AI & Machine Learning Course Overview

Master the Essentials of AI and Machine Learning: Become a Certified AI Specialist and Shape the Future of Intelligent Systems!

Rohil NextGen offers a comprehensive AI & ML Fundamentals training program covering Machine Learning Basics, Neural Networks, Deep Learning, Natural Language Processing, and Computer Vision.

TOP RATED 4.9 4.9 Ratings

2025

Latest Curriculum

60

Total Hours

25

Theory (Hours)

35

Practical (Hours)

Why Choose AI & ML Certification?

Annual Income

Estimated Salary

₹ 15 L

Career Opportunities

Growth Rate This Year

40%

Industry Demand

Projected by 2026

8 L

Become a Professional in AI & Machine Learning Fundamentals

❝ Learn Machine Learning Models, Neural Networks, Deep Learning, Natural Language Processing, and Computer Vision ❞

Course Overview

This AI & Machine Learning Fundamentals course is designed to teach you the core principles of AI, deep learning techniques, supervised and unsupervised learning, and how to apply these methods to solve real-world problems using Python and popular ML frameworks like TensorFlow and Scikit-Learn.

Key Learning Outcomes

Understand the fundamentals of Machine Learning, AI, and Data Science.
Learn about Deep Learning and Neural Networks for complex data processing.
Explore Natural Language Processing (NLP) for working with text data.
Understand Computer Vision techniques for image and video analysis.
Apply algorithms and frameworks like TensorFlow and PyTorch to build AI models.

Career Prospects with This Course

Become a Machine Learning Engineer or Data Scientist.
Work as an AI Research Scientist or AI Developer in tech industries.
Apply AI in industries like healthcare, fintech, and autonomous vehicles.
Explore academic and research roles in AI and Robotics.
Develop intelligent systems and applications in emerging fields like AI and IoT.

AI Course Syllabus

Phase 1: Python & Math Foundations (Weeks 1–4)

Weeks 1-2: Python Basics

Data types, variables, operators
Control flow (if, for, while)
Functions, modules, and packages
Data structures: lists, tuples, dictionaries, sets
File handling and exceptions

Weeks 3-4: Mathematical Foundations

Linear algebra: matrices, vectors, operations
Probability and statistics basics
Calculus fundamentals: derivatives, gradients
Introduction to optimization techniques

Phase 2: Machine Learning Basics (Weeks 5–10)

Theory

Introduction to Machine Learning
Supervised vs Unsupervised Learning
Regression Algorithms
Classification Algorithms
Clustering Algorithms
Model Evaluation and Validation

Practical

Implementing Linear Regression
Building Classification Models
Clustering with K-Means
Model Evaluation Techniques

Phase 3: Deep Learning Foundations (Weeks 11–14)

Theory

Introduction to Neural Networks
Activation Functions
Backpropagation Algorithm
Optimization Algorithms
Introduction to TensorFlow and Keras

Practical

Building First Neural Network
Implementing Backpropagation
Working with TensorFlow
Model Training and Tuning

Phase 4: Advanced Deep Learning & Computer Vision (Weeks 15–18)

Theory

Convolutional Neural Networks (CNNs)
Image Classification and Object Detection
Transfer Learning
Autoencoders and GANs

Practical

Building CNN for Image Classification
Implementing Transfer Learning
Creating GANs for Image Generation
Computer Vision Projects

Phase 5: Natural Language Processing (NLP) (Weeks 19–22)

Theory

Text Preprocessing and Tokenization
Word Embeddings (Word2Vec, GloVe)
Recurrent Neural Networks (RNNs)
Transformers and BERT
Sentiment Analysis and Text Classification

Practical

Text Preprocessing Pipelines
Building RNNs for Text Generation
Implementing Sentiment Analysis
Working with Transformer Models

Phase 6: Reinforcement Learning & Advanced Topics (Weeks 23–25)

Theory

Introduction to Reinforcement Learning
Markov Decision Processes
Q-Learning and Policy Gradients
Deep Reinforcement Learning
AI Ethics and Responsible AI

Practical

Implementing Q-Learning
Building RL Agents
Training Deep RL Models
Ethical AI Implementation

Phase 7: Capstone Project (Weeks 26–28)

Theory

Project Planning and Design
Data Collection and Preparation
Model Selection and Training
Evaluation and Deployment Strategies

Practical

End-to-end AI Project Implementation
Model Deployment and Testing
Project Documentation and Presentation

Phase 8: Career Preparation (Week 29)

Theory

Resume Building for AI Roles
Interview Preparation Strategies
AI Industry Trends and Opportunities
Professional Development Planning

Practical

Mock Technical Interviews
Portfolio Development
LinkedIn Profile Optimization
Job Application Strategy

Frequently Asked Questions

What is AI & Machine Learning?

AI (Artificial Intelligence) is the simulation of human intelligence in machines, while Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed.

Do I need programming experience for this course?

Basic programming knowledge is helpful but not required. The course starts with Python fundamentals and gradually builds up to advanced AI concepts.

Will I receive a certificate after completing the course?

Yes, upon successful completion of the course and final project, you will receive a certificate from Rohil NextGen validating your AI & Machine Learning skills.

What kind of projects will I work on?

You'll work on real-world projects including image classification, sentiment analysis, predictive modeling, and a comprehensive capstone project that demonstrates your AI skills.

What are the job opportunities after completing this course?

Graduates can pursue roles such as Machine Learning Engineer, Data Scientist, AI Developer, NLP Engineer, Computer Vision Engineer, and AI Research Scientist.

Is there placement assistance after course completion?

Yes, we provide comprehensive placement assistance including resume building, interview preparation, and connections to our hiring partners in the AI industry.