LEARNING PATH: Python: Advanced Machine Learning with Python

This video gives a glimpse of what you will learn through this video.

This video, will let you kick off your Python and machine learning journey with the basic, yet important concepts of machine learning.

This is the first step with few practical tasks to get started. Yes! We are talking about installation and initial set up, which we’ll be doing together in this video.

Natural language processing or NLP, is a significant subfield of machine learning, which deals with the interactions between machine and human natural languages. We will explore this, through this video.

After a short list of real-world applications of NLP, we will be touring the essential stack of Python NLP libraries in this video.

Let’s get a step ahead by getting the Newsgroups data and downloading it.

This video will walk you through the steps to achieve the extraction of features from the dataset.

Let’s get a general idea of how the data is structured, what possible issues may arise, and if there are any irregularities that we have to take care of.

This video will let you improve the most indicative Features from visualization by using the data preprocessing Techniques.

How could you find the best division or a decent Approximation between set of data in a dataset? That’s what we are going to learn in this video.

When we read a text, we expect certain words appearing in the title or the body of the text to capture the semantic context of the document. This video, will let you achieve this in Machine Learning and Python programming.

It is a great starting point of learning classification with a real-life example-our email service providers are already doing this for us, and so can we. We will be learning the fundamental and important concepts of classification.

How can we make a prediction of probability distribution over all classes, besides the most likely class that the data sample is associated with? Let’s answer this question, through this video!

This video will let you understand the magic behind the algorithm-how naive Bayes works.

The most important task after learning a concept is to implement it. Let’s try the implementation of Naïve Bayes algorithm with some sample codes, in this video.

Beyond accuracy, there are several measurements that give us more insights and avoid class imbalance effects. Let’s see these performance measures right now!

Having learned what metrics are used to measure a classification model, we can now study how to measure it properly. Let’s do it right away!

This video will show you how to adopt a more comprehensive approach to extract text features, the term frequency-inverse document frequency

Since there can be infinite number of feasible Hyperplanes, how can we identify the optimal one? Let's do it in this video!

Let's put into action, the fundamentals of SVM Classifier right away on news topic classification and learn to deal with more than two classes.

What could you do if you are not able to find any linear hyperplane to separate two classes? Let’s see the solution to this problem!

This video will walk you through different scenarios where the linear kernel is favored over RBF.

It is finally time to build our state-of-the-art, SVM-based news topic classifier with all we just learned. Let’s do it!

After a successful application of SVM with the linear kernel, let’s look at one more example where SVM with the RBF kernel is suitable for it

How can you find whether a given ad on a given page or app will be clicked by a given user or not, with predictive features? Let’s answer this question.

When working on a prediction model, how can you get all of the possible decision alternatives and the corresponding outcomes? Let’s see this, now.

With a solid understanding of partitioning evaluation metrics, let's practice the CART tree algorithm by hand on a simulated dataset.

After several examples, it is now time to predict ad click-through with the decision tree algorithm we just thoroughly learned and practiced.

How will you reduce the high variance that a decision tree model suffers from and hence in general performs better than a single tree? Let’s see the solution for this problem now!

This video will show you how one-hot encoding converts the categorical feature to k binary features

Let’s turn to a new algorithm with high scalability to large datasets. Yes! We are talking about Logistic regression which is one of the most scalable classification algorithms.

Let’s deploy the algorithm that we just developed and test it in our click-through prediction project.

How can we rank the importance of features based on their occurrences in nodes among all trees, and select the top most important ones? Let’s see this right now!

Let’s explore the relationships between the observations and the targets, and to output a continuous value based on the input features of an unknown sample.

This video will walk you through some essential steps to apply regression techniques in predicting prices of a particular stock.

Let’s get started with obtaining the dataset we need for our project.

Since all features and driving factors are available, let’s focus on regression algorithms that estimate the continuous target variables from these predictive features.

After linear regression, the next regression algorithm need to learn is decision tree regression. Let’s start exploring it right away!

Let’s get started with the third regression algorithm which is support vector regression (SVR).

So far, we have covered several popular regression algorithms in-depth and implemented them from scratch by using existing libraries. Let’s step ahead and evaluate the performance of these regression algorithms.

Now that we have learned three commonly used and powerful regression algorithms and performance evaluation metrics, why don't we utilize all of these in solving our stock price prediction problem? Let’s do it in this video.

Apparently, no machine learning system can be built without data. Data collection should be our first focus. Let’s learn the best practices for the data preparation stage.

With well-prepared data, it is safe to move on with the training sets generation stage. Let’s move ahead and explore this.

Given a machine learning problem, the first question many people ask is usually: what is the best classification/regression algorithm to solve it? This video will bring to you an answer for this question.

After all the processes in the previous three stages, we now have a well established data preprocessing pipeline and a correctly trained prediction model. The last stage of a machine learning system involves saving those resulting models from previous stages and deploying them on new data, Let’s see how to do this.