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Introduction to Artificial Intelligence and Machine Learning.

Learn the fundamentals of Artificial Intelligence and Machine Learning, exploring key concepts, algorithms, and real-world applications to build intelligent systems and advance your career in AI-driven technologies.

Real-time Projects: Work on AI/ML projects using real-world datasets
Live Sessions: Interactive classes with industry experts
Expert Trainers: Learning from professionals with years of practical industry experience
Diverse Domains: Work on projects spanning healthcare, finance, marketing, e-commerce, and more
Career Guidance & Internship Opportunities
Post-training Support: Continuous guidance post-training until you secure a job

4.5

I 20+ Reviews

Artificial Intelligence & Machine Learning Course

LearnersByte offers a comprehensive Artificial Intelligence (AI) and Machine Learning (ML) course in Hyderabad designed for students and professionals eager to excel in these cutting-edge technologies. This program covers key concepts like supervised and unsupervised learning, neural networks, and data preprocessing. With hands-on projects, learners gain practical experience in building AI/ML models, enhancing their technical skills and career prospects. Taught by industry experts, the course prepares you for roles like Data Scientist, Machine Learning Engineer, and AI Developer. Whether you’re a beginner or looking to upskill, our AI/ML course provides the knowledge and certification to thrive in the tech-driven job market. Enroll today to become a part of Hyderabad’s growing AI revolution.

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AL & ML course training in Hyderabad - Course Details

Our Artificial Intelligence (AI) and Machine Learning (ML) course offers in-depth training on essential topics like supervised and unsupervised learning, neural networks, data preprocessing, and model optimization. Designed for both beginners and professionals, the course focuses on hands-on projects, enabling you to build AI/ML models and apply them to real-world scenarios. Key modules include data analysis, deep learning basics, and practical applications of AI. Taught by industry experts, this course provides valuable certification and career guidance, preparing you for roles in data science and machine learning. Join us to master AI/ML and elevate your career in tech.

AI & ML Course - Eligibility & Pre-requisites

Course Details:

Pre-Requisites:

Open to any graduate.
No technical or coding skills required, but a basic understanding of Python is recommended.
Willingness to learn machine learning algorithms and data science concepts.

Outcome:

Gain practical experience in Machine Learning (ML) and Artificial Intelligence (AI).
Master core ML algorithms and their applications.
Work with key libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib.
Implement AI concepts using supervised and unsupervised learning.
Complete a hands-on project using AI/ML techniques.

Modules

Module 1: Introduction to AI & ML - 8 Hours

Topics

Introduction to AI and ML: Definitions, scope, and applications.
Types of ML: Supervised, unsupervised, and reinforcement learning.
Python Libraries Overview: Introduction to NumPy, Pandas, Scikit-learn, and Matplotlib.
Machine Learning Workflow: Problem formulation, data collection, model development, and evaluation.

Hands-On:

Module 2: Supervised Learning Algorithms - 12 hours

Topics

Hands-On:

Module 3: Unsupervised Learning Algorithms -12 hours

Topics

Hands-On:

Module 4: Introduction to Deep Learning - 10 hours

Topics

Hands-On:

Module 5: Model Evaluation & Hyperparameter Tuning (8 hours)

Topics

Hands-On:

Final Project & Capstone (15-20 hours)

Execution Process:

Problem Definition: Choosing a real-world problem for the final project (e.g., classification or regression).
End-to-End Project Development: Data collection, preprocessing, model selection, evaluation, and deployment.

Hands-On

Implementing a complete machine learning model from start to finish.
Data wrangling, feature engineering, model evaluation, and project documentation.

Course Details:

Best fit for:

Final-year students, graduates, or working professionals aiming to master AI/ ML and apply it in real-world projects for professional growth.

Pre-Requisites:

Open to any graduate.
No technical or coding skills required, but a basic understanding of Python is recommended.
Willingness to learn machine learning algorithms and data science concepts.

What you’ll achieve

In-depth understanding of AI/ML techniques and advanced concepts.
Mastery of machine learning algorithms, deep learning, natural language processing, and reinforcement learning.
Hands-on experience with popular AI/ML libraries (Scikit-learn, TensorFlow, Keras, PyTorch).
Practical exposure to AI/ML in real-world projects.
Model deployment using cloud platforms (AWS, GCP).
Internship experience with live industry projects.

Modules

Module 1: Introduction to AI, ML, and Tools (15 hours)

Topics

Introduction to AI and ML: History, applications, and future trends.
Python Libraries for AI/ML: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow.
Setting Up AI/ML Environments: Using Anaconda and Jupyter Notebooks.
Version Control and Project Management: Introduction to Git, GitLab, and JIRA.

Hands-On:

Module 2: Supervised Learning Algorithms (20 hours)

Topics

Regression Algorithms: Linear, Logistic, Polynomial Regression.
Classification Algorithms: K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes.
Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC Curve, AUC.
Model Selection and Cross-Validation.

