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Machine Learning Mastery: Python, Data, Models & Deployment
A course by Oladele Emmanuel, Machine learning
Hot & New
English
99% Positive Reviews (1)
104 Students
Master Python, data analysis, machine learning models, and deployment with real projects from beginner to professional.
What you'll learn
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Understand what machine learning is, the main types (supervised, unsupervised, reinforcement), and where it’s used in real life.
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Set up a complete Python environment for ML using VS Code/Jupyter the right way.
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Write strong Python code for ML projects (data types, operators, control flow, functions, modules, and packages).
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Handle errors properly with exception handling and build cleaner, more reliable programs.
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Use OOP in Python (classes, inheritance, polymorphism, encapsulation, abstraction) for real-world ML workflows.
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Apply advanced Python concepts like iterators, generators, closures, and decorators.
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Analyze and clean datasets with NumPy and Pandas (missing values, outliers, filtering, groupby, merges, pivot tables, and I/O).
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Create clear, professional visualizations with Matplotlib and Seaborn for reports and presentations.
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Build statistics, probability, and math foundations for ML (distributions, hypothesis testing, p-values, Bayes, vectors, optimization).
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Train, evaluate, and deploy ML models using scikit-learn (regression, classification, clustering, PCA) while integrating SQL databases (MySQL/PostgreSQL).
Description
Course Content
Reviews
Machine Learning Mastery: Python, Data, Models & Deployment is a hands-on, step-by-step course built to take you from beginner to confident ML practitioner—with projects, exercises, and deployment included. You’ll start with the fundamentals of machine learning and its real-world uses, then build strong Python skills (from basics to OOP, error handling, and advanced concepts like decorators and generators). From there, you’ll learn data analysis and visualization with NumPy, Pandas, Matplotlib, and Seaborn, strengthen your statistics and probability foundation, and train real machine learning models—then deploy them like a professional.
Designed for students, beginners, and aspiring ML engineers, this course goes beyond theory. By the end, you’ll complete practical projects (including an Uber data analysis capstone), work confidently with real datasets, and understand the full ML workflow from data to deployment.
Course Highlights
🔹 ML Foundations
Learn what ML is, key types (supervised, unsupervised, reinforcement), common applications, and how to set up your Python ML environment.
🔹 Python for ML
Master Python from basics to advanced: control flow, functions, modules, exceptions, OOP, and advanced tools like iterators, closures, and decorators—with exercises and assignments.
🔹 Data Analysis + Visualization
Clean and explore real data with NumPy and Pandas, then communicate insights using Matplotlib and Seaborn (plus file I/O like CSV, Excel, and SQL).
🔹 Capstone: Uber Data Analysis
Run a complete data analysis project—from cleaning and exploration to insights and presentation-ready visuals.
🔹 Stats, Probability & Math for ML
Cover descriptive and inferential statistics, probability distributions, hypothesis testing, and ML math essentials like vectors, calculus, and optimization.
🔹 Feature Engineering + Data Prep
Handle outliers, missing values, categorical and imbalanced data, and perform EDA/feature engineering using real datasets.
🔹 Models + Deployment + Databases
Build supervised models (regression/classification) and unsupervised models (clustering, PCA), evaluate performance, work with MySQL/PostgreSQL, and learn how deployment fits into real ML projects.
Key Questions
What are the course requirements?
Access to a Laptop or Computer.
Will the certificate be issued?
Yes, a certificate of completion will be issued at the end of the course at No charge.
Designed for students, beginners, and aspiring ML engineers, this course goes beyond theory. By the end, you’ll complete practical projects (including an Uber data analysis capstone), work confidently with real datasets, and understand the full ML workflow from data to deployment.
Course Highlights
🔹 ML Foundations
Learn what ML is, key types (supervised, unsupervised, reinforcement), common applications, and how to set up your Python ML environment.
🔹 Python for ML
Master Python from basics to advanced: control flow, functions, modules, exceptions, OOP, and advanced tools like iterators, closures, and decorators—with exercises and assignments.
🔹 Data Analysis + Visualization
Clean and explore real data with NumPy and Pandas, then communicate insights using Matplotlib and Seaborn (plus file I/O like CSV, Excel, and SQL).
