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data Intermediate 21 lessons

Machine Learning A-Z

Master classical ML algorithms. Build Regression, Classification, and Clustering models using Scikit-Learn to solve predictive business problems.

Before Deep Learning, you must master Machine Learning. This course covers the essential algorithms used in business today. You will learn Linear and Logistic Regression for prediction, Decision Trees and Random Forests for classification, and K-Means for clustering data. We cover the entire pipeline: data preprocessing, feature scaling, model training, and evaluation metrics (Accuracy, Precision, Recall). By the end, you will be able to deploy a predictive model to a web API.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
AI Research
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Secure Enrollment via SSL

Complete Course Syllabus

  • 1
    Preprocessing
    Handling missing data, encoding categoricals, and scaling.
  • 2
    Regression Models
    Predicting continuous values like house prices.
  • 3
    Classification
    Predicting categories like Spam vs Not Spam.
  • 4
    Clustering
    Grouping customers based on purchasing behavior.
  • 5
    Model Selection
    Grid Search and Cross-Validation for tuning.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$110k - $160k

Career Paths

Machine Learning Engineer $110k-$160k
Data Scientist $105k-$150k
AI Researcher $120k-$170k

What You Will Learn

Train predictive models using Linear and Logistic Regression
Build robust classifiers using Random Forests and SVMs
Cluster unlabeled data using K-Means and Hierarchical Clustering
Evaluate model performance using Confusion Matrices and ROC curves
Deploy trained models to production using Flask/FastAPI wrappers

Skills You Will Gain

Scikit-Learn Regression Analysis Classification Clustering Model Evaluation

Who Is This For

Data Analysts
Developers entering AI
Statisticians

Prerequisites

Python
Pandas
Basic Statistics

Machine Learning A-Z FAQs

Deep Learning?

No, this covers Classical ML (the foundation).

Math heavy?

We explain concepts intuitively, libraries do the math.

Real projects?

Yes, predicting churn and customer segmentation.

TensorFlow?

No, Scikit-Learn is the tool for classical ML.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

Jupyter
Scikit-Learn
Matplotlib
Pickle
Anaconda

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