Program Objective:
By the end of this course, the trainee will be able to:
- Understand the key concepts and tools in the field of Machine Learning and determine where they might be used effectively.
- Recognize the fundamental ideas of programming.
- Establish hands-on proficiency in Python programming.
- Acquire Python skills to manage, wrangle, and manipulate data.
- Implement popular data analysis techniques using Python.
- Develop supervised and unsupervised Machine Learning models using Python.
- Apply them to resolve real-world practical problems in a capstone project.
Program Module:
Upon completion of all the following modules, participants will receive the Professional Certificate in Machine Learning with Python.
1. Programming Fundamentals in Python
1.1. Programming Fundamentals.
1.2. Algorithms and Flowcharts.
1.3. Python Installation and Usage.
1.4. Variables and Expressions.
1.5. Data Structures in Python.
1.6. Working with Python Libraries.
1.7. Conditional Statements.
1.8. Python Functions.
1.9. Working with Loops and Iterations.
2. Fundamentals of Data Analysis in Python
2.1. Fundamentals of Pandas and Numpy.
2.2. Data Wrangling and Manipulation.
2.3. Feature Engineering.
2.4. Data Visualization in Python.
2.5. Descriptive Statistics.
2.6. Exploratory Data Analysis.
2.7. Probability Distributions.
2.8. Hypothesis Testing.
2.9. T-Test, Chi-Squared Test, ana ANOVA.
3. Standard Algorithms of Machine Learning
3.1. Introduction to Machine Learning.
3.2. Fundamentals of Scikit-Learn as Python library for Machine Learning.
3.3. Supervised Learning.
3.3.1. Regression Models.
3.3.2. Evaluation Metrics.
3.3.3. Cross-Validation and Parameter Tuning.
3.3.4. Class-Imbalance Problem and Python Piplines.
3.3.5. K-Nearest Neighbours.
3.3.6. Decision Trees.
3.3.7. Naïve Bayes.
3.3.8. Support Vector Machines.
3.4. Unsupervised Learning.
3.4.1. K-Means.
3.4.2. Hierarchical Clustering.
3.4.3. Evaluation of Clustering Algorithms.
4. Ensemble Learning and Hybrid Models
4.1. Ensemble Learning and Hybrid Models.
4.1.1. Bagging.
4.1.2. Random Forest.
4.1.3. Boosting.
4.1.4. Stacking.
4.2. Capstone project.
4.2.1. How to conduct a capstone project in Machine Learning.
4.2.2. Project Follow-up and Discussions.
4.2.3. Final Presentation and Assessment.
Program Duration:
100 hrs
Assessments:
- Classroom exercises.
- Group assignments.
- Individual assignments.
- Capstone project.
Certification:
After successfully completing all four of the aforementioned modules, participants will be awarded a professional certificate in Machine Learning with Python.
Intended Learners:
A professional wishing to boost his/her profile with highly sought-after abilities, a business owner seeking to acquire an advantage in the marketplace, or someone looking to start a career in data science would all benefit from taking this diploma.
Prerequisites:
- Basic computer skills.
- An ability to install software from online websites.
Express Interest : cce@guc.edu.eg
For registration click here