binary Search Tree
• Inorder, Preorder and Postorder in BST in DSA Python
• Searching and Sorting in BST in DSA Python
• Delete Node From Tree in DSA Python

• Strictly Binary Tree
• AVL Tree
• Red Black Tree
• Threaded Binary Tree in DSA
• B Tree in DSA Python
• B+ Tree in DSA Python
• Heap Tree in DSA Python

• Address Calculation of Array in DSA Python
• Address Calculation of 3D Array in DSA Python
• Address Calculation of Lower & Upper Bound of Array in DSA
• Tower of Hanoi in DSA Python

• What is a Graph in DSA?
• Graph Representation
• Graph Algorithms
• Kruskal’s Algorithm
• Prim’s Algorithm for Minimum Spanning Tree in DSA
• Dijkstra’s Algorithm
• Dijkstra Algorithm for Directed Graph
• Floyd Warshall Algorithm
• Breadth First Search Algorithm
• Depth First Search Algorithm
• What is Hashing

Analyze and classify movie reviews as positive or negative using natural language processing techniques. This project explores text preprocessing and sentiment classification.

Build and train a convolutional neural network to classify images into 10 different categories. This computer vision project introduces deep learning frameworks and image preprocessing.

Detect fraudulent credit card transactions using anomaly detection algorithms. This project focuses on handling imbalanced datasets and implementing effective classification models.

Develop a recommendation engine to suggest movies to users based on their viewing history. This collaborative filtering project delves into matrix factorization and similarity measures.

Classify news articles into predefined categories using NLP techniques. This text classification project covers tokenization, vectorization, and model training for multi-class classification.

Predict weekly sales for Walmart stores across different departments using historical sales data. This project involves time series forecasting and multivariate regression.

Build a model to predict customer churn based on usage patterns and demographic information. This project focuses on handling imbalanced datasets and implementing classification algorithms.

Predict future energy consumption levels using historical usage data and external factors. Focuses on time series forecasting and model deployment.

Develop a deep learning model to detect emotions from facial images. Focuses on convolutional neural networks (CNNs) and image preprocessing.

Develop a system that predicts stock price movements using live market data and technical indicators. Implement streaming data processing to update predictions continuously.

Predict future sales for a retail store using historical sales data and promotional information. This project focuses on time series analysis and regression techniques to help optimize inventory and marketing strategies.

Automatically categorize e-commerce products into predefined categories using product descriptions and metadata. This project leverages text classification and machine learning algorithms.

Build a computer vision model to recognize and classify traffic signs from images. This project involves deep learning with convolutional neural networks (CNNs) and image preprocessing techniques.

Predict which employees are likely to leave the company based on their demographic and job-related features. This classification project focuses on handling imbalanced datasets and feature selection.

Build a model to classify songs into their respective genres using audio features. This project involves audio data processing, feature extraction using libraries like Librosa, and applying machine learning algorithms for multi-class classification.

Develop a classification model to identify spam emails based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing machine learning algorithms for binary classification.

Analyze and classify blockchain transactions to identify patterns, anomalies, and potential fraudulent activities. This project delves into network analysis, feature engineering from transaction data, and applying machine learning algorithms for anomaly detection.

Develop a classification model to diagnose diseases from patient medical records. This project emphasizes data preprocessing, handling missing values, and building accurate diagnostic models.

Build a real-time language translation model using neural machine translation techniques. This natural language processing project involves sequence-to-sequence models and attention mechanisms to facilitate multilingual communication.

Build a classification model to predict loan approval outcomes based on applicant demographics, financial history, and loan details. This project emphasizes handling imbalanced datasets and feature selection to improve prediction accuracy.

Create a model to recognize emotions from speech recordings using audio signal processing and machine learning algorithms. This project explores feature extraction from audio data and classification techniques to interpret human emotions.

Develop a natural language processing (NLP) model to classify news articles as real or fake based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing classification algorithms to combat misinformation.

Build a natural language processing (NLP) model to detect emotions in text data, such as tweets or customer reviews. This project involves text preprocessing, sentiment analysis, and classification algorithms to interpret human emotions accurately.

Create a machine learning model to verify the authenticity of handwritten signatures. This computer vision project involves image preprocessing, feature extraction, and implementing classification algorithms to detect forgeries.