Learn Machine Learning from scratch, this course for beginner who want to learn the fundamental of machine learning and artificial intelligence. the course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It’s highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.
The objective of this course is to explain the Machine learning and artificial intelligence in a very simple and way to understand. I strive for simplicity and accuracy with every definition, code I publish. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details.
Who this course is for:
- Beginners of Machine learning developers curious about machine model
What you’ll learn
- Fundamental of Machine Learning; Introduction, types of machine learning, applications
- Supervised, Unsupervised and Reinforcement learning
- Principal Component Analysis (PCA); Introduction, mathematical and graphical concepts
- Confusion matrix, Under-fitting and Over-fitting, classification and regression of machine model
- Support Vector Machine (SVM) Classifier; Introduction, linear and non-linear SVM model, optimal hyperplane, kernel trick, project in Python
- K-Nearest Neighbors (KNN) Classifier; Introduction, k-value, Euclidean and Manhattan distances, outliers, project in Python
- Naive Bayes Classifier; Introduction, Bayes rule, project in Python
- Logistic Regression Classifier; Introduction, non-linear logistic regression, sigmoid function, project in Python
- Decision Tree Classifier; Introduction, project in Python
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