Artificial Intelligence [Machine Learning] Diploma

About The Diploma

Artificial Intelligence ( Machine Learning ) Diploma is designed to equip trainees with the essential skills and knowledge needed to excel in the rapidly evolving field of AI and Machine Learning. Through a comprehensive curriculum, trainees will gain proficiency in Python programming and its application in machine learning & AI.

This diploma program is structured into two parts. In the foundational section, trainees will embark on a journey to master Python programming, delve into descriptive statistics and probability theory, understand linear algebra concepts using Python, and become proficient in utilizing essential Python libraries for data science. Additionally, they will grasp fundamental database concepts and relational database management systems, laying a strong foundation for working with large datasets.

The advanced section of the program builds upon this foundation, introducing to advanced Machine Learning techniques. They will explore Python’s capabilities in machine learning, from preprocessing data to building and evaluating models. Through courses in inferential statistics, advanced linear algebra, and both supervised and unsupervised learning, trainees will develop the ability to draw insights from data, make predictions, and uncover hidden patterns.

Furthermore, the curriculum includes specialized topics such as time series forecasting, reinforcement learning, and an introduction to deep learning through neural networks. These advanced topics provide trainees with a deeper understanding of cutting-edge techniques in Artificial Intelligence and prepare them to tackle complex real-world challenges

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Course Description

Why Study Artificial Intelligence & Machine Learning

There are several reasons why studying Artificial Intelligence & Machine Learning  is important:

  • Career Opportunities: AI is one of the fastest-growing fields, and there is a high demand for skilled AI professionals. Pursuing a career in AI can lead to a lucrative and fulfilling profession.
  • Problem Solving: AI is being used to solve some of the world's most complex problems, such as climate change, disease detection, and traffic congestion. Studying AI can help you understand how to develop solutions to these and other pressing issues.
  • Innovation: AI is driving innovation in many industries, including healthcare, finance, and transportation. By studying AI, you can learn how to develop new technologies that can revolutionize these industries.
  • Better Decision Making: AI can help organizations make better decisions by analysing large amounts of data and providing insights that humans may not be able to detect. Studying AI can help you understand how to apply these techniques to make better decisions
  • Understanding the Future: AI is expected to have a significant impact on society in the coming years. By studying AI, you can gain a better understanding of how this technology will shape the future and be better prepared for the changes it may bring. 

Who should attend

The Artificial Intelligence ( Machine Learning ) Diploma is suitable for individuals without a technical background who are eager to explore the landscape of Artificial Intelligence (AI).

  • Professionals: The Artificial Intelligence & Machine Learning Diploma is beneficial for professionals who want to expand their knowledge and apply AI techniques to their work. For example, professionals in the fields of healthcare, finance, marketing, and manufacturing can use AI to improve their operations and make better decisions.
  • Entrepreneurs: The Artificial Intelligence & Machine Learning Diploma can be valuable for entrepreneurs who want to develop new AI-based technologies and products. The diploma provide a strong understanding of AI concepts and techniques, which can help entrepreneurs create innovative solutions that leverage AI technologies.
  • Researchers: The Artificial Intelligence & Machine Learning Diploma can also be beneficial for researchers who are interested in exploring the potential of AI. The Diploma provide a strong foundation in AI concepts and techniques, which can help researchers develop new AI-based applications and technologies.

Diploma Objectives

  • Acquire basic programming skills in Python for data manipulation, analysis, and visualization
  • Understand descriptive statistics & probability to summarize and describe features of data sets and make informed decisions.
  • Implement linear algebra operations in Python using libraries like NumPy.
  • Explore essential Python libraries for data manipulation (e.g., Pandas), visualization (e.g., Matplotlib, Seaborn), and scientific computing (e.g., NumPy, SciPy).
  • Understand fundamental database concepts.
  • Learn & practice about supervised learning & unsupervised learning algorithms to identify patterns and structures in data sets.
  • Introduce and understand reinforcement learning & deep learning principles and algorithms.

Prerequisites for attending The Artificial Intelligence Diploma

  • Obtaining a Certificate of completion after attending Foundation Of Artificial Intelligence Course

Certificate of Completion

To have successfully completed a trainee should:

  • have an attendance rate of not less than 80% (or such higher attendance  requirement as prescribed for the course
  • Pass the course practical assessments and projects in at least 80% of the total number of assignments as required in each training course.

Course Schedule

2024-07-05 5:00 PM - 9:00 PM Friday


   Python Basic  

  • Basic Syntax
  • Data Types
  • Operators
  • Control flow statements
  • Decisions
  • Loops
  • Functions

Data Structures

  • List and tuples
  • Sets
  • Dictionaries
  • Strings


  • Classes
  • Objects
  • Data members
  • Overloading
  • Inheritance

  Files and  Databases

  • Reading from Files
  • Writing into files
  • Database Connections
  • files using pandas
  • Dealing with files using pandas

Descriptive  Statistics

  • Population vs samples
  • Measures of Central Tendency
  • Measures of Variability
  • Detection of Outliers


  • Probability Laws
  • Probability Distribution
  • Bayesian Theorem
  • Central Limit Theorem
  • Confidence Interval
  • Matrix Operations
  • Matrix Inverse and Decomposition
  • Vectors
  • Numpy
  • Matplotlib
  • Plotly
  • Seaborn
  • Pandas
  • Sklearn

  • Data cloud services
  • Auto ML
  • ML Ops

  • Object oriented programming
  • Try except
  • Module
  • Confidence Intervals
  • Significance Tests About Hypotheses
  • Comparing Two Groups
  • Basis and Dimension
  • Matrices
  • Elementary Operations and Matrix Invariants
  • Inner Product
  • Linear Regression Review
  • Logistics Regression Review
  • SVM Review
  • K-Nearest Neighbor (K-NN) classification
  • Decision Tree Classification
  • Naive Bayes
  • Random Forest Classification
  • Ensemble Classifier(Bagging-Stacking-Vote)
  • Model optimization
  • XGBoost
  • ADaBoost
  • LightGBM

K Means clustering

  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters

Hierarchical clustering

  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms

Principal Component Analysis (PCA)

DB scan

  • understanding basics of forecasting
  • Statistical forecasting models
  • FB prophet model
  • Upper Confidence Bound
  • Thompson Sampling
  • Neural Network Architecture and How it Works
  • Artificial Neural Network
  • Convolutional Neural Networks
  • Recurrent neural network