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ML-BEGIN.AW1
Machine Learning for Beginners
No experience? No problem. Learn machine learning from scratch in our step-by-step course and build in-demand skills today.
- 13 Interactive Lessons and 103 topics mapped to the official exam objectives
Beginner Self-paced · 1 year access
13Interactive Lessons
103Topics
01 / Skills you'll get
What you will be able to do
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No credit card required
Jump into the AI world with our Machine Learning online course for beginners.
Through hands-on lessons, you’ll explore core concepts from data preprocessing and feature selection to regression, classification, and neural networks. Learn to build models from the ground up, implement algorithms with scikit-learn, and master techniques like decision trees, SVMs, and clustering.
- Data Preprocessing: Clean, transform, and prepare raw data for machine learning tasks.
- Feature Selection & Extraction: Identify key data features using PCA, LDA, and correlation analysis techniques.
- Model Building: Implement regression (linear, gradient descent) and classification (KNN, logistic regression, Naive Bayes) from scratch.
- Neural Networks and Deep Learning: Understand perceptrons, multi-layer networks, and backpropagation.
- Real-World Application: Use scikit-learn to deploy algorithms like SVMs, decision trees, and clustering (K-means, hierarchical).
- Model Evaluation: Validate models using training, testing, and cross-validation techniques.
Course Highlights
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13 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
13 Interactive Lessons · 103 topics01 Preface +
02 An Introduction to Machine Learning 6 topics +
- Conventional algorithm and machine learning
- Types of learning
- Working
- Applications
- History
- Conclusion
03 The Beginning: Pre-Processing and Feature Selection 8 topics +
- Introduction
- Dealing with missing values and ‘NaN’
- Converting a continuous variable to categorical variable
- Feature selection
- Chi-Squared test
- Pearson correlation
- Variance threshold
- Conclusion
04 Regression 9 topics +
- Introduction
- The line of best fit
- Gradient descent method
- Implementation
- Linear regression using SKLearn
- Experiments
- Finding weights without iteration
- Regression using K-nearest neighbors
- Conclusion
05 Classification 13 topics +
- Introduction
- Basics
- Classification using K-nearest neighbors
- Implementation of K-nearest neighbors
- The KNeighborsClassifier in SKLearn
- Experiments – K-nearest neighbors
- Logistic regression
- Logistic regression using SKLearn
- Experiments – Logistic regression
- Naïve Bayes classifier
- The GaussianNB Classifier of SKLearn
- Implementation of Gaussian Naïve Bayes
- Conclusion
06 Neural Network I – The Perceptron 11 topics +
- Introduction
- The brain
- The neuron
- The McCulloch Pitts model
- The Rosenblatt perceptron model
- Activation functions
- Implementation
- Learning
- Perceptron using sklearn
- Experiments
- Conclusion
07 Neural Network II – The Multi-Layer Perceptron 11 topics +
- Introduction
- History
- Introduction to multi-layer perceptrons
- Architecture
- Backpropagation algorithm
- Learning
- Implementation
- Multilayer perceptron using sklearn
- Experiments
- Conclusion
- Practical/Coding
08 Support Vector Machines 7 topics +
- Introduction
- The Maximum Margin Classifier
- Maximizing the margins
- The non-separable patterns and the cost parameter
- The kernel trick
- SKLEARN.SVM.SVC
- Conclusion
09 Decision Trees 8 topics +
- Introduction
- Basics
- Discretization
- Coming back
- Containing the depth of a tree
- Implementation of a decision tree using sklearn
- Experiments
- Conclusion
10 Clustering 6 topics +
- Introduction
- K-means
- Spectral clustering
- Hierarchical clustering
- Implementation
- Conclusion
11 Feature Extraction 8 topics +
- Introduction
- Fourier Transform
- Patches
- sklearn.feature_extraction.image.extract_patches_2d
- Histogram of oriented gradients
- Principal component analysis
- Conclusion
- Preface
12 Appendix 1: Cheat Sheet – Pandas 12 topics +
- Creating a Pandas series
- Indexing
- Slicing
- Common methods
- Boolean index
- DataFrame
- Adding a Column in a Data Frame
- Deleting column
- Addition of Rows
- Deletion of Rows
- unique
- Iterating a Pandas Data Frame
13 Appendix 2: Face Classification 4 topics +
- Introduction
- Data
- Methods
- Observation and Conclusion
03 / FAQs
Questions before you start
How can a beginner learn machine learning?+
Beginners should start with:
- Python programming (loops, functions, libraries like NumPy, Pandas)
- Core math concepts (linear algebra, statistics, calculus)
- Structured online courses (explore our catalog to find a machine learning course for beginners)
- Hands-on projects (Kaggle datasets, implementing models from scratch)
- Scikit-learn & TensorFlow for practical implementation
Can I learn ML in 1 month?+
Yes, you can grasp machine learning basics (regression, classification, basic neural networks) and complete small projects.
And no, if you’re aiming for mastery. Becoming job-ready takes 3-6 months of consistent study.
Is ML easier than AI?+
Let’s break it down to make it more digestible:
- ML is a subset of AI, so it’s narrower in scope.
- AI includes non-learning systems (e.g., rule-based chatbots), while ML focuses on data-driven learning.
- ML can be harder due to maths/stats requirements, but AI’s broader concepts (e.g., robotics, NLP) add complexity.
Can I get an ML job without experience? +
Build a portfolio (GitHub projects, Kaggle competitions) to compensate for a lack of formal experience.
Keep going
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