Hands-On:

Module 3: Unsupervised Learning Algorithms (20 hours)

Topics

Hands-On:

Module 4: Introduction to Deep Learning (25 hours)

Topics

Hands-On:

Module 5: Natural Language Processing (NLP) (20 hours)

Topics

Hands-On:

Module 6: Reinforcement Learning (Optional/Advanced) (20 hours)

Topics

Hands-On:

Module 7: Model Deployment & Cloud Integration (20 hours)

Topics

Hands-On:

Module 8: Final Project Development & Internship (30-40 hours)

Key- Concept

End-to-End Project: Data collection, cleaning, model building, deployment.
Real-world industry projects under mentorship.
Internship integration and experience.

Project Execution:

Developing a complete AI/ML project.
Internship project work, reporting, and presentations.

Final Project: Complete a comprehensive AI/ML project.
Capstone Presentation: Showcase your project, insights, and model demonstrations.
Internship Certificate: Internship completion with hands-on experience in live industry projects.
Project Portfolio: Portfolio with code, project documentation, and final reports.

Course Details

Generative AI (GenAI) is revolutionizing AI by enabling machines to create human-like text,
images, music, and videos. This course is designed to take learners from basics to mastery,
ensuring they gain a solid understanding of how generative models work and how to build them.

Pre-Requisites:

Open to any graduate.
No technical or coding skills required

Outcome:

Gain practical experience in Machine Learning (ML) and Artificial Intelligence (AI).
Master core ML algorithms and their applications.
Work with key libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib.
Implement AI concepts using supervised and unsupervised learning.
Complete a hands-on project using AI/ML techniques.

Key Take aways

1) Source Code Discussed in the class.
2) Cheat sheets.
3) Important Topics PDF Materials.
4) Important Interview Questions PDF.
5) Mini Assignments & Exercises – Daily or weekly exercises to reinforce learning.
6) Code Debugging & Best Practices Guide – Common errors and how to fix them.
7) GitHub Repository Access – A structured repo with categorized resources.
8) Datasets for Practice – Curated datasets for hands-on ML/DL/NLP/CV projects.
9) Mock Tests & Quizzes – Topic-wise quizzes to test understanding.
10)Private WhatsApp/Telegram Community – A space for students to discuss doubts and share job openings.

Course Modules

Module 1: Introduction to Python & Setup (4 Hours)

• Getting Started with Python
-> Installation of Anaconda Distribution
-> Python Tools & Technologies
-> Python IDEs Overview (PyCharm, VS Code, Thonny, etc.)
-> Setting up Python Virtual Environments
-> Working with Jupyter-Lab, Line Magic Commands, Cell Magic Commands
-> Markdown Language
• Mini Project Code Walkthrough: Setting up a Python environment for a data analytics
project

Module 2: Python Fundamentals (4 Hours)

• Fundamentals of Python
-> Python Basics, Indentation, Comments, Variables
-> Data Types, Type Casting, Operators
-> Input and Output, Control Flow Statements (if-else, while, for, range)
-> Strings, String Manipulation
• Mini Project Code Walkthrough: Developing a basic Temperature Converter

Module 3: Functions and Advanced Data Structures (4 Hours)

Functions in Python
-> Defining Functions, Nested Functions, Closure Functions, Lambda Functions
-> Higher-Order Functions (filter, map, reduce)
• Data Structures
-> Lists (Basics, List Comprehension, Slicing)
-> Tuples, Sets, Dictionary
-> Performance considerations, Shallow and Deep Copy
• Mini Project Code Walkthrough: Building a Student Grade Tracker

Module 4: File Handling and Error Handling (3 Hours)

• Testing and Debugging
-> Writing unit tests (unittest or pytest), Debugging techniques
-> Python Errors and Exception Handling
• File Handling in Python
-> Working with CSV, Excel, and PDF files
-> Advanced file handling techniques
• Mini Project Code Walkthrough: Automating CSV Report Generation for Sales Data

Module 5: Python Libraries & External Modules (3 Hours)

• Built-in Modules or Libraries
-> datetime, math, statistics, random
-> os and sys (system interaction)
-> itertools, functools (functional programming)
-> collections (specialized data structures)
-> logging (handling logs)
• Working with External Libraries
-> pip, installing and using packages (requests, beautifulsoup4)
• Mini Project Code Walkthrough: Web Scraper for Extracting Product Prices

Module 6: Connecting Python with Databases (3 Hours)

• Connecting to Databases
-> SQLite3 with Python
-> Using MySQL with Python
-> Using MongoDB with Python
• Mini Project Code Walkthrough: Developing an Inventory Management System
• Project 1: Console-Based Python Application

Module 7: : Object-Oriented Programming in Python (4 Hours)

• Object-Oriented Programming Concepts
-> Classes, Objects, and Constructors
-> Static Methods and Class Methods
-> Inheritance, Multiple Inheritance
-> Overriding, Properties in Python
-> Abstract Classes and Methods
-> Polymorphism, Magic Functions
• Mini Project Code Walkthrough: Designing an Employee Management System

Module 8: Advanced Python Features (3 Hours)

