AI Intermediate Path

Hackify AI Intermediate Path

A 12-week applied AI path for students who want machine learning basics, data preparation, notebook workflow, evaluation thinking, prompt-based workflows, and project-backed confidence without jumping straight into advanced research topics.

Python Workflow Notebook Discipline ML Basics Data Preparation Evaluation Metrics Prompt Workflows

Duration12 Weeks

LevelIntermediate

DeliveryApplied AI + 2 projects

AdmissionAdvisor-reviewed admission

View Full Syllabus Join Now

Certification Overview

Why this learning path is structured this way

The AI Intermediate Path gives students a more practical understanding of how machine learning and applied AI workflows actually fit together. Instead of focusing only on hype, it introduces notebook discipline, evaluation thinking, prompt workflows, and project delivery in a guided way. Students finish with better technical confidence and clearer language for interviews and presentations.

Moves from beginner AI awareness into real applied AI workflow thinking

Introduces machine learning basics without overwhelming students with research-heavy depth

Includes two guided projects to create visible proof of applied AI work

Builds stronger readiness for the advanced 24-week AI Professional Path

Skills and Tools

Execution areas students build through the path

Python Workflow Notebook Discipline ML Basics Data Preparation Evaluation Metrics Prompt Workflows

Content Options

Open each content option to explore the course-wise syllabus

Open each content option below to explore the 3-month AI curriculum, progressing from advanced Python and mathematics into machine learning, NLP, deployment, and portfolio projects.

Balances AI theory, coding foundations, and applied project work
Includes machine learning, deep learning, NLP, generative AI, and deployment topics
Designed for learners ready to move beyond basics into applied AI development

Advanced Python
OOP Concepts
Libraries and APIs

Linear Algebra
Probability
Statistics
Calculus Fundamentals

Data Collection
Data Cleaning
Feature Engineering

Regression Algorithms
Classification Algorithms
Clustering Algorithms
Model Evaluation

Neural Networks
TensorFlow
Keras
PyTorch Basics

Text Processing
Sentiment Analysis
Language Models
Text Classification

Prompt Engineering
Fine Tuning Concepts
RAG Architecture
Vector Databases

Model Deployment
APIs
Streamlit
AI Applications

Recommendation System
NLP Chatbot
Predictive Analytics Model

Hands-On Projects

Project work students complete before certification completion

The intermediate AI path includes two guided projects because visible applied work matters when students want internships, placements, or a stronger step into advanced AI training.

Project 1

Beginner Machine Learning Workflow

Students prepare data, train a beginner model, review evaluation results, and explain the performance in plain language.

Organize the data and workflow structure
Compare simple results and reasoning
Present what the model does well and where it struggles

Project 2

Applied AI Use-Case Project

Students combine prompt workflows or applied model output with a real business or productivity-style case.

Frame the use case clearly
Show how AI supports the task
Explain value, risks, and human review points

Learning Paths

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Hackify AI Intermediate Path