The technology industry is among the most rapidly growing businesses today. The rapid innovations in this industry have given us several state-of-the-art technology products and services.
The world is captivated by the introduction of several advanced technologies due to their capabilities. Among them are technologies like artificial intelligence (AI) and its sub-domains, such as machine learning and deep learning, that have displayed tremendous potential.
We have witnessed the massive evolution of the artificial intelligence industry and its transformation into a multi-billion-dollar industry in no time. In recent times, artificial intelligence is making a mark in every possible industry.
Today, AI is everywhere, be it industrial equipment, finance, technology, or healthcare. AI has shown the ability to perform several laborious tasks at ease and efficiency, and accuracy. Therefore, AI is the next-generation technology here to stay for several more decades.
AI’s ability is evident from its recent contributions, but the built AI models are powered by machine learning and deep learning algorithms. The critical factor behind the AI models’ success is its powerful algorithms and the programming languages used for building such models.
It is essential to have an efficient, easy to understand, and capable programming language that can build complex machine learning models with more freedom and with utmost ease as complex coding structures will not result in an effective and capable computational model.
Therefore, the most widely used programming language that has emerged as the primary language behind such model building is Python.
Machine learning is considered a field in itself, and the need for machine learning scientists and engineers is on the rise in the job market. Among the required skills, machine learning engineers are expected to have in-depth knowledge about Python, and it is a must-have skill required by every company.
The aspirants looking to gain entry into this market of enormous potential and lucrative job opportunities must aim to build their Python programming skills and related libraries and their uses.
Several courses are offered online. However, most do not cover essential algorithms that are crucial for being a successful machine learning engineer. Finding a course online among multiple offerings is not an easy task as it often turns out to be different from what is being displayed in the curriculum.
The building of practical knowledge and hands-on experience is significant for being considered for leadership roles. Therefore, Practical Machine Learning with Scikit-Learn on Udemy is worth mentioning to build upon the crucial machine learning algorithms and the related libraries for a comprehensive understanding.
What to Expect from the Course?
The course is a beginner-oriented course covering machine learning algorithms, data preprocessing, and relevant libraries crucial for model building.
Furthermore, the course explores the concepts of classification, regression, component analysis, and boosting performed using scikit-learn. The algorithms that are going to be covered in the course are.
- Linear Regression
- Polynomial Regression
- Multiple Linear Regression
- Logistic Regression
- Support Vector Machines
- Decision Trees
- Random Forest
- Principal Component Analysis
- Gradient Boosting
By completing the course, the learners can expect to be well-equipped to implement the algorithms, data preprocessing, and a thorough understanding of which algorithms work best for a particular task. The course prerequisites are basic knowledge of Python and Google Collab Account.
It’s ideal for people looking to get into the AI industry and are looking to find the right learning path and for people looking to gain expertise in building accurate models. The course curriculum is discussed in the following section.
About the Instructor
The course is designed and taught by Adam Eubanks. He is a self-taught programmer and a learning enthusiast. His expertise is mainly in artificial intelligence, Ruby on Rails web development, Python, and Linux.
The courses offered by the instructor are short but specific to the concerned topics. He aims to help the students on topics that are generally considered difficult when getting started as a beginner.
Section 1: Introduction
The first part of the tutorial is the introductory section. The instructor explains the purpose of keeping a short tutorial because several courses on the online platform are longer yet do not cover the essential machine learning algorithms.
The course is created to impart the Python basics and complex and useful machine learning algorithms. Machine learning models are expected to perform well with accuracy.
Therefore the knowledge of the algorithms covered in the course will ensure that learners know which algorithms to use for a specific problem and achieve accurate predictions from the machine learning models.
The tutor explains that the first part will cover the essential aspects of data preprocessing as it is vital to have the correct data fed into the machine learning model.
The concepts of dealing with missing values and the algorithms will be covered in a specific order to make it easy to understand. The entire course is practiced on Google Colab, and the codes for the course are available on GitHub.
The tutor covers the primary issues related to data and missing value, a common problem faced with the datasets. In the real world scenario, the dataset used for computational models are large datasets, and often there are missing values in them.
The tutor explains that if such data is used with missing values, it will lead to error as the machine learning models can’t handle missing data.
For handling such cases, it is suggested that the future data points can be removed if they are some of the missing data in the dataset. For example, a problem targeting to find out the customer satisfaction and only a minimal percent of people have taken part in the survey.