• Python Advanced Features
-> Python Decorators
-> Python Iterators
-> Python Generators
• Understanding Design Patterns
-> Creational, Structural, and Behavioral Patterns
• Mini Project Code Walkthrough: Implementing a Singleton-Based Logger

Module 9: Working with APIs & Data Serialization (3 Hours)

• Python for Serialization
-> Pickle for Serialization and Deserialization
• Working with APIs
-> RESTful API concepts
-> Consuming APIs with Python (requests library)
• Mini Project Code Walkthrough: Automated Weather Report Generator using APIs

Module 10:Concurrency, Parallelism & Web Development (4 Hours)

• Concurrency and Parallelism
-> Asynchronous programming (asyncio)
-> Multiprocessing
-> Creating Multiple Threads
• Python Web Frameworks
-> Streamlit for interactive Web Apps
-> Flask Web Framework Basics
-> Django Web Framework Basics
-> FastAPI for Modern Web APIs
• Mini Project Code Walkthrough: Creating a Flask API for JSON Data
• Project 2: Building a Real-World Web Application
-> Developing a Blog App using Django
-> Connecting to a database and implementing authentication

Module 11: Deployment & Final Capstone Project (5 Hours)

• Deployment
-> Basic server concepts
-> Deploying Python web applications
• Capstone Project: End-to-End Python Application

Course Modules

Module 1: Fundamentals of Statistics for Machine Learning (6 Hours)

1. Descriptive Statistics (1.5 Hours)

Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, Standard Deviation, IQR)
Measures to Describe Shape (Skewness, Kurtosis)
Code Walkthrough: Computing descriptive statistics using Python (NumPy, Pandas)
Case Study: Customer spending analysis

2. Correlation s Probability (2 Hours)

Correlation (Types, Pearson, Spearman, Scatter Diagram)
Probability Basics (Types of Events, Probability Types, Bayes Theorem)
Code Walkthrough: Visualizing correlation with Pandas and Seaborn
Case Study: Relationship between ad spending C sales

3. Inferential Statistics s Probability Distributions (2.5 Hours)

Estimation C Hypothesis Testing
Discrete Distributions (Uniform, Binomial, Poisson)
Continuous Distributions (Exponential, Normal, Standard Normal, Student’s T)
Code Walkthrough: Implementing probability distributions with SciPy
Case Study: Predicting loan default probabilities

Module 2: Python Libraries for Data Science (5 Hours)

· Introduction to Python for ML (1 Hour)

Python Basics,C Jupyter Notebook

· Data Manipulation s Visualization (4 Hours)

NumPy (1 Hour)
Pandas (1 Hour)
Matplotlib C Seaborn (1 Hour)
Code Walkthrough: Data preprocessing and visualization
Case Study: Analyzing stock market trends

Module 3: Introduction to AI & ML (3 Hours)

Overview of AI C ML
Applications of AI in Real World
AI Project Lifecycle
Types of Learning (Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning)
AI Ethics C Bias

Case Study: AI-powered product recommendations

Module 4: Foundations of Machine Learning (3 Hours)

· Building an ML Model (2 Hours)

Data Preprocessing (Handling Missing Data, Outliers, Noisy Data)
Overfitting vs Underfitting

· Evaluation Metrics (1 Hours)

Regression Metrics (MAE, MSE, R2 Score)
Classification Metrics (Accuracy, Precision, Recall, F1-Score)

Code Walkthrough: Implementing ML models with Scikit-Learn
Case Study: Credit card fraud detection

Module 5: Supervised Learning (6 Hours)

Regression Algorithms (2.5 Hours)

Linear Regression, Polynomial Regression
Ridge Regression, Lasso Regression, ElasticNet Regression
Code Walkthrough: Regression models with Scikit-Learn

Classification Algorithms (3.5 Hours)

Decision Tree, Random Forest, SVM, Naive Bayes, KNN
Handling Imbalanced Data
Code Walkthrough: Classification models with Scikit-Learn

Case Study: Email spam classification

Module 6: Unsupervised Learning (6 Hours)

· Clustering (2.5 Hours)

K-Means, Hierarchical, DBSCAN
Code Walkthrough: Clustering algorithms in Python

· Dimensionality Reduction (1 Hour)

PCA, t-SNE

· Association Rule Mining (1 Hour)

Apriori, F-P Growth Algorithm

· Anomaly Detection (1.5 Hours)

Isolation Forest, One-Class SVM

Case Study: Customer segmentation for targeted marketing

Module 7: Feature Engineering & Selection (6 Hours)

· Feature Engineering (3 Hours)

Encoding (Label, One-Hot, Count Encoding, Mean Encoding, Word2Vec)
Feature Interaction, Datetime Features, Text Features

· Feature Selection (3 Hours)

Filter, Wrapper, Embedded Methods
RFE, Decision Tree-Based Methods (XGBoost, LightGBM

· Code Walkthrough: Feature Engineering using Pandas C Scikit-Learn

· Case Study: Enhancing predictive performance in healthcare data

Module 8: Optimization & Model Building (5 Hours)

Loss Functions s Gradient Descent (2 Hours)

