AI & ML Training in Indore

Essential Subjects for AI Education

An Overview of Artificial Intelligence

knowing the basics of artificial intelligence.

AI’s development and history.

AI’s uses and significance in the modern world.

Learning Machines (ML)

 Reinforcement, unsupervised, and supervised learning.

use data to build and train models.

Common algorithms include K-Nearest Neighbors (KNN), Random Forests, Decision Trees, and Linear Regression.

Model assessment and fine-tuning (hyperparameter optimization, cross-validation).

Deep Learning (DL)

Recognizing neural networks, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

frameworks for creating deep learning models, such as PyTorch, Keras, and TensorFlow.

Deep learning applications in speech and picture identification.

Processing Natural Language (NLP)

methods for interpreting and evaluating spoken language.

Text summarization, language modelling, sentiment analysis, and text analysis.

Tools: BERT, GPT, SpaCy, and NLTK.

Vision in Computers

methods for giving machines the ability to “see” and comprehend pictures and videos.

segmentation, face recognition, object identification, and image classification.

applications in driverless vehicles, healthcare (such as MRI scans), and security.

Key Topics Addressed in Machine Learning  

Fundamental Ideas:

 Recognizing the distinctions between deep learning, machine learning, and artificial intelligence.

Three categories of machine learning exist: reinforcement learning, unsupervised learning, and supervised learning.

ML applications: real-world applications in sectors such as robotics, retail, healthcare, and finance.

Preprocessing of Data

Managing outliers, inconsistent data, and missing values is known as data cleaning.

Data transformation includes categorical variable encoding, scaling, and normalization.

Feature engineering is the process of extracting valuable characteristics from unprocessed data in order to enhance model performance.

Learning Under Supervision

Classification: Algorithms like Support Vector Machines (SVM), Decision Trees, and Logistic Regression are utilised for classification jobs.

Regression: Methods like Ridge Regression and Linear Regression that forecast continuous values.Accuracy, precision, recall, F1 score, confusion matrix, and cross-validation are all used to evaluate the model.

Learning Without Supervision

Clustering is the process of putting related data points into groups using techniques such as hierarchical clustering, DBSCAN, and K-means.

Dimensionality Reduction: Methods for simplifying data, such as Principal Component Analysis (PCA).

Finding odd patterns in data is known as anomaly detection.

Learning via Reinforcement

Fundamentals of Reinforcement Learning: 

Gaining knowledge of agents, settings, incentives, and behaviors.

Algorithms include Policy Gradient Methods, Deep Q-Networks (DQN), and Q-learning.

Applications include recommendation systems, gaming, and robotics.