Then the data is not going to result in accurate machine learning predictions. Another critical factor to remember is the individual data points can be removed when there is a situation that involves limited or no useful information compared to the targeted problem, i.e., if a particular problem requires 10 features. Still, an individual point barely has one feature, then it is not considered meaningful data, and it can be removed.
The third option for handling missing data is dependent on the scenario at hand. For example, suppose a dataset has only selective data points missing, and the dataset can still contribute to the model without impacting the overall features required for its prediction accuracy. In that case, the data should be handled in a slightly different manner.
The approach for handling such an issue is by setting the missing value using the mean or the median of the data set and then using the input as the missing value. The mean value computation and the median value as the missing value are explained further and demonstrated with Python. The instructor provides additional links for reference.
The module explores categorical variables as well. In machine learning problems, all variables are not just a set of numbers but categorical too. For instance, color can’t have anything in between to define them as it is either red or yellow.
Therefore, the columns need to be converted as per red and yellow categories. The example is explained with the help of an example.
The conversion comprises a binary format for the model to identify the data’s categorical variables quickly. Finally, the topic of feature scaling is covered.
The tutor explains that feature scaling will not work for every problem, depending on the algorithm and the problem. The idea behind feature scaling is to interpret specific data efficiently for the machine learning model.
For example, if there are two variables, namely population, and average age, these variables are on different scales.
Such factors make it difficult for the model to interpret. Thus, feature scaling helps to take only the essentials for computation. The purpose is to get both the variables to be on the same scale.
The tutor further explains the correlation between the variables and how it ensures accurate predictions compared to the weaker correlation.
It is important to note that no miscellaneous features are taken into consideration while selecting features. Also, it is imperative not to include more features than the data points.
If the problem is on sentiment analysis, and there are 10 features, but over a thousand data points, it should be avoided. Therefore, the key takeaway is to avoid unrelated variables such as predicting a person’s weight based on the time of waking up.
Such areas are critical and must be checked thoroughly while building a machine learning model.
Another critical point that is shared is that features should not be extraordinarily correlated and create overfitting problems. Overfitting is a scenario when the model is completely accurate on the data that is being fed but fails to perform on the new data.
Section 2: Regression
The concepts of regression are covered in this section. The tutor begins by explaining a regression problem. Ideally, a regression problem is associated with factors that are termed as sliding values such as height, weight, or price. In simple terms, these factors can have something in between and not as straightforward as a categorical variable.
The simplest regression algorithm is linear regression. According to the tutor, the linear regression algorithm is easy and very fast to work with and help with various problems. The problem is discussed with the help of a practical example and plotting and the line for data points and predicting the targeted problem’s outcome.
The tutor explains that it is useful to use the preprocessed data from his GitHub profile links. The data is converted into a CSV format for easy readability for the algorithm.
The example is based on predicting the height of a father and son, and the tutor explains the correlation of the variables in the data for the problem. The real example is shown with a follow-along example.
The next concept is on polynomial regression that is covered with an example as well. Additionally, the instructor focuses on using a scatterplot for the given problem and analyzing the accuracy of the data with the help of linear regression. The concept of multiple linear regression is covered to explain the analysis of the accuracy.
The learners need to perform the implementation of the coding as instructed by the tutor for the example. The essential techniques to check the overfitting of a machine learning model’s data using scikit-learn is explained in detail.
Furthermore, it is demonstrated how to split the data into test data and training data. Finally, the concept of mapping polynomial features and adding multiple X and Y features for predictions is also covered in-depth.
Section 3: Classification
The third section of the tutorial covers the concepts of classification. The classification problem differs from the regression problems related to identifying the objects as the prediction outcome.
Some of the common usages of classification are found for image processing tasks using machine learning models. The classification techniques are widely used for healthcare-related tasks, such as identifying a cancer tumor and benign or malignant. The tutor works on a cancer-related data set for the practical example.
The data is split into train and test data sets for the given problem. It is further explained that overfitting must be avoided as it will lead to misclassification by the model. There is a need for augmentation of the features to identify the correlation between them.
Further, it is covered that logistic regression is a classification algorithm. Therefore it is used for the problem in the current section too. The problem is covered entirely with a follow-along example for better understanding and gaining hands-on experience. The tutor shares additional documentation for the classification techniques.
Furthermore, the confusion matrix and its uses are explained, and the need to use it to identify the model’s accuracy. It is explained that a confusion matrix provides the details on false predictions and how far the model could predict correctly.
It is shown in the form of positive values that were predicted to be positive and represent the negative values that were predicted to be negative.
The tutor emphasizes that using a confusion matrix is essential, especially for individuals working on cancer prediction or healthcare-related issues with a machine learning prediction model.
