Fundamentals of Machine Learning with Python Implementation.
What you'll learn
- Use Python for Data Science and Machine Learning
- Implement Machine Learning Algorithms
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Basics of Reinforcement Learning
- Linear Regression
- Logistic Regression
- Clustering
- SVM
- Neural Network Concept
- Random Forest
- PCA and SVD
Course content
Introduction1 lecture • 9min
- IntroductionPreview08:41
Types of learning2 lectures • 43min
Linear regression4 lectures • 1hr 4min
Logistic regression3 lectures • 27min
Resources1 lecture • 1min
SVM4 lectures • 56min
Random forest classifier3 lectures • 38min
Basics of Neural Networks2 lectures • 46min
Computing accuracy and Dimensionality reduction3 lectures • 49min
Clustering3 lectures • 39min
Requirements
- Basics of python programming
- Linear algebra (understanding of matrices)
- Differential calculus (optional)
Description
Hello there! Welcome to Fundamentals of Machine Learning with Python Implementation. There are many courses available out there for this domain but what makes us different is that the learning in this class is gradual. All the concepts are built from scratch to give students a fair idea of how various algorithms work in addition to live demonstrations
In this course, students will acquire a good understanding of basic concepts of machine learning. The course also introduces students to deep learning (neural nets) and also artificial intelligence. The concepts are developed from scratch to make students well equipped with all the basics and math involved with all machine learning algorithms
Some concepts we cover include
Various types of learning like supervised, unsupervised and reinforcement learning.
Various supervised learning algorithms like linear and logistic regression.
Clustering techniques.
A brief introduction to Neural Nets.
Parameter tuning, data visualization and accuracy estimation techniques
Reinforcement learning techniques like Q-learning and SARSA
Deciding which algorithm fits for a given problem
Knowing all of these techniques will give an edge to the developer in order to solve many real world problems with high accuracy.
Who this course is for:
- Anyone interested in Machine Learning.
- Students who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any computer science student hoping to broaden their skillset
- Anyone interested in Machine Learning Including business leaders, managers, app developers, consumers - you!
Instructor
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- 85,520 Students
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Three Millennials
We transform students who are just beginners into paid professionals.
We have over 70,000+ Happy Students Learning from our courses. Our courses diversify from Web Development to Mobile App Development (Both iOS and Android Development) to Python Programming, Unity Development, Machine Learning, Artificial Intelligence, and much more.
We believe that the knowledge we share today will become the leading experience for tomorrow. Our primary motive is to help others learn variety of skills and give them sufficient knowledge, so they can excel in their careers.
Feel free to reach out to us with any questions you have regarding our Courses or any questions for that matter.
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