🔹 Capstone: Uber Data Analysis
Run a complete data analysis project—from cleaning and exploration to insights and presentation-ready visuals.
🔹 Stats, Probability & Math for ML
Cover descriptive and inferential statistics, probability distributions, hypothesis testing, and ML math essentials like vectors, calculus, and optimization.
🔹 Feature Engineering + Data Prep
Handle outliers, missing values, categorical and imbalanced data, and perform EDA/feature engineering using real datasets.
🔹 Models + Deployment + Databases
Build supervised models (regression/classification) and unsupervised models (clustering, PCA), evaluate performance, work with MySQL/PostgreSQL, and learn how deployment fits into real ML projects.
Key Questions
What are the course requirements?
Access to a Laptop or Computer.
Will the certificate be issued?
Yes, a certificate of completion will be issued at the end of the course at No charge.
Module 1: Introduction to Machine Learning
5 lectures
8 minutes
Module 2.1 Basic Python Programing
5 lectures
20 minutes
19 minutes
Module 2.2 Flow Control Structure
4 lectures
15 minutes
2 minutes
12 minutes
Module 2.3 Basic Data Structures
4 lectures
26 minutes
10 minutes
20 minutes
14 minutes
Module 2.4 Python Function
3 lectures
16 minutes
9 minutes
Module 2.5 Python modules and packages
2 lectures
15 minutes
Module 2.6 Exception Handling in python
2 lectures
19 minutes
9 minutes
Module 2.7 OOP
5 lectures
21 minutes
7 minutes
10 minutes
Module 2.8 Advance Python
1 lecture
13 minutes
Module 3: Python for Data Analysis – Lesson 3.1: Introduction to NumPy
3 lectures
19 minutes
18 minutes
9 minutes
Module 3.2 Pandas
10 lectures
8 minutes
16 minutes
10 minutes
11 minutes
18 minutes
6 minutes
12 minutes
20 minutes
9 minutes
19 minutes
Module 3.3 Matplotlib
3 lectures
8 minutes
6 minutes
Module 3.4 Seaborn
5 lectures
8 minutes
5 minutes
6 minutes
12 minutes
29 minutes
Module 4 – Statistics and Mathematics for ML
4 lectures
18 minutes
13 minutes
9 minutes
16 minutes
Module 5: Feature Engineering and Data Preparation Lesson : 5.1 – Feature Engineering and Data Preparation
7 lectures
19 minutes
11 minutes
9 minutes
9 minutes
21 minutes
Module 5.2 Supervised Learning
10 lectures
17 minutes
8 minutes
8 minutes
5 minutes
15 minutes
6 minutes
7 minutes
5 minutes
Module 5.3 Other Supervised Learning
10 lectures
42 minutes
30 minutes
2 minutes
35 minutes
30 minutes
16 minutes
16 minutes
29 minutes
47 minutes
Module 5.4 Under Supervised Learning
2 lectures
28 minutes
33 minutes
Module 5.5 Final Project and Deployment
3 lectures
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Recent reviews
Empower and fuel your creativity and passion for learning, read testimonials
Seyitan Adekoya
Rating |
I just started the course and and the level of understanding the tutor is so amazing This is just so beautiful. I can't wait to keep on learning.
Frequently Asked Questions (FAQ)
Are the videos downloadable ?
The videos are efficiently compressed for quick downloads at any time.
How do I access the course videos and materials?
Once your payment is complete, you will have full access to the course lessons and materials. You can download and watch them at your convenience, and our team will provide full support and assistance throughout your learning journey.
How can I obtain my certificate after completing the course?
Once you have completed the course, you can easily download your certificate directly from your dashboard.
See course top reviews
Empower and fuel your creativity and passion for learning, read testimonials
Seyitan Adekoya
Rating |
I just started the course and and the level of understanding the tutor is so amazing This is just so beautiful. I can't wait to keep on learning.
₦6,000
NGN
70% Disc.
₦20,000
6hours left
Course features
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88 Lessons(22 Hours)
-
3 years access to course
-
104 Students
-
Skill level : All Levels
-
Certificate : Yes
-
Rated: 5.0
Certificate preview
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