Master the most transformative technology of the 21st century with our elite Artificial Intelligence (AI) training course. In 2026, AI is no longer just about automation; it is about Generative Intelligence and autonomous agents that redefine how the world works. This comprehensive program is designed to take you from foundational mathematics and Python logic to the cutting edge of Large Language Models (LLMs) and Computer Vision. The curriculum is uniquely optimized for the 2026 job market, featuring deep dives into Prompt Engineering, Retrieval-Augmented Generation (RAG), and MLOps for deploying models at scale
Our Artificial Intelligence training curriculum at MAK Technologies is engineered to meet the elite standards of the 2026 tech ecosystem. The structure transitions from Mathematical foundations and Python logic to advanced Generative Engine Optimization (GEO) and autonomous AI agents. Students gain mastery in Machine Learning pipelines, deep neural architectures, and LLM fine-tuning through intensive, project-based modules. This curriculum is designed to produce high-impact AI Engineers, Data Scientists, and AI Solutions Architects ready for global deployment.
Master linear algebra, calculus, and probability—the essential mathematical DNA of all AI models.
Deep dive into Supervised, Unsupervised, and Reinforcement Learning using Scikit-Learn.
Build complex architectures using PyTorch and TensorFlow, focusing on CNNs for Vision and RNNs for sequence data.
Learn strategic techniques like Chain-of-Thought (CoT) and React to optimize model reliability and reduce hallucinations.
Build enterprise-grade AI systems by integrating LLMs with Vector Databases (Pinecone/Milvus) for live data grounding.
Design autonomous agents capable of tool-calling, multi-step reasoning, and independent task execution..
Learn to scale AI models using Docker, Kubernetes, and CI/CD pipelines for real-world production environments.
Navigate the critical 2026 landscape of bias detection, data privacy, and Responsible AI principles.
Course Content
Using Python for Machine Learning - Introduction to Data Science
1. Understanding Data Science
2. The Data Science Life Cycle
3. Understanding Artificial Intelligence (AI)
4. Overview of Implementation of Artificial Intelligence
5.Machine Learning
6. Deep Learning
7. Artificial Neural Networks (ANN)
8.Natural Language Processing (NLP)
