Machine Learning Mastery

Course Description

This course teaches Machine Learning from beginner to advanced level, covering everything from Python programming to building, training, and deploying intelligent AI models using modern tools like NumPy, Pandas, Scikit-learn, and TensorFlow. You'll learn professional data manipulation, model building, and deep learning techniques through hands-on projects. If you're looking to master artificial intelligence and machine learning, this is the perfect course for you.

Course Contents
Phase 1: Machine Learning Theory
- Lecture 1: Introduction to Machine Learning
- Lecture 2: Applications of Machine Learning
- Lecture 3: AI, ML, and DL Differences
- Lecture 4: Intro to Deep Learning and Neural Networks
- Lecture 5: Types of Models/Algorithms
- Lecture 6: Steps of Building Machine Learning System

Phase 2: Python Programming for ML
- Lecture 7: Introduction to Python Programming
- Lecture 8: Variable and Data Types
- Lecture 9: Basic Arithmetic Operations
- Lecture 10: Control Statement
- Lecture 11: Loop, Break, and Continue
- Lecture 12: List
- Lecture 13: Tuple
- Lecture 14: Set
- Lecture 15: Dictionary
- Lecture 16: Functions
- Lecture 17: Classes, Methods, and Objects
- Lecture 18: Inheritance
- Lecture 19: Module and Pip
- Lecture 20: Date and Time
- Lecture 21: RegEx
- Lecture 22: Input Function
- Lecture 23: Try...Except

Phase 3: NumPy for Data Computing
- Lecture 24: Introduction to Numpy
- Lecture 25: Creating Arrays
- Lecture 26: Array Basics
- Lecture 27: Mathematical Operations
- Lecture 28: Matrices
- Lecture 29: Linear Algebra
- Lecture 30: Random and Probability
- Lecture 31: Data Type and Conversion

Phase 4: Pandas for Data Analysis
- Lecture 32: Introduction to Pandas
- Lecture 33: Read CSV
- Lecture 34: Read JSON
- Lecture 35: Data Analysis
- Lecture 36: Data Cleaning
- Lecture 37: Data Engineering

Phase 5: Data Visualization with Matplotlib
- Lecture 38: Introduction to Matplotlib
- Lecture 39: Creating Plots
- Lecture 40: Customizing Plots
- Lecture 41: Sales Visualization Sample
- Lecture 42: Saving and Exporting Visualizations

Phase 6: Machine Learning with Scikit-Learn
- Lecture 43: Introduction to Scikit-learn
- Lecture 44: Loading and Testing Datasets
- Lecture 45: Training a Simple ML Model
- Lecture 46: Data Preprocessing Basics
- Lecture 47: Encoding and Scaling
- Lecture 48: Supervised and Unsupervised Learning
- Lecture 49: Regression and Classification Algorithms
- Lecture 50: Model Evaluation Metrics
- Lecture 51: Training a DecisionTreeClassifier Model
- Lecture 52: Training a LinearRegression Model
- Lecture 53: Working with Classification Algorithms
- Lecture 54: Working with Regression Algorithms
- Lecture 55: Model Performance
- Lecture 56: Ensemble Algorithms
- Lecture 57: Gradient Boosting Algorithms
- Lecture 58: Hyperparameter Tuning

Machine Learning Projects
- Lecture 59: Project I - Sales Forecasting
- Lecture 60: Project II - Credit Card Fraud Detection
- Lecture 61: Project III - Course Recommendation

Model Deployment
- Lecture 62: Deployment I - Streamlit Basics
- Lecture 63: Deployment II - Building Streamlit Projects
- Lecture 64: Deployment III - Hosting on Streamlit Cloud

Resources and Recommendations
- Lecture 65: Vibe Coding (Coding in the Age of AI)
- Lecture 66: Project and Learning Resources

Phase 7: Deep Learning
- Lecture 67: Introduction to Deep Learning
- Lecture 68: FeedForward Neural Network Lab

Phase 8: Natural Language Processing
- Lecture 69: Natural Language Processing
- Lecture 70: NLP Lab
- Lecture 71: Sentiment-Analysis Lab
- Lecture 72: Named Entity Recognition Lab
- Lecture 73: Sequence Model Lab

Phase 9: Convolutional Neural Network
- Lecture 74: Convolutional Neural Network
- Lecture 75: CNN Architecture-Padding
- Lecture 76: CNN Architecture-Pooling
- Lecture 77: CNN Image Classification
- Lecture 78: Transfer in Learning
- Lecture 79: Transfer Learning Resnet
- Lecture 80: Transfer Learning VGG16

Bonus Phase: RAG
- Lecture 81: Intro to RAG
- Lecture 82: RAG Lab

Phase 10: Conclusion
- Lecture 83: Deployment
- Lecture 84: Summary and Conclusion
- Lecture 85: Recommendations

What Can You Do After Completing This Course:
- Build and deploy machine learning models for real-world problems
- Analyze and visualize complex datasets with professional tools
- Create AI-powered applications and deploy them to the cloud
- Work with neural networks for computer vision and NLP tasks
- Develop recommendation systems and predictive analytics solutions
- Work efficiently using modern AI development tools and workflows
- Pursue freelance opportunities as ML Engineer or Data Scientist
- Create AI solutions for business, healthcare, finance, etc.

Course Features:
- Hands-on coding lessons
- Real-world examples and mini projects
- Downloadable notes, source codes, and datasets
- Step-by-step explanations in Hausa language

Who This Course Is For:
- Beginners who want to start a career in ML or Data Science
- Students and professionals looking to add Machine Learning skills

Requirements:
- A personal computer
- Min 8GB RAM
- 256GB+ ROM

Tutors:
- Muhammad Auwal Ahmad
- Abdullahi Ahmad Babura

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Course Benefits

Course Fee