Batch Gradient Descent, Stochastic, Mini-Batch

Hyperparameter Optimization (2 Hours)

Grid Search, Random Search, Bayesian Optimization

Model Selection (1 Hour)

Cross-validation techniques

Code Walkthrough: Implementing Gradient Descent
Case Study: Hyperparameter tuning for optimal stock price prediction

Module 9: Advanced Techniques in Machine Learning (6 Hours)

· Ensemble Learning (3 Hours)

Bagging (Random Forest, Bootstrap Aggregation)
Boosting (GBM, XGBoost, LightGBM, CatBoost)

· Transfer Learning s Time Series Analysis (3 Hours)

ARIMA, SARIMA, Prophet
Feature Engineering for Time Series Data

Code Walkthrough: Forecasting using Prophet
Case Study: Predicting customer churn

Module 10: Model Interpretability & Deployment (6 Hours)

· Explainability s Interpretability (2 Hours)

SHAP, LIME, Counterfactual Explanations, Saliency Maps

· Model Deployment (2.5 Hours)

Flask, Django, Streamlit
APIs C Endpoints

· Ethics s Governance in AI (1.5 Hours)

AI Fairness C Transparency
Bias Mitigation Strategies
Ethical Case Studies

Code Walkthrough: Deploying an ML model using Flask

Module 11: End-of-Course Case Studies (3 Hours)

Predictive Modeling
Customer Segmentation (Clustering)
Fraud Detection (Anomaly Detection)
Time Series Forecasting
Model Deployment
Code Walkthrough: Implementing case studies end-to-end

Module 12: Capstone Project (5 Hours)

· Problem Selection (1 Hour)

Choose a real-world ML problem

· Project Planning (1 Hour)

Data collection, preprocessing, model selection

· Model Training s Evaluation (1 Hours)

Feature Engineering, Model Optimization, Interpretation

· Deployment (1 Hour)

Deploy the model as a web API using Flask/Streamlit

· Presentation (1 Hour)

Showcase project findings and key learnings

Modules

Module 1: Deep Learning Essentials (5 Hours)

Understanding the Core of Generative AI

• What is AI, Machine Learning, and Deep Learning?
• Evolution of AI: From Rule-Based Systems to Generative AI
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Introduction to Neural Networks: Perceptron, MLP, Backpropagation
• Activation Functions: Sigmoid, ReLU, Softmax
• Optimization Algorithms: Gradient Descent, Adam, RMSProp

Code Walkthrough:

• Implementing Perceptron and MLP using NumPy
• Implementing Activation Functions in Python
• Backpropagation and Gradient Descent in TensorFlow

Module 2: Exploring Neural Network Architectures (5 Hours)

Understanding the Mathematical Backbone of Generative AI

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and LSTMs for Sequence Generation
• Transformers: The Backbone of Modern Generative AI
• Transfer Learning and Fine-Tuning in Deep Learning
• Introduction to PyTorch & TensorFlow for Generative AI

Code Walkthrough:

• Building a CNN using TensorFlow
• Implementing RNNs and LSTMs for Text Generation
• Introduction to Transformers using Hugging Face

Module 3: Generative Models - VAEs, GANs, and Diffusion (5 Hours)

Introduction to Generative AI Models

• Difference Between Generative and Discriminative Models
• Statistical Foundation: Probability Distributions, KL Divergence
• Introduction to Generative Adversarial Networks (GANs)
• Introduction to Variational Autoencoders (VAEs)
• Introduction to Diffusion Models

Code Walkthrough:

• Implementing Gaussian and Multinomial Distributions
• Simple GAN Model Implementation
• Training a Variational Autoencoder (VAE) for Image Generation

Module 4: Retrieval-Augmented Generation (RAG) (4 Hours)

Enhancing Generative AI with External Knowledge

• Introduction to RAG: Why It’s Important
• Differences Between Traditional LLMs and RAG
• Vector Databases: FAISS, ChromaDB for Retrieval
• Fine-Tuning LLMs with External Knowledge

Code Walkthrough:

• Implementing RAG using FAISS
• Connecting LangChain with a Vector Database
• Using OpenAI API with RAG for Document-Based Chatbots

Module 5: Advanced Generative Adversarial Networks (GANs) (5 Hours)

Adversarial Learning for Data Generation

• Introduction to GANs: Generator vs. Discriminator
• Minimax Loss Function in GANs
• Different Types of GANs:
– DCGAN (Deep Convolutional GAN)
– CGAN (Conditional GAN)
– CycleGAN (Image-to-Image Translation)
– StyleGAN (High-Resolution Image Generation)
• Addressing Mode Collapse and Training Challenges

Code Walkthrough:

• Implementing a Basic GAN Model
• DCGAN for Image Generation
• Conditional GAN for Class-Specific Image Synthesis
• Implementing CycleGAN for Image-to-Image Translation

Module 6: Advanced Image Generation with Diffusion Models & CLIP (4 Hours)