9.How Python connected to Machine Learning
10.Python as a tool for Machine Learning Implementation
Introduction to Python
1. What is Python and history of Python
2.Python-2 and Python-3 differences
3. Install Python and Environment Setup
4.Python Identifiers, Keywords, and Indentation
5.Comments and document interlude in Python
6.Command-line arguments and Getting User Input
7.Python Basic Data Types and Variables
List, Ranges & Tuples in Python
1. Understanding Lists in Python
2. Understanding Iterators
3. Generators, Comprehensions and Lambda Expressions
4.Understanding and using Ranges
Python Dictionaries And Sets
1. Introduction to the section
2.Python Dictionaries and More on Dictionaries
3. Python Dictionaries and More on Dictionaries
Input and Output in Python
1. Reading and writing text files
2.Appending to Files
3. Writing Binary Files Manually and using Pickle Module
Python functions
1. Python user defined functions
2.Python packages functions
3. The anonymous Functions
4. Loops and statement in Python
5.Python Modules & Packages
Python Exceptions Handling
1. What is Exception?
2.Handling an exception
3. try….except…else
6. try-finally clause
6.The argument of an Exception
7.Python Standard Exceptions
8.Raising an exception
9. User-Defined Exceptions
Python Regular Expressions
1. What are regular expressions?
2.The match Function and the Search Function
3.Matching vs Searching
4. Search and Replace
5.Extended Regular Expressions and Wildcard
Useful additions
1.Collections -named tuples, default dicts
2.Debugging and breakpoints, Using IDEs
Data Manipulation using Python
1.Understanding different types of Data
2.Understanding Data Extraction
3.Managing Raw and Processed Data
4.Wrangling Data using Python
5. Using Mean, Median and Mode
6.Variation and Standard Deviation
7.Probability Density and Mass Functions
8.Understanding Conditional Probability
9.Exploratory Data Analysis (EDA)
10.Working with Numpy, Scipy and Pandas
Understanding Machine Learning Models
1.Understand what is a Machine Learning Model
2.Various Machine Learning Models
3.Choosing the Right Model
4.Training and Evaluating the Model
5. Improving the Performance of the Model
More on Models
1.Understanding Predictive Model
2.Working with Linear Regression
3.Working with Polynomial Regression
4.Understanding Multi Level Models
5.Selecting the Right Model or Model Selection
6.Need for selecting the Right Model
7.Understanding Algorithm Boosting
8.Various Types of Algorithm Boosting
9.Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
1.Understanding the Machine Learning Algorithms
2.Importance of Algorithms in Machine Learning
3.Exploring different types of Machine Learning Algorithms
4.Supervised Learning
5.Unsupervised Learning
6.Reinforcement Learning
Exploring Supervised Learning Algorithms
1.Understanding the Supervised Learning Algorithm
2.Understanding Classifications
3.Working with different types of Classifications
4.Learning and Implementing Classifications
5.Logistic Regression
6.Naïve Bayes Classifier
7.Nearest Neighbour
8.Support Vector Machines (SVM)
9.Decision Trees
10.Boosted Trees
11.Random Forest
12.Time Series Analysis (TSA)
13.Understanding Time Series Analysis
14.Advantages of using TSA
16.Understanding various components of TSA
16.AR and MA Models
17.Understanding Stationarity
18.Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
1.Understanding UnSupervised Learning
2.Understanding Clustering and its uses
3.Exploring K-means
4.What is K-means Clustering
5.How K-means Clustering Algorithm Works
6.Implementing K-means Clustering
7.Exploring Hierarchical Clustering
8.Understanding Hierarchical Clustering
9.Implementing Hierarchical Clustering
10.Understanding Dimensionality Reduction
11.Importance of Dimensions
12.Purpose and advantages of Dimensionality Reduction
13.Understanding Principal Component Analysis (PCA)
14.Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
1.What is Hypothesis Testing in Machine Learning
2.Advantages of using Hypothesis Testing
3.Basics of Hypothesis
4.Normalization
5.Standard Normalization
6.Parameters of Hypothesis Testing
7.Null Hypothesis
8.Alternative Hypothesis
9.The P-Value
10.Types of Tests
11.T Test
12.Z Test
13.ANOVA Test
14.Chi-Square Test
Overview Reinforcement Learning Algorithm
1.Understanding Reinforcement Learning Algorithm
2.Advantages of Reinforcement Learning Algorithm
3.Components of Reinforcement Learning Algorithm
4.Exploration Vs Exploitation tradeoff
Introduction to Deep Learning
1.Understanding Artificial Intelligence
2.Understanding Machine Learning
3.Understanding the need for Deep Learning for Machines
4.Understanding Deep Learning
5.Understanding the Importance of Neural Network
6.Understanding how Artificial Intelligence, Machine Learning and Deep Learning are related
7.Introduction to Deep Learning Frameworks
8.Introduction to Tensorflow and Keras
Setting Up Deep Learning Environment
1.Installing Tensorflow
2.Installing Keras
