Overview

A complete AI & Machine Learning program by Dixp.ai designed for BTech, MTech, BCA, MCA students Working Professionals to master end-to-end AI skills — from fundamentals to deployment.

What You Will Learn

• Introduction to AI & ML
• Linear & Logistic Regression
• Decision Trees & Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Neural Networks & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Model Deployment

Program Structure

Module 1: Introduction to AI & Machine Learning

• What is AI, ML, and Deep Learning
• Types of ML: Supervised, Unsupervised, Reinforcement Learning
• AI lifecycle & real-world applications
• Data basics: features, labels, datasets
• Ethics, bias, and responsible AI

Module 2: Linear & Logistic Regression

• Linear regression fundamentals
• Cost functions & gradient descent
• Multiple linear regression
• Logistic regression for classification
• Model evaluation metrics (MSE, RMSE, Accuracy)

Module 3: Decision Trees & Random Forests

• Decision tree structure and splitting criteria
• Overfitting & pruning techniques
• Ensemble learning concepts
• Random Forest algorithm
• Feature importance

Module 4: Support Vector Machines (SVM)

• Maximum margin classifiers
• Kernel trick (Linear, Polynomial, RBF)
• Soft margin & hyperparameters
• Classification vs regression (SVR)

Module 5: K-Nearest Neighbors (KNN)

• Distance metrics (Euclidean, Manhattan)
• K selection strategies
• KNN for classification & regression
• Performance optimization

Module 6: Neural Networks & Deep Learning

• Artificial neural networks (ANNs)
• Activation functions
• Backpropagation & optimization
• Introduction to CNNs & RNNs
• Transfer learning basics

Module 7: Natural Language Processing (NLP)

• Text preprocessing & tokenization
• Bag of Words & TF-IDF
• Word embeddings (Word2Vec, GloVe)
• Sentiment analysis
• Intro to transformers

Module 8: Computer Vision

• Image representation & preprocessing
• Convolutional Neural Networks (CNNs)
• Image classification & object detection
• Transfer learning with pre-trained models

Module 9: Model Deployment

• Model serialization (Pickle, ONNX)
• APIs using Flask/FastAPI
• Edge vs cloud deployment
• Model monitoring & versioning
• Performance optimization

Capstone Project & Certification

Complete a hands-on AI and ML capstone project that simulates real industry challenges, and earn the prestigious Dixp.ai Certified AI & ML Professional certificate.

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