Introduction to Machine Learning [Free Course]
Unlock the world of Machine Learning with our free series. Simple, impactful, and designed for all. Start your journey today
“Machine learning will automate jobs but also create new ones, and the new ones will be about enhancing the human experience. - Satya Nadella ”
Course Overview:
This course provides an in-depth introduction to the field of Machine Learning. It covers the foundational concepts, theories, and practical applications of machine learning, from basic algorithms to advanced techniques.
Prerequisites:
A basic understanding of mathematics, including calculus, linear algebra, and probability.
A familiarity with programming concepts and experience using a programming language such as Python.
Course Objective:
- Understand the fundamental concepts and principles of machine learning.
- Learn how to apply machine learning algorithms to real-world problems.
- Develop an understanding of supervised, unsupervised, and reinforcement learning.
- Gain proficiency in Python programming language for machine learning.
- Learn about various machine learning tools and frameworks.
Module 1: Introduction to Machine Learning
Types of Machine Learning:
Data Exploration and Visualization
Data Visualizations Scatter Plots and Heatmaps
Module 2: Regression
Module 3: Classification
- Introduction to Classification
- Types of Classifiers
- Linear Classifiers
- Non-Linear Classifiers
- Decision Trees
- CART Algorithm
- ID3 Algorithm
- Random Forests
- Support Vector Machines (SVMs)
- Kernel Trick
- Soft Margin SVMs
- Multi-Class SVMs
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Neural Networks for Classification
- Perceptron Algorithm
- Multilayer Perceptron (MLP)
- Backpropagation Algorithm
- Activation Functions
- Evaluation Metrics for Classification
- Confusion Matrix
- Accuracy, Precision, Recall, and F1-Score
- Receiver Operating Characteristic (ROC) Curve
- Area Under the Curve (AUC)
Module 4: Features & Model Selection
Module 5: Ensemble Learning
Module 6: Clustering
Introduction to Clustering
DBSCAN
Module 7: Dimensionality Reduction
t-Distributed Stochastic Neighbor Embedding (t-SNE)
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