The next topic covered under classification is the use of the Support Vector Machine (SVM). The use of SVM for classification problems are hugely popular among machine learning engineers.
SVMs can also be used for aggression problems. The concepts of SVM and its implementation is covered with an example. The tutor also explores kernels’ topics, and additional tips and tricks are offered for using them.
Additional resources are shared as well. The instructor adds that small test data sets will not witness a massive difference in logistic regression and SVM results. However, more complex data with more complex features are large datasets.
Overall, it is explained that SVM performs better than logistic regression. It is easier to use logistic regression as it is simple and doesn’t require a lot of processing power.
Finally, the topic of decision trees is explored in-depth. It is explained that decision trees are mighty and capable of performing classification tasks. The purpose of the decision tree is to break up actions and features to achieve different classifications.
For example, if a decision tree is to be made for deciding if a person is fit or unfit in terms of health conditions, the factor that is taken into account is food intake. If the food intake is high and fast food, then it is considered unfit, whereas if the person doesn’t have such a food consumption routine, he/she is deemed fit.
Similarly, if someone is into daily fitness, they are fit, otherwise unfit. The tutor explains that even though the examples are simple and easy to understand, there will be more complex problems targeted using a decision tree in real-world scenarios.
A decision tree allows to map out complex features and what a dataset is trying to show when it comes to classifying new data points. It is further explained with a follow-along example. The additional concepts on entropy are covered as well.
Additionally, the topic of ensemble learning is covered as well, and the need for optimization. The instructor provides information for learners that a classification model is prone to overfitting. Therefore it is crucial to have an accurate test and train sets for classification problems.
Random forest is explained and compared to see if the results are better than a single decision tree. It is also discussed that the overall support vector machine is among the best for classification algorithms.
Section 4: Boosting and Optimization
The final section covers important concepts on optimization. The need for optimization is to elevate the algorithms’ performance to the maximum and aim for more prediction accuracy. The cancer data is used once again for the problem with an aim for higher accuracy.
The data is split into the train, and test sets and further implementations are performed. The tutor kept 20 percent of the testing data and the remaining for training the machine learning model. The results are evaluated for checking the accuracy.
The instructor also uses principal component analysis for reducing the features for achieving higher accuracy. The idea behind principal component analysis is explained in detail.
This technique’s primary use is to reduce the dataset’s dimensionality when there are a lot of correlated features in the dataset. It is further explored with the help of an example.
Finally, the concept of gradient boosting is introduced. It helps to identify how well the model has performed on the test dataset. All of the concepts are put together and compiled for a final performance check. The example covers each course topic with a thorough implementation resulting in a hands-on experience for learners.
Benefits of this course
The advantage of this course on Practical Machine Learning with scikit-learn is the content that has been designed for a beginner course. The course doesn’t overwhelm the newcomers as specific introductory courses focus on many concepts that confuse them. It follows a straightforward approach.
Although the course is short, many important concepts are covered in the course with detailed explanations and practical examples. There are provisions for additional resources as well.
The core concepts are explained well. A significant benefit is that all the concepts are also demonstrated with an example that learners can practice and improve their coding skills.
Hands-on experience is essential for a career in artificial intelligence or as a machine learning engineer. Hence this course builds upon these skills. There are simplified examples and a follow-along approach.
Another factor that this course could be preferred is the duration of the content. There are no lengthy explanations and complicated examples. All the content is presented in a simplified manner.
The course will provide certification of completion. It is a free tutorial available on Udemy. However, there is access to online content, certification, direct access to the instructor for Q&A, and direct replies from the instructor. It is important to note that access to online content is available for the free version, and the remaining options from the paid version are not being offered.
You will get access to the learning community on Udemy consisting of students and tutors. The benefits of such access to a learning community are the availability of essential resources and tips shared in the forum.
You can also post your questions on the forum and get explanations from experienced professionals and tutors who are actively communicating on this platform.
The course provides a hands-on learning experience. This course is designed to provide sufficient exposure to writing the codes and understanding the concepts taught theoretically.
A hands-on approach lets you grasp the concept much faster. The examples that are being covered are also shown practically, thus enhancing the overall learning process. The course is a perfect balance of theory and practical knowledge and provides crucial information and additional resources.
Overall Rating of the Course
- Instructor Expertise: 5
- Additional Resources: 3
- Course Content Quality: 4
- Career Value: 5
- Delivery of the Content: 4
- Visuals and Readability: 5
- Examples: 5
- Speed of Delivering the Content: 5
- Basic Concepts: 5