Denoising-Based Image Synthesis

• Introduction to Diffusion Models
• Noise Schedules and Reverse Diffusion Process
• Understanding Stable Diffusion & OpenAI’s DALL-E
• Implementing Denoising Diffusion Probabilistic Models (DDPM)
• Contrastive Language-Image Pretraining (CLIP)

Code Walkthrough:

• Implementing a Simple Diffusion Model for Image Generationf
• Using CLIP for Image-Text Matching

Module 7: Reinforcement Learning Essentials & Advanced RL (5 Hours)

Building Decision-Making AI Systems

• Q-Learning & Bellman Equation
• Multi-Armed Bandits for Decision Making
• Policy Gradients (REINFORCE Algorithm)
• Actor-Critic Methods for Deep RL

Code Walkthrough

• Implementing Q-Learning from Scratch
• Training a Policy Gradient Model
• Implementing Actor-Critic in PyTorch

Module 8:Reinforcement Learning with Human Feedback (RLHF) (5 Hours)

Optimizing AI Models with Human Preferences

• Introduction to RLHF and Its Importance
• Reward Models and Human Feedback
• Fine-Tuning LLMs with RLHF
• OpenAI’s Approach to RLHF in GPT Models

Code Walkthrough:

• Implementing RLHF with PPO (Proximal Policy Optimization)
• Fine-Tuning a GPT Model with RLHF for Text Generation
• Training a Reward Model with Human Preferences

Module 9: Prompt Engineering (3 Hours)

Optimizing Interactions with AI Models

• Understanding Prompt Engineering
• Zero-shot, Few-shot, and Chain-of-Thought Prompting
• Creating Effective Prompts for GPT Models
• Hands-on with OpenAI’s GPT-4, LangChain

Code Walkthrough:

• Designing Prompts for Optimized LLM Output
• Testing Prompt Variations for AI Model Performance

Module 10: AI Model Evaluation & Validation (3 Hours)

Assessing Generative AI Performance

• Metrics for Evaluating Generative Models
• BLEU, ROUGE, Perplexity for LLMs
• FID, IS Scores for Image Models
• Evaluating RLHF and RAG-based models

Code Walkthrough:

• Evaluating Text Generation Outputs
• Implementing Image Model Quality Metrics

Module 11: AI in Industry Applications (3 Hours)

Real-World Generative AI Use Cases

• AI in Healthcare, Finance, Retail, and Creative Industries
• Real-world Success Stories (e.g., AI in Customer Support, Financial Forecasting)
• Enterprise AI Adoption Challenges

Case Studies:

• AI in Medical Diagnosis and Drug Discovery
• Financial AI Fraud Detection

Module 12: AI Ethics and Responsible Use (3 Hours)

Building Fair and Transparent AI

• Fairness, Accountability, and Transparency
• Bias Detection and Mitigation Techniques
• AI Governance and Policy Considerations

Case Studies:

• Detecting Bias in AI Models
• Implementing a Simple Deepfake Detection Model

Module 13: Fine-Tuning & Deploying Generative AI Models (5 Hours)

Topics

Optimizing Models for Production Use
• Customizing Pre-Trained LLMs for Enterprise Use
• Memory Optimization Techniques for Large Models
• Deploying AI Models on AWS, Azure

Code Walkthrough:
• Deploying a Hugging Face Model on AWS
• Fine-Tuning GPT-3 on Custom Datasets

Capstone Project (5 Hours)

Hands-On AI Model Development
• Project Walkthrough
• Real-World AI Implementation
• Doubts Clarification
Code Walkthrough:
• End-to-End AI Model Development
• Deploying the Model on Cloud

Course Modules

Basic Topics in Python

Getting Started with Python

Installation of Anaconda Distribution
Python Tools & Technologies

Python IDEs Overview

Comparison of popular IDEs (PyCharm, VS Code, Thonny, etc.)
Setting up Python Virtual Environments

Working with Jupyter-Lab

Line Magic Commands
Cell Magic Commands
Markdown Language

Fundamentals of Python

Python Basics

Indentation
Comments
Variables
Data Types
Type Casting
Operators
Input and Output
Control Flow Statements
▪ if-else
▪ while
▪ for loop
Range Statement
Strings
Functions
▪ Nested Functions
▪ Closure Functions
▪ Lambda Functions
Higher-Order Functions
▪ Filter
▪ Map
▪ Reduce
Data Structures
▪ Lists
▪ Basics of List
▪ Advanced List Manipulation (List Comprehension, Slicing
etc)
▪ Tuples
▪ Sets
▪ Dictionary
▪ Performance considerations
Shallow and Deep Copy
Testing and Debugging
▪ Writing unit tests (unittest or pytest)
▪ Debugging techniques
▪ Python Errors and Exception Handling
File Handling in Python
▪ Working with CSV, Excel, and PDF files
▪ Advanced file handling techniques
Built-in Modules or Libraries
▪ Datetime
▪ Math
▪ Statistics
▪ Random
▪ os and sys (system interaction)
▪ itertools and functools (functional programming)
▪ collections (specialized data structures)
▪ logging (handling logs)

Modules, Libraries and Packages

Working with External Libraries:

▪ Introduction to pip
▪ Installing and using packages (e.g., requests, beautifulsoup4)

