Data Science Supervised Machine Learning in Python
Data Science Supervised Machine Learning in Python
What is inside?
In today's dilemma, the most demanded resurgence is seen in artificial intelligence (AI) and machine learning (ML).
Machine learning is the sub-branch of Artificial intelligence. ML has bought so many amazing results to everyday life. For instance, ML is able to recognize medicines from images and predict diseases without the assistance of medical experts.
Besides this, one of the famous machine learning programs, Google's AlphaGo developed with deep reinforcement techniques is in high insights, today. This Ml program is able to beat the world's renowned champions in one of the strategical games, Game Go.
Moreover, self-driving vehicles are also a good example to deliver the importance of machine learning which is holding the entire automotive industry. The demand for smart vehicles is increasing day by day because our world has drastically reduced the ratio of accidents that used to occur due to human err.
On top of that, Google recently announced that it is "ML First," which means, machine learning directs our search engines. Precisely, machine learning is getting our attention more and more as the days are passing. It seems our coming years will be the slave of machine learning, as every product will embed AIoT and machine learning techniques.
Machine learning does not bound here. It is facilitating every industry such as robotic, finance, marketing (advertising), medicine, education, and everything in between.
This also means that Machine Learning is bringing us hundred and thousands of career opportunities.
Wondering, how it works? You might be thinking that we are talking about the machines that could actually think themselves and acts accordingly. If you are thinking so, you are right. Curious to know how? How certain machines can control or shape the world's conventional models, which require human assistance?
This course is the answer!
What will you learn in this course?
This course will cover various algorithms to help you master the art of supervised machine learning. These algorithms include:
Machine Learning Algorithm: K-Nearest Neighbour
This algorithm is extremely intuitive and simple. K-Nearest Neighbour is simple to implement in order to classify the categories. With our instructors' guidance, you will be able to access and implement source code that could be used in various applications. Also, we will see some cases where this algorithm might fail under some conditions.
We believe a learner must understand both the positive and negative aspects of implementing a certain algorithm.
Machine Learning Algorithm: Naive Bayes Classifier
Secondly, we will explore Naive Bayes Classifier along with General Bayes Classifier. Students love this algorithm because they find it interesting to predict the results based on probability. In this algorithm, we will see how we can transform Bayes Classifiers into various classifiers such as linear and quadratic classifiers and speed up the algorithm's outcomes.
Machine Learning Algorithm: Decision Tree algorithm
Next, we will focus on the Decision Tree algorithm. Since it is a complex algorithm, we will ensure to make its explanation in the simplest way. Due to complexity, most courses do not add this algorithm in their curriculum; proudly we do not skip it. We always make sure to help students implement this algorithm, too.
Machine Learning Algorithm: Perceptron model
Perception models are ancestors of deep learning and neural networks and are important in a machine-learning context. We have added this algorithm with various scenarios, so students do not lack any concept.
Other Machine Learning Algorithms include:
- Feature Extraction
- Feature Selection
- Multiclass Classification.
From comparison to approaching better outcomes, we will explain 360-degree concepts of supervised machine learning. Mostly, we will be focusing on the Sci-Kit Learn library because it optimizable and well-test library that could work in every scenario.
We will cap everything practically through real-world application by writing runnable machine learning codes and developing models that could predict accurate outcomes.
This is what reputable companies do and generate profits from.
We assure you every material we will be providing you in this course would be free from charges. You can install, download, or repair all machine learning libraries with no fee at all. You can obtain all required libraries such as Python, Numpy, and Scipy with basic commands on Linux, Windows, or Mac.
What's more? The course is totally focused on "how to build and understand", not just "formal interaction with screen".
For instance, anyone can get hands-on API modifications in just 15 minutes after reviewing our documentation.
Our courses are never about memorizing the facts and figures, we rely on pure experimentations. Our courses are all about seeing yourself in the big picture where you can see yourself as an expert in machine learning.
From understanding the machine learning models internally to bringing up new logic and combinations of algorithms, we provide superficial expertise in machine learning algorithms/models.
Make sure you always "git pull" so you have the latest version!
Tips & Traps (for getting through the course):
- Watch videos at 2x.
- Make handwritten notes to increase concepts retaining abilities about information.
- Write formulae, logic, and equations, if you do not! We guarantee it will just look like gibberish.
- Ask relevant questions on the discussion board. As they say, the more the better!
- Realizing practices will take only a few days or weeks to accomplish this course.
Useful course ordering:
- Linear Regression in Python
- Logistic Regression in Python
- Supervised Machine Learning in Python
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- Convolutional Neural Networks in Python
- Easy NLP
- Cluster Analysis and Unsupervised Machine Learning
- Unsupervised Deep Learning
- Hidden Markov Models
- Recurrent Neural Networks in Python
- Natural Language Processing with Deep Learning in Python
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StartNaive Bayes (9:00)
StartNaive Bayes Handwritten Example (3:28)
StartNaive Bayes in Code with MNIST (5:56)
StartNon-Naive Bayes (4:04)
StartBayes Classifier in Code with MNIST (2:03)
StartLinear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (6:07)
StartGenerative vs Discriminative Models (2:47)
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- Amanda Smith
Tons and tons of advice. Make sure to take notes. Very comprehensive course with lots of great advice, a lot of things I never thought of trying. Definitely a don't miss for authors, musicians, or those with any other small business.
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