Artificial Intelligence
Data Science & Machine Learning
Explore essential concepts of data science, including data processing, statistical analysis, and visualization. Learn supervised and unsupervised machine learning algorithms and apply them using Python and relevant libraries. This course equips students for roles in data-driven decision making. Our expert instructors bring years of experience, ensuring training is enriched with practical labs and real-world examples.
About this course
Explore essential concepts of data science, including data processing, statistical analysis, and visualization. Learn supervised and unsupervised machine learning algorithms and apply them using Python and relevant libraries. This course equips students for roles in data-driven decision making. Our expert instructors bring years of experience, ensuring training is enriched with practical labs and real-world examples.
Advantages
Practical teaching
In addition to class hours, you will practice the topics covered with your instructor and mentor dur
Mentors
The knowledge and skills you learn at the academy will be further strengthened with the mentor syste
Academic transcript
Assignments and projects are checked by the instructor, and your knowledge and skills are determined
Training program
- 1 Introduction to Programming
- 2 Conditional Statements
- 3 Strings
- 4 Loops
- 5 Functions
- 6 Data Structures & Algorithms (for Data Science)
- 7 Comparative Analysis of Data Structures
- 8 Pandas for Data Analysis/Data Cleaning/Data Processing
- 9 NumPy
- 10 Data Visualization: Plotly, Matplotlib, Seaborn
- 11 OOP
- 1 Sample & Population differences, Mean, Median, Mode, Variance, Deviation
- 2 Philosophy of Randomness, Random Variables
- 3 Covariance and Correlation
- 4 Quantiles, Outlier Detection and Exclusion
- 5 The Application and Moral of Standardization and Normalization of the Data
- 6 Distributions: Normal Distribution, Binomial Distribution
- 7 P value, Hypothesis testing
- 1 Supervised vs Unsupervised Learning
- 2 Machine Learning Model Preparation Stages
- 3 Regression Analysis: Linear Regression
- 4 Gradient Descent in Linear Regression
- 5 Logistic Regression
- 6 Regularization
- 7 Variance vs Bias
- 8 Error Metrics
- 9 K-Means
- 10 Decision Tree
- 11 PCA
- 12 Anomaly Detection
- 13 Recommender System
- 14 Neural Networks
- 15 Large Scale Machine Learning
- 16 Convolutional Neural Networks
- 17 Recurrent Neural Networks
- 18 Timeseries Analysis
- 19 Data Cleaning and Preprocessing Procedures in Natural Language Processing / Understanding / Generation
Upcoming groups
April 25, 2026
Office
- Days
- Wed - Sat
- Hours
- 19:00 - 21:00