Pythonic Practices

▪ Idiomatic Python code
▪ Code style and readability (PEP 8)
▪ Zen of Python

Connecting to Database from Python

▪ SQLite 3 for Python
▪ Using MySQL with Python
▪ Using MongoDB with Python

Project 1 – Developing a Real-World Console Based Python Application

Advanced Topics in Python

Object-Oriented Programming (OOP) in Python

Introduction to Python Object-Oriented Programming
Classes, Objects, and Constructors
Static Methods and Class Methods
Inheritance and Multiple Inheritance
Overriding
Properties in Python
Abstract Classes and Methods
Polymorphism in Python
Magic Functions

Python Advanced Features

Python Decorators
Python Iterators
Python Generators

Understanding Design Patterns

Creational, Structural and Behavioral Patterns

Python for Serialization

Pickle for Serialization and Deserialization

Python for Advanced Functionalities

Regular Expressions
Working with APIs
▪ RESTful API concepts
▪ Consuming APIs with Python – Requests Library (for HTTP requests)
Beautiful Soup (for parsing HTML and XML documents, web scraping)

Concurrency and Parallelism

Asynchronous programming (asyncio)
Multiprocessing
Creating Multiple Threads

Python Web Frameworks

Working with Flash using Python
Working with Django using Python
Working with FastAPI using Python

Deployment

Basic server concepts
Deploying Python web applications

Project 2 – Building a real-world Web Application

Course Modules

1. Basics of Statistics

1. Descriptive Statistics
• Measures of Central Tendency (Averages):
-Mean,Median &Mode
• Measures of Dispersion:
-Range, Percentiles, Quartiles, Interquartile Range (IQR),Variance & Standard Deviation
• Measures to Describe the Shape of Distribution:
-Skewness & Kurtosis

2. Measures of Correlation
• Correlation:
-Importance of Correlation, Types of Correlation & Degree of Correlation
• Methods to Measure Correlation:
– Scatter Diagram, Karl Pearson’s Coefficient of Correlation & Spearman’s Rank Correlation Coefficient

3. Probability for Statistics
• Types of Events:
-Independent Events, Dependent Events, Mutually Exclusive Events & Inclusive Events
• Types of Probability:
-Marginal Probability,Joint Probability & Conditional Probability
• Bayes Theorem

4. Inferential Statistics
• Estimation
• Hypothesis Testing

5. Probability Distributions
• Discrete Distributions:
-Uniform Distribution, Binomial Distribution & Poisson Distribution
• Continuous Distributions:
-Exponential Distribution,Normal Distribution (Gaussian Distribution) (Bell Curve),Standard Normal Distribution (Z-distribution) & Student’s T-Distribution

2. Introduction to Python Libraries for Data Science

• NumPy: Numerical Computing
• Pandas: Data Manipulation
• Matplotlib: Data Visualization
• Seaborn: Statistical Data Visualization

3. Introduction to Artificial Intelligence and Machine Learning

• Overview of AI and ML: Foundations of AI and its capabilities.
• Applications of AI: Real-world use cases of Artificial Intelligence.
• AI Project Life Cycle: Key stages in the development and deployment of AI projects.
• Types of Learning:
-Supervised Learning: Predict outcomes using labeled data
-Unsupervised Learning: Discover patterns without labels
-Semi-Supervised Learning: Leverage labeled and unlabeled data
-Reinforcement Learning: Learn optimal actions using rewards.
• AI Ethics and Bias: Ensuring fairness and reducing bias in AI systems.

4. Foundations of Machine Learning

• Steps to Build an ML Model: End-to-end ML model creation process.
• Overfitting vs Underfitting: Balancing model complexity for better predictions.
• Data Preprocessing: Handling missing values, outliers, and noisy data.
• Evaluation Metrics:
-Regression Metrics: MAE, MSE, R2 Score.
-Classification Metrics: Accuracy, Precision, Recall, F1-Score.

5. Supervised Learning

• Regression Algorithms:
– Linear Regression, Polynomial Regression.
– Ridge Regression (L2 Regularization), Lasso Regression (L1 Regularization).
– ElasticNet Regression (L1 and L2 Regularization).
• Classification Algorithms:
– Decision Tree, Random Forest, SVM.
– Naive Bayes, K-Nearest Neighbors (KNN).
• Advanced Topics: Handling imbalanced data and class weighting

6. Unsupervised Learning

• Clustering:
– K-Means Clustering, Hierarchical Clustering.
– DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
• Dimensionality Reduction:
– Principal Component Analysis (PCA), t-SNE (t-Distributed Stochastic Neighbor
Embedding).
• Association Rule Mining:
– Apriori Algorithm, F-P Growth Algorithm.
• Anomaly Detection:
– Isolation Forest, One-Class SVM.
• Advanced Clustering: Spectral Clustering and Affinity Propagation.

7. Feature Engineering

• Encoding Techniques:
– Label Encoding, One-Hot Encoding.
– Count Encoding, Mean Encoding, Weight of Evidence Encoding.
• Feature Interaction: Creating new features from existing data.
• Datetime Functions: Extracting useful features from time data.
• Text Features: Tokenization and text vectorization (e.g., Word2Vec).

