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AI & ML Training with Real-Time Projects

Artificial Intelligence & Machine Learning Program

Learn Artificial Intelligence, Machine Learning & Deep Learning from Basics to Advanced

Build strong foundations in Artificial Intelligence, Machine Learning, and Deep Learning through real-time projects and practical implementation. Gain job-ready skills for AI and ML roles from beginner to advanced level.

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Batch Details & Schedule

Choose a learning format and schedule that fits your lifestyle

Next Batch Starts

18th May 2026

Session Time

07:20 AM to 08:20 AM

Course Duration

4–5 months

Download Complete Curriculum

Course Features & Highlights

Everything you need to become a successful AI & ML professional

Live Projects

Work on real-world projects from day one with industry use cases

Expert Trainers

Learn from industry professionals with 10+ years of experience

100% Placement Support

Dedicated placement cell with assistance until you get hired

Industry Certification

Get certified and boost your resume with recognized credentials

LMS Access

Access to learning materials and recorded sessions

Mock Interviews

Weekly mock interviews to prepare you for real job scenarios

Resume Building

Professional resume preparation and LinkedIn profile optimization

Soft Skills Training

Communication, aptitude, and personality development sessions

Complete Course Curriculum

Comprehensive modules covering every aspect of AI & ML

  • Module 1: Fundamentals of Artificial Intelligence & Machine Learning
    • Introduction to AI & ML
      • Artificial Intelligence Fundamentals
      • Machine Learning Concepts
      • Types of Machine Learning
      • Real-World AI Applications
    • Python for AI & ML
      • Python Basics
      • Data Types & Functions
      • NumPy & Pandas
      • Data Manipulation Techniques
    • Mathematics for ML
      • Statistics Fundamentals
      • Probability Concepts
      • Linear Algebra Basics
      • Data Visualization
  • Module 2: Data Preprocessing & Feature Engineering
    • Data Cleaning
      • Handling Missing Values
      • Outlier Detection
      • Data Transformation
      • Data Normalization & Standardization
    • Exploratory Data Analysis (EDA)
      • Data Visualization Techniques
      • Pattern Identification
      • Correlation Analysis
    • Feature Engineering
      • Feature Selection
      • Feature Extraction
      • Encoding Techniques
      • Dimensionality Reduction
  • Module 3: Machine Learning Algorithms
    • Supervised Learning
      • Linear Regression
      • Logistic Regression
      • Decision Trees
      • Random Forest
      • Support Vector Machines (SVM)
      • K-Nearest Neighbors (KNN)
    • Unsupervised Learning
      • K-Means Clustering
      • Hierarchical Clustering
      • Principal Component Analysis (PCA)
    • Model Evaluation
      • Accuracy Metrics
      • Confusion Matrix
      • Precision & Recall
      • Cross Validation
  • Module 4: Deep Learning
    • Neural Networks
      • Introduction to Deep Learning
      • Artificial Neural Networks (ANN)
      • Activation Functions
      • Forward & Backpropagation
    • Deep Learning Architectures
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
      • LSTM Networks
      • TensorFlow & PyTorch
    • Model Building
      • Training Deep Learning Models
      • Practical Implementation
  • Module 5: Natural Language Processing (NLP)
    • NLP Fundamentals
      • Text Preprocessing
      • Tokenization
      • Stop-word Removal
      • Stemming & Lemmatization
    • Text Representation
      • Bag of Words (BoW)
      • TF-IDF
      • Word Embeddings
    • NLP Applications
      • Sentiment Analysis
      • Text Classification
      • Chatbot Development
  • Module 6: Real-World AI & ML Projects
    • Machine Learning Projects
      • House Price Prediction
      • Sales Forecasting
      • Customer Churn Prediction
      • Recommendation Systems
    • AI Applications
      • Face Recognition
      • Spam Detection
      • Fraud Detection
      • AI Chatbots
    • Deep Learning Projects
      • Image Classification
      • Object Detection
      • Text Generation Systems
  • Module 7: Practical Implementation & Interview Preparation
    • Hands-On Coding Sessions
      • End-to-End ML Pipeline Development
      • Model Training & Deployment
      • Real-Time Case Studies
    • Interview Preparation
      • AI & ML Interview Questions
      • Mock Interviews
      • Project Discussions
      • Resume Building Support

Prerequisites & Eligibility

Course Prerequisites
Basic understanding of programming concepts and Python fundamentals is beneficial
Dedication to complete hands-on labs, case studies, and capstone projects
Commitment for the program duration (45 days, 70 hours)

Certification

Get industry-recognized certification that validates your skills and boosts your career prospects

  • Industry-recognized AI & ML course completion certificate from Avowal Data Systems
  • Digital certificate with a unique verification ID for easy authenticity checks
  • Covers AI fundamentals, machine learning, deep learning, NLP, and real-world AI project implementation
  • Shareable on LinkedIn, resumes, portfolios, and other professional platforms
  • Valid proof of practical AI & ML skill development for employers, recruiters, and hiring teams
  • Includes project-based learning recognition and full course syllabus coverage
Frequent Questions

Find answers to common questions about our AI & ML training program

  • What will I learn in this AI & ML course?

    This course covers AI fundamentals, machine learning concepts, supervised and unsupervised learning, deep learning, NLP basics, real-world AI applications, and practical project implementation using industry-relevant tools.

  • Is prior programming knowledge required for an AI & ML course?

    Basic programming knowledge is helpful, especially Python fundamentals, but the course begins with core concepts and gradually progresses into advanced AI, machine learning, and deep learning techniques.

  • Does this AI & ML course include hands-on projects and practical sessions?

    Yes, the course includes live practical sessions, coding exercises, project discussions, case studies, and hands-on implementation of machine learning models, deep learning systems, and AI-based applications.

  • Will I learn deep learning and NLP as part of this program?

    Yes. The curriculum includes neural networks, CNNs, RNNs, LSTMs, TensorFlow, PyTorch, and NLP fundamentals such as text preprocessing, TF-IDF, word embeddings, sentiment analysis, and chatbot development.

  • What career opportunities are available after completing AI & ML training?

    After completing AI & ML training, learners can pursue roles such as Machine Learning Engineer, AI Engineer, Data Scientist, Deep Learning Engineer, NLP Engineer, Computer Vision Developer, and AI Research Associate.