Weekdays Regular Classes -1 Hr/Day
February 21, 2026 10:00 AM GMT+0530 (Monday - Friday)
Weekend Regular Classes -3 Hrs/Day
February 21, 2026 10:00 AM GMT+0530 (Saturday - Sunday)

AI-Foundation Training Syllabus

  • Introduction to AI & ML

    • What is Artificial Intelligence?

    • What is Machine Learning?

    • AI vs ML vs Deep Learning

    • Applications of AI & ML

    • Real-world Use Cases

    • Career Paths in AI & ML

  • Python for AI & ML (Quick Revision)

    • Python Basics for ML

    • Data Types & Control Flow

    • Functions & Modules

    • NumPy Basics

    • Pandas Basics

    • Data Handling with Python

  • Mathematics for AI & ML (Essentials)

    • Scalars, Vectors & Matrices

    • Matrix Operations

    • Transpose & Inverse

    • Dot Product

    • Linear Algebra intuition for ML

  • Statistics for AI & ML (Core Module)

    • Descriptive Statistics

    • Mean, Median, Mode

    • Variance & Standard Deviation

    • Range & Percentiles

    • Skewness & Kurtosis

    • Probability Basics

    • Probability Concepts

    • Random Variables

    • Probability Distributions

    • Conditional Probability

    • Data Distributions

    • Normal Distribution

    • Binomial Distribution

    • Poisson Distribution

    • Central Limit Theorem (Concept)

    • Statistical Inference

    • Sampling Techniques

    • Population vs Sample

    • Bias & Variance

    • Confidence Intervals

    • Hypothesis Testing (Basics)

    • P-value (Conceptual)

    • Correlation & Relationships

    • Correlation vs Causation

    • Covariance

    • Pearson & Spearman Correlation

  • Data Preprocessing & EDA

    • Data Collection

    • Data Cleaning

    • Handling Missing Values

    • Handling Outliers

    • Feature Scaling

    • Feature Encoding

    • Exploratory Data Analysis (EDA)

    • Data Visualization for Insights

  • Supervised Learning

    • Simple Linear Regression

    • Multiple Linear Regression

    • Polynomial Regression

    • Regularization (Ridge, Lasso)

    • Logistic Regression

    • K-Nearest Neighbors (KNN)

    • Support Vector Machine (SVM)

    • Naive Bayes

    • Decision Trees

    • Random Forest

  • Unsupervised Learning

    • K-Means Clustering

    • Hierarchical Clustering

    • DBSCAN

    • Principal Component Analysis (PCA)

    • Dimensionality Reduction

  • Model Evaluation & Performance Metrics

    • Bias-Variance Trade-off

    • Confusion Matrix

    • Accuracy, Precision, Recall, F1-Score

    • ROC Curve & AUC

    • Cross-validation

  • Feature Engineering

    • Feature Creation

    • Feature Selection

    • Feature Transformation

    • Handling Categorical Data

    • Handling Text Data

  • Machine Learning Pipelines

    • ML Workflow

    • Pipeline Creation

    • Hyperparameter Tuning

    • Grid Search & Random Search

  • Introduction to Deep Learning

    • Neural Network Basics

    • Perceptron

    • Activation Functions

    • Loss Functions

    • Gradient Descent

    • Backpropagation (Concept)

  • Deep Learning with Python

    • TensorFlow / Keras Overview

    • Building Neural Networks

    • ANN Models

    • CNN Introduction

    • RNN Introduction

  • Model Deployment Basics

    • Saving & Loading Models

    • Introduction to Flask / FastAPI

    • Deploying ML Models (Overview)

    • Cloud Deployment Basics (AWS)

  • Ethics & Responsible AI

    • Bias in AI

    • Fairness & Transparency

    • Data Privacy

    • Ethical AI Practices

  • AI & ML Projects

    • Beginner ML Projects

    • Intermediate ML Projects

    • Advanced ML Projects

    • End-to-End Capstone Project

  • Interview & Career Preparation

    • ML Interview Questions

    • Statistics Interview Questions

    • Case Studies

    • Resume & Portfolio Building

    • GitHub & LinkedIn Optimization

Frequently Asked Questions

AI Foundational Course Training FAQs

  • Who can attend this training?

    Anyone interested in learning the basics of Artificial Intelligence and Machine Learning, including students, working professionals, job seekers, and individuals from non-technical backgrounds. 

  • How to attend this training?

    You can join the training online through Synkoc’s virtual classroom platform after completing the enrollment process. 

  • What is the exact location?

    This is an online-only course, accessible from anywhere. 

  • What makes Synkoc Training Institute stand out for AI courses?

    Synkoc offers industry-aligned curriculum, hands-on projects, expert trainers, flexible learning options, and strong career support, ensuring high-quality AI education. 

  • Which tools and technologies will I be learning in this course?

    You will learn AI fundamentals, Python basics, ML algorithms, data preprocessing, model building, evaluation techniques, and commonly used tools like Jupyter Notebook and essential Python libraries. 

  • What are the qualifications and industry experience of the AI instructors at Synkoc?

    Our instructors are experienced AI/ML professionals with real-world project exposure and strong academic backgrounds. 

  • Can I access software and tools during the course?

    Yes. All required tools, Python environments, libraries, and setup instructions will be provided. 

  • Are there any prerequisites for the AI Foundational Course?

    No. Basic computer knowledge is sufficient. No prior coding or math background is required. 

  • How hands-on is the AI training at Synkoc?

    The course includes practical exercises, guided labs, datasets, real-time examples, and mini-projects to apply your learning effectively. 

  • Does Synkoc offer any certification upon completion?

    Yes. You will receive a Synkoc AI Foundational Course Certificate after completing the training. 

  • Is the AI course suitable for beginners?

    Absolutely. The course is designed especially for beginners and those starting their AI journey. 

  • Can I access course materials after completing the AI course?

    Yes. You will retain access to essential course notes, recordings (if provided), and study material. 

  • What learning resources are provided by Synkoc?

    You will receive study materials, practice datasets, coding examples, project files, and recommended reference links. 

  • How flexible is the schedule for the AI course at Synkoc?

    We offer flexible online class timings to suit students, working professionals, and part-time learners. 

  • Are there opportunities for networking or career support at Synkoc?

    Yes. You will receive mentorship, resume guidance, interview preparation, and career assistance. 

  • What is Synkoc’s policy regarding technical support for software-related issues?

    Our team provides full technical assistance for installation, environment setup, and troubleshooting during the course. 

  • Can I attend a demo class before enrolling in the AI course?

    Yes. You can book a free demo class to experience the training before joining. 

  • What is Synkoc’s refund or cancellation policy for the AI course?

    The refund or cancellation policy will be provided at the time of registration, based on Synkoc’s training terms.