8. Feature Selection

• Filter Methods: Removing irrelevant features based on metrics.
• Wrapper Methods:
– Forward Selection, Backward Elimination.
– Recursive Feature Elimination (RFE).
• Embedded Methods:
– Ridge Regression, Lasso Regression, ElasticNet.
– Decision Tree-Based Methods (e.g., Random Forest, XGBoost, LightGBM).

9. Optimization and Model Building

• Loss Functions: Quantifying the error in predictions.
• Gradient Descent:
– Batch Gradient Descent, Stochastic Gradient Descent (SGD).
– Mini-Batch Gradient Descent.
• Hyperparameter Optimization:
– Grid Search, Random Search, Bayesian Optimization.
• Model Tuning: Fine-tuning hyperparameters for improved accuracy.

10. Advanced Techniques

• Ensemble Learning:
– Bagging: Random Forest and Bootstrap Aggregation.
– Boosting: Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost.
• Transfer Learning: Reusing pretrained models for new tasks.
• Time Series Analysis:
– ARIMA and SARIMA Models, Prophet for Forecasting.
– Feature Engineering for Time Series Data.

11. Explainability and Interpretability

• SHAP (SHapley Additive exPlanations): Explaining feature impacts on predictions.
• LIME (Local Interpretable Model-agnostic Explanations): Simplifying complex models
for human interpretation.
• Advanced Tools: Counterfactual Explanations and Saliency Maps.

12. Model Deployment and Real-World Applications

• Tools for Deployment:
– Deploying with Flask, Django, Streamlit.
• APIs and Endpoints: Building interfaces for model access.
• Real-World Application Projects: End-to-end deployment of ML solutions.

13. Ethics and Governance in AI

• AI Fairness and Transparency: Ensuring equitable AI decisions.
• Bias Mitigation Techniques: Strategies to reduce biases in AI systems.
• Ethical Use Cases: Real-world examples addressing ethical challenges.

Projects

• Hands-on Real-World Applications:
1. Predictive Modeling.
2. Customer Segmentation (Clustering).
3. Fraud Detection (Anomaly Detection).
4. Time Series Forecasting.
5. Model Deployment Project.

Modules

Module 1: Introduction to AI and Deep Learning Fundamentals

Topics

Understanding the Core of Generative AI
• What is AI, Machine Learning, and Deep Learning?
• Evolution of AI: From Rule-Based Systems to Generative AI
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Introduction to Neural Networks: Perceptron, MLP, Backpropagation
• Activation Functions: Sigmoid, ReLU, Softmax
• Optimization Algorithms: Gradient Descent, Adam, RMSProp

Module 2: Advanced Deep Learning Concepts

Topics

Understanding the Mathematical Backbone of Generative AI
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and LSTMs for Sequence Generation
• Transformers: The Backbone of Modern Generative AI
• Transfer Learning and Fine-Tuning in Deep Learning
• Introduction to PyTorch & TensorFlow for Generative AI

Module 3: Generative Models - Understanding the Basics

Topics

Introduction to Generative AI Models
• Difference Between Generative and Discriminative Models
• Statistical Foundation: Probability Distributions, KL Divergence
• Introduction to Generative Adversarial Networks (GANs)
• Introduction to Variational Autoencoders (VAEs)
• Introduction to Diffusion Models

Module 4:Variational Autoencoders (VAEs)

Topics

Understanding Latent Space Representations
• What are Autoencoders?
• Introduction to VAEs: Encoder-Decoder Structure
• KL Divergence Regularization in VAEs
• Reparameterization Trick for Backpropagation
• Generating New Data from Latent Space

Module 5: Generative Adversarial Networks (GANs) - Fundamentals & Advanced

Topics

Adversarial Learning for Data Generation
• Introduction to GANs: Generator vs. Discriminator
• Minimax Loss Function in GANs
• Different Types of GANs:
– DCGAN (Deep Convolutional GAN)
– CGAN (Conditional GAN)
– CycleGAN (Image-to-Image Translation)
– StyleGAN (High-Resolution Image Generation)
• Addressing Mode Collapse and Training Challenges

Module 6: Diffusion Models - The Next Evolution

Topics

Denoising-Based Image Synthesis
• Introduction to Diffusion Models
• Noise Schedules and Reverse Diffusion Process
• Understanding Stable Diffusion & OpenAI’s DALL-E
• Implementing Denoising Diffusion Probabilistic Models (DDPM)

Module 7: Large Language Models (LLMs) & Transformers

Topics

Scaling Up Text Generation
• Self-Attention Mechanism in Transformers
• Understanding GPT Models (GPT-3, GPT-4)
• BERT vs. GPT: Key Differences
• Fine-Tuning LLMs for Domain-Specific Use Cases

Module 8:Multimodal Generative AI (Text, Image, Audio, Video)

