Udemy Machine Learning A-Z: Hands-On Python & R In Data Science

Udemy

Category: Data Science Tags: Beginner, Online Course

About this course

The Udemy Machine Learning course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. Let’s begin with the basics of this topic from Machine Learning to the use of Python and R for Machine Learning.

About Machine Learning

Machine learning is an application of artificial intelligence (AI) that consists of various learning methods (Supervised, unsupervised, semi-supervised & Reinforcement learning). The machine learning algorithms provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that will be provided. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Python and R for Data Science

Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools. Here Python and R languages come into the picture, as they are the two most popular tools open-source and free tools used by data scientists. Both Python programming and R programming share many characteristics. These languages have developed robust ecosystems of open source tools and libraries that help data scientists of any skill level more easily perform analytical work. Python, being more concerned with predictive accuracy, has developed a positive reputation in machine learning, while R, as a language, has been used by many for statistical inference, and has gained reputation and trust in data analysis.

When to use Python?

Python programming is widely used throughout the industry and enables easy collaboration within development teams. If you need a flexible, multi-purpose programming language surrounded by a large community of developers and extendable with machine learning packages, Python is a top pick.

When to use R?

If your project is statistics-heavy, R programming is a better candidate for the task. R is also an excellent choice for narrow problems that require a one-time dive into a dataset. For example, if you’d like to analyze a corpus of text by deconstructing paragraphs into words or phrases and identifying patterns, R is a top pick. (Source: python vs R for data science)

Udemy Machine Learning Course is for

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning.
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
• People who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
• Students in college who want to start a career in Data Science.
• Data analysts who want to level up in Machine Learning.
• People who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning tools.

Syllabus of the Udemy Machine Learning Course

The Udemy machine learning course has made learning easy and exciting, but at the same time, we dive deep into Machine Learning. It is structured in the following way:

Part 1.  Udemy Machine Learning Course

I. Data preprocessing using Python programming

• In the first part of the Udemy machine learning course, you will learn and practice on importing the libraries and datasets.
• Taking care of missing data, an encoding of the categorical data.
• Splitting the datasets into the training and test sets and feature scaling.

II. Data preprocessing using R programming

• Dataset description and importing the dataset.
• Taking care of missing data, an encoding of the categorical data.
• Splitting the datasets into the training and test sets and feature scaling.

Part 2. Udemy Machine Learning Course

Regression

• Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
• Evaluation of Regression model performance.
• Regression model selection in Python and R.

Part 3. Udemy Machine Learning Course

Classification

• Classification models like Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree, Random Forest.
• Model selection using Python programming.
• Evaluating classification model performance.

Part 4. Udemy Machine Learning Course

Clustering

• The fourth part of the Udemy machine learning course emphasizes on K-Means clustering using Python and R programming.
• Hierarchical clustering using dendrograms.
• Hierarchical clustering in python and R.

Part 5. Udemy Machine Learning Course

Association Rule Learning

• Apriori, Eclat in Python and R.

Part 6. Udemy Machine Learning Course

Reinforcement Learning

I. Upper Confidence Bound

• The multi-armed bandit problem.
• Upper confidence bound (UCB) intuition.
• Upper confidence bound in Python and R.

II. Thompson Sampling

• The intuition behind the Thompson sampling algorithm and how it solves the Multi-Armed Bandit problem.
• Algorithm comparison: UCB vs Thompson sampling.
• Thompson sampling in Python and R.

Part 7. Udemy Machine Learning Course

Natural Language Processing (NLP) and Bag of Words Model

• In the seventh part of the Udemy machine learning course, the user will understand and learn NLP and the well-known model of the Bag of Words model.
• Natural language processing n Python and R

Part 8. Udemy Machine Learning Course

Deep Learning

I. Artificial Neural Networks (ANN)

• General introduction of artificial neural networks
• The match behind different activation functions (Threshold, Sigmoid, Rectifier & Hyperbolic Tangent), how to choose an activation function?
• How do neural networks work and learn?
• Gradient descent to optimize weights to minimize the cost function
• Summary of the learning process of a backpropagation neural network and steps to train.
• Business problem description and ANN in Python and R

II. Convolutional Neural Networks (CNN)

• Introduction of convolution neural networks and convolution operation
• Increasing the non-linearity in images with the ReLU layer.
• Pooling, Flattening, Full connection processes.
• Summary of CNN and its building process.
• Softmax function and use of cross-entropy loss to measure the error at a softmax layer.
• CNN in Python.

Part 9. Udemy Machine Learning Course

Dimensionality Reduction

• The 9th part of the Udemy machine learning course is based on dimensionality reduction viz., Principle component analysis (PCA), Linear discriminant analysis (LDA), Kernel PCA in Python, and R.

Part 10. Udemy Machine Learning Course

Model Selection & Boosting

• k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost.

Moreover, the Udemy machine learning course is packed with practical exercises that are based on real-life examples. So hands-on practice will be done to building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

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