Machine Learning and Deep Learning Bootcamp in Python

Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement Learning in Keras and TensorFlow

4.51 (1402 reviews)
Data Science
Machine Learning and Deep Learning Bootcamp in Python
32.5 hours
Nov 2023
last update
regular price

What you will learn

Solving regression problems (linear regression and logistic regression)

Solving classification problems (naive Bayes classifier, Support Vector Machines - SVMs)

Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks

The most up to date machine learning techniques used by firms such as Google or Facebook

Face detection with OpenCV

TensorFlow and Keras

Deep learning - deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs)

Reinforcement learning - Q learning and deep Q learning approaches

Why take this course?


  1. 深度神经网络:深度神经网络(DNNs)是由多层非线性转换组成的神经网络,能够学习数据中的复杂模式。它们在图像识别、自然语言处理等领域有着广泛的应用。

  2. ReLU激活函数和梯度消失问题:ReLU(Rectified Linear Unit)是一种常用的激活函数,它帮助解决深层网络中的梯度消失或爆炸问题。这些问题会影响模型的训练效率和性能。

  3. 训练深度神经网络:训练深度网络需要有效的方法来优化损失函数,避免过拟合等问题。

  4. 损失函数(代价函数):损失函数衡量模型预测与实际数据之间的差异,目标是最小化这个差异。常见的包括均方误差、交叉熵等。

  5. 卷积神经网络(CNNs):CNNs通过卷积层和池化层来提取图像中的有用特征,减少参数数量并避免过拟合。它们在图像识别、分类等任务中表现出色。

  6. 循环神经网络(RNNs):RNNs通过在节点之间保持信息连接的方式处理序列数据,特别适合处理时间序列和自然语言等具有顺序性的任务。

  7. 循环神经网络的梯度爆炸问题:由于权重的累积,在训练RNNs时可能会遇到梯度爆炸的问题,影响模型的学习过程。

  8. 长短期记忆(LSTM)和门控循环单元(GRUs):LSTM和GRUs是特殊类型的RNNs,它们能够更好地处理长期依赖问题。

  9. 数值优化(在机器学习中):数值优化是训练神经网络的核心部分,包括选择合适的优化算法(如SGD、Adam等)和调整学习率等。

  10. 计算机视觉相关的深度学习模型:如YOLO(You Only Look Once)和SSD(Single Shot MultiBox Detector),这些模型专门用于实时物体检测任务。

  11. 课程内容:这个课程提供了150+的视频讲解、讲义和源代码,覆盖了从基础到高级的机器学习和深度学习知识,以及计算机视觉中的最新技术。

  12. 优势:这个课程的优势在于它将理论知识与实际应用相结合,帮助学习者在实践中提高技能,同时也提供了30天的退款保证。



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Our review

📈 Course Overview:

This online course offers a comprehensive introduction to machine learning (ML), covering both the theoretical foundations and practical applications. The course is well-rated, with an average global rating of 4.51 and recent reviews praising its conceptual clarity, use of examples, and mathematical background. However, some users have suggested improvements for more complete and complex practical examples, the inclusion of Jupyter Notebooks, better plot libraries like Seaborn, and dataframes with pandas.


  • 🧠 Solid Theoretical Foundation: The course provides a good coverage of the necessary theory for applying ML techniques in industry settings.
  • 📝 Practical Examples: Many users appreciate the practical examples and illustrations that help understand the concepts.
  • 🤝 Comprehensive Topics: It covers all major topics within machine learning, including neural networks and deep learning.
  • 🌍 Global Audience: The course is accessible to a wide audience, with content available in English and potentially other languages.
  • 📈 Wide Range of Learning Styles: The course includes a variety of learning materials, from theoretical explanations to practical examples, catering to different learning preferences.
  • Unique Perspectives: Some users find the instructor's unique approach and perspective particularly valuable.


  • 🛠️ Code Updates: A few users have noted that some code provided in the course may be outdated or require updates to run effectively.
  • 🧪 Desire for More Math: Some learners wish for a more detailed explanation of the mathematical concