AI & ML

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Best AI & ML Full Stack Coaching Center in Kukatpally, Hyderabad

Greetings from PVRCoding Academy’s Artificial Intelligence and Machine Learning course.The goal of this innovative program is to educate you to the foundations as well as more complex ideas in AI and ML, the most popular programming language in the industry.Regardless of your level of expertise as a developer, this course will provide you the tools you need to succeed in the quickly changing fields of AI and ML.

Introduction to AI & ML

Core Concepts

Key Algorithms and Techniques

Neural Networks and Deep Learning

Natural Language Processing (NLP)

Data Preparation and Processing

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Learning Modules

Introduction to Python Programming
Introduction to Python

Variables, data types, and basic operations

Control structures (if statements, loops)

Functions and modules
Data Structures and Libraries
Lists, tuples, dictionaries, and sets

Introduction to NumPy for numerical computing

Introduction to Pandas for data manipulation
Data Manipulation and Visualization
DataFrames and Series

Data cleaning and preprocessing

Handling missing data and outliers
Data Visualization
Introduction to Matplotlib and Seaborn

Creating various plots (line, bar, scatter, histogram)

Visualizing data distributions and relationships
Introduction to Machine Learning
Fundamentals of Machine Learning

Understanding AI, ML, and Deep Learning

Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

Supervised Learning – Regression

Linear regression

Polynomial regression

Evaluation metrics (MSE, RMSE, R²)

Supervised Learning – Classification

Logistic regression

k-Nearest Neighbors (k-NN)

Decision trees and Random forests

Evaluation metrics (Accuracy, Precision, Recall, F1 Score)
Neural Networks and Deep Learning
Introduction to Neural Networks

Understanding perceptrons and neural networks

Activation functions and loss functions

Training neural networks (forward and backward propagation)

Deep Learning with TensorFlow and Keras

Setting up TensorFlow and Keras

Building and training neural networks

Convolutional Neural Networks (CNNs) for image recognition

Recurrent Neural Networks (RNNs) for sequential data