3.Understanding Deep Learning Environment in Cloud Platform with AWS
4.Executing Tensorflow Code
5.Executing Tensorflow in AWS
Exploring Tensorflow
1.Understanding Placeholders
2.Creating Placeholders
3.Updating Placeholders with Data
4.Understanding Variables and Constants
5.Understanding Computation Graph
6.Exploring Tensor Board
7.Understanding Functions in Tensorflow
8.Exploring various Key Functions
9.Activation Functions
10.Sigmoid Functions and Softmax Functions
11.Understanding Rectified Linear Units - ReLu and Hyperbolic Tangent Functions
Building Neural Network
1.Understanding a Neural Network
2.Understanding the Components of a Neural Network
3.Input Layers
4.Computational Layers
5.Output Layers
6.Understanding Forward Propagation and Back-Propagation
7.Understanding the Hyper Parameters
8.Understanding Perceptron
9.Understanding Inputs and Weights
10.Understanding Outputs
11.Understanding Multi Layered Perceptron (MLP)
12.Understanding and implementing Regularization
13.Training Neural Networks
14.Understanding Training Data Sets
15.Understanding and using the MNIST Data Set
16.Application Areas of MLP
17.Working examples for MLP using Tensorflow and Keras
Understanding Convolutional Neural Networks (CNN)
1.Understanding what is Convolutional Neural Networks
2.Understanding the Architecture of CNN
3.Understanding the Convolutional Layers
4.Understanding the Pooling Layer
5.Understanding the Normalization Layer
6.Understanding the Fully-Connected Layer
7.Understanding various Popular CNN Architectures and Models
8.Understanding MLP Vs CNN
9.Exploring the Imagenet Dataset
10.Understanding Outputs
11.Application Areas of CNN
12.Working Examples for CNN using Tensorflow and Keras
Understanding Recurrent Neural Networks (RNN)
1.Understanding Sequences
2.Need for Neural Networks to Handle Sequences
3.Understanding Recurrent Neural Networks - RNN
4.Understanding the Recurrent Neuron
5.Managing Forward Propagation and Back Propagation in a RNN
6.Exploring various RNN Architectures
7.Application Areas of RNN
8.Working Examples for RNN using Tensorflow and Keras
Understanding Recursive Neural Networks
1.Understanding Recursive Neural Networks
2.Understanding the differences between Recurrent and Recursive Neural networks
3.Application areas of Recursive Neural Networks
4.Working Examples for Recursive Neural Networks using Tensorflow and Keras
Why Choose MAK Technologies?
Industry-Oriented Training
Real-Time Project Learning
Flexible Online Classes
Expert Mentor Support
Why You Should Learn Artificial Intelligence Course?
AI is no longer a niche field; by 2026, it has become a "digital colleague" across healthcare, finance, and creative industries.
The World Economic Forum predicts AI will create 97 million new global jobs in 2026, specifically targeting those with verified AI skill sets.
Professionals with AI expertise earn 56% more on average than their non-AI-skilled peers in the current tech landscape.
Gain an elite edge by learning to build, fine-tune, and deploy Large Language Models (LLMs) and autonomous AI agents.
As companies automate repetitive tasks, those who can manage and design AI workflows are becoming the most "essential" assets in any organization.
Learn to analyse massive datasets instantly to uncover hidden patterns, moving from a data-watcher to a high-level AI Systems Architect.
AI skills empower you to launch global campaigns or build custom SaaS products in days rather than months, effectively "amplifying" a small team's output.
Mastering AI literacy today ensures you remain relevant as technology evolves 66% faster in AI-exposed roles compared to traditional sectors.
Our course is open to everyone—from students and fresh graduates to working professionals in any field. For technical roles like AI Engineer, a background in Science or Commerce with a foundation in Mathematics is preferred, but curious beginners from non-IT backgrounds can start with our foundational modules.
No. While Python is the standard language for AI development, you don’t need prior expertise to start. We provide a comprehensive "Python for AI" bootcamp to teach you the coding basics required for Machine Learning and building autonomous agents from scratch.
AI is the broad science of mimicking human intelligence. Machine Learning (ML) is a subset focused on algorithms that learn from data patterns. Deep Learning is a specialized form of ML using multi-layered neural networks to solve complex tasks like image recognition and language translation.
Yes. Our 2026 curriculum is heavily focused on Generative AI, including training on LLM fine-tuning, Prompt Engineering, and building RAG (Retrieval-Augmented Generation) systems to ensure you are ready for the current job market.
Absolutely. We offer dedicated career support, including AI-specific resume building, mock technical interviews with industry experts, and direct referrals to top-tier MNCs and AI startups for roles like AI Analyst, ML Engineer, and GenAI Specialist.
Frequently Asked Questions (FAQ)