Machine Learning, Data Science and Deep Learning with Python

  • Created by Frank Kane
  • Course Duration 15.5 hours
  • Price USD$NT$590
  • User Rating 4.6
  • Platform Udemy
  • Course Link Explore Course
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Are you fascinated by the world of Machine Learning and Data Science? Do you want to unlock the secrets hidden within massive datasets? Look no further! The comprehensive online course on Machine Learning, Data Science, and Deep Learning with Python is here to equip you with the necessary skills and knowledge to master the art of AI and data analysis.

Course Overview

The course offers a structured and in-depth exploration of machine learning and data science concepts. With over 100 lectures and 15 hours of video content, this hands-on tutorial provides a practical approach to learning. Throughout the course, you’ll have access to real-world examples and Python code demonstrations, enabling you to apply what you’ve learned in a meaningful way.

What You’ll Gain

By enrolling in this course, you’ll acquire a wide range of skills that are essential for machine learning and data science practitioners. Here are just a few key areas you’ll delve into:

Build artificial neural networks with Tensorflow and Keras

Artificial neural networks are the backbone of modern machine learning. With the guidance of experienced instructors, you’ll learn how to construct and train neural networks using industry-standard tools like Tensorflow and Keras. By the end of the course, you’ll be able to create sophisticated models that can handle complex tasks.

Implement machine learning at massive scale with Apache Spark’s MLLib

As datasets continue to grow in size, it’s crucial to learn how to scale machine learning algorithms. This course introduces you to Apache Spark’s MLLib, a powerful tool for distributed machine learning. You’ll explore how to leverage Spark’s capabilities to handle big data and gain insights from large-scale datasets.

Classify images, data, and sentiments using deep learning

Deep learning has revolutionized various fields, including image and text classification. You’ll gain hands-on experience in training deep neural networks to classify images, analyze sentiments, and categorize data. Unlock the power of deep learning and witness its applications in real-world scenarios.

Make predictions using regression analysis

Regression analysis plays a vital role in making predictions and understanding relationships between variables. This course guides you through the implementation of linear regression, polynomial regression, and multivariate regression models. You’ll learn how to extract valuable insights and make accurate predictions from your data.

Data Visualization with MatPlotLib and Seaborn

Data visualization is an essential skill for data scientists. Through the course, you’ll discover how to use powerful Python libraries such as Matplotlib and Seaborn to create meaningful visualizations. Visual representations of data help uncover patterns, trends, and outliers, facilitating better decision-making.

Who This Course is For

This course caters to individuals from various professional backgrounds, each with their unique motivations for learning machine learning and data science. Whether you’re a software developer looking to transition into the lucrative field of data science, a technologist intrigued by deep learning, or a data analyst seeking a career change into the tech industry, this course offers valuable insights.

Prerequisites for this course include some coding or scripting experience. If you’re new to programming, don’t worry! The course includes a crash course that will help you get up to speed quickly. Additionally, high school-level math skills are recommended to grasp the mathematical concepts involved in machine learning.

Now, let’s delve into the exciting topics covered in this course.

Machine Learning Fundamentals

Before diving into the intricacies of machine learning algorithms, it’s important to understand the core concepts. This section of the course introduces you to the foundations of machine learning,including supervised and unsupervised learning algorithms. You’ll explore the different types of learning tasks such as regression, classification, and clustering. Additionally, the course provides an overview of decision trees, random forests, and support vector machines, which are widely used in the industry.

Deep Learning and Neural Networks

Deep learning has revolutionized the field of artificial intelligence, enabling breakthroughs in image recognition, natural language processing, and more. In this section, you’ll gain a comprehensive understanding of artificial neural networks, the building blocks of deep learning. The course covers multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). With hands-on exercises, you’ll learn how to implement these models using popular deep learning frameworks such as TensorFlow and Keras.

Data Visualization and Analysis

Data visualization is a powerful tool for understanding complex datasets. In this module, you’ll discover the importance of visualizing data and learn how to create compelling visualizations using Python libraries like Matplotlib and Seaborn. By mastering data visualization techniques, you’ll be able to communicate your findings effectively and extract valuable insights from your data.

Real-World Applications

Machine learning and data science have numerous real-world applications. This section of the course explores practical use cases, including sentiment analysis, image recognition and classification, regression analysis, and clustering. You’ll learn how to apply these techniques to solve real problems and gain practical experience in working with different types of data.

Model Evaluation and Deployment

Building accurate machine learning models is only part of the process. Evaluating and deploying these models effectively is equally important. This module focuses on model evaluation techniques, such as train/test splits and cross-validation, to ensure the robustness of your models. You’ll also explore Bayesian methods for statistical inference, reinforcement learning algorithms, and collaborative filtering techniques for recommender systems.


Congratulations! You’ve reached the end of your journey into the world of machine learning and data science. Throughout this course, you’ve gained valuable skills and knowledge that will set you on a path to success in this rapidly growing field. Machine learning and data science are essential in today’s tech-driven world, and the demand for skilled professionals continues to rise.

Are you ready to take the next step? Enroll now in the comprehensive online course on Machine Learning, Data Science, and Deep Learning with Python. Gain lifetime access to the course materials, including video lectures, code examples, and additional resources. Whether you’re looking to advance your career or explore new opportunities, this course is your gateway to success.

Don’t miss out on this opportunity to master machine learning and data science. Enroll now and unlock your potential!


Q: Do I need prior coding experience to take this course?

A: While some coding or scripting experience is required, the course also includes a crash course for beginners. This crash course will help you quickly grasp the basics of programming and set a strong foundation for learning machine learning and data science.

Q: What software do I need for this course?

A: To complete this course, you’ll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will guide you through the process of installing the necessary software, including Anaconda and other free tools.

Q: How long will I have access to the course materials?

A: When you enroll in this course, you’ll gain lifetime access to all the course materials, including video lectures, code examples, and downloadable resources. You can revisit the content anytime, allowing you to learn at your own pace and reinforce your knowledge whenever needed.

Q: Are there any prerequisites for this course?

A: To make the most of this course, it’s recommended to have at least high school-level math skills. Understanding mathematical concepts like algebra and statistics will greatly assist you in comprehending the algorithms and techniques covered. Some coding or scripting experience is also beneficial, as the course assumes basic programming knowledge.

Q: Can this course help me switch to a career in data science?

A: Absolutely! This course is designed to equip you with the essential techniques and skills required in the field of data science. By mastering machine learning algorithms, data visualization, and real-world applications, you’ll be well-prepared for a career transition into data science. The demand for data scientists is rapidly increasing, and this course will help you stand out in the job market.

Enroll now and embark on an exciting journey into the world of machine learning and data science!