Topics

Combining Multiple AI Modalities
• Introduction to Multimodal AI
• CLIP (Contrastive Language-Image Pretraining)
• DALL-E 2: Text-to-Image Generation
• Speech and Music Generation with AI (Tacotron, Jukebox)

Module 9: Fine-Tuning & Deploying Generative AI Models

Topics

Optimizing Models for Production Use
• Customizing pre-trained LLMs for Enterprise Use
• Memory Optimization Techniques for Large Models
• Deploying AI Models on AWS, Azure, GCP

Module 10: Ethical AI, Bias, and AI Governance

Topics

Building Responsible AI Systems
• Bias in Generative AI & Mitigation Strategies
• AI Regulation & Compliance: GDPR, CCPA
• Deepfake Detection & AI Misinformation Prevention

Final Project: Complete a comprehensive AI/ML project.
Capstone Presentation: Showcase your project, insights, and model demonstrations.
Internship Certificate: Internship completion with hands-on experience in live industry projects.
Project Portfolio: Portfolio with code, project documentation, and final reports.

100% Placement Assistance

Internships & Experience

360° Skills Enablement

Frequently Asked Questions about AI & ML course in Hyd

What is an AI/ML course?

AI/ML (Artificial Intelligence / Machine Learning) is an evolving and high-demand field
in IT, where you learn how to build intelligent systems capable of data-driven decision
making, predictions, and automation. The course focuses on key AI/ML concepts, such as
data preprocessing, model building, and applying advanced techniques to real-world
problems. You’ll work on various tools and frameworks like Python, TensorFlow, Scikitlearn, and more.

What is the duration of the training?

There are two programs for AI/ML training:

Advanced AI-ML course – 60 Hours
Advanced GenAI Course – 60 Hours
Python – 40 Hours

What is the prerequisite for these training programs?

Basic AI/ML Course (3.5 months):

Any graduate can enroll.
Basic coding skills and basic maths required.
Basic knowledge of programming (Python) or willingness to learn.

Advanced AI/ML Course (6 months):

Any graduate can enroll.
Some basic understanding of programming and mathematics (especially linear algebra and statistics) is beneficial.
Ideal for those who have completed the basic course or have some experience in programming and are eager to learn advanced AI/ML techniques.

What are the benefits after completing the training?

You will be ready to work on real-world AI/ML projects, with the ability to develop intelligent systems and deploy machine learning models.
You’ll be equipped to crack interviews at top companies in sectors like IT, finance, healthcare, e-commerce, and more.
You will gain the hands-on experience necessary to transition into AI/ML roles.

What is the fee of the course?

For detailed fee structure and Payment options- Contact us at 👇

What are other competitor courses? Why AI/ML from us?

Yes, there are other AI/ML courses offered by platforms like Coursera, edX, and Udemy. However, our course stands out because:

We provide real-world datasets and work on practical AI/ML projects.
Our instructors are industry experts with years of hands-on experience.
We offer placement assistance, career counselling, and internship opportunities.
Our training includes live sessions, personalized guidance, and mock interview preparation.

How experienced are the trainers at AI/ML Learnersbyte?

We have over 5 years of experience in AI/ML and data science training. Our team comprises experts who have worked on cutting-edge AI/ML projects across various industries.

Do you have real-world datasets? If yes, which industry verticals?

Yes, we provide real-time datasets across various industries:

Healthcare: Patient data, disease prediction, etc.
Finance: Stock prediction, fraud detection, etc.
E-commerce: Recommendation systems, sales forecasting, etc.
Manufacturing: Predictive maintenance, supply chain optimization, etc.

What kind of projects will I work on?

During the course, you will work on projects that span multiple domains:

Business Intelligence: Building machine learning models for sales forecasting and customer churn prediction.
Healthcare: Implementing disease detection systems and healthcare data analytics.
Finance: Stock price prediction, fraud detection, and credit scoring models.
E-commerce: Building recommendation systems, customer segmentation models.
Natural Language Processing (NLP): Text classification, sentiment analysis, and chatbot creation.
Computer Vision: Image recognition, object detection, and facial recognition models.

Do you provide placement assistance?

Yes, we provide career guidance, including:

Resume building
CV uploading on job portals
Mock interview sessions
References and networking opportunities through our alumni network.

Do you provide internships?

Yes, we offer internship programs (up to 6 months) for:

Final-year students, college graduates, and freshers.
Those looking to switch careers to AI/ML in the IT industry.
Internship certificates are provided upon completion.

Does your course cover projects? What will be the duration?

Yes, both the Basic and Advanced courses cover real-world projects. • Basic Course includes 2-3 smaller projects with real-world datasets.

Advanced Course includes larger, more comprehensive projects with complex datasets across various domains.
Duration and fees for projects are included in the course. Custom projects are available for those seeking specific skills.

Details for a Free Consultation

Hear from our Alumni

The trainers explained every topic slowly and clearly, making sure even beginners like me understood. The best part was the practical exercises

Madhu went over and beyond and patiently went through the course material catering to individual nuances. Highly recommended for anyone looking to make transition to cloud.

Such well versed with subject and experience , Madhu can make aws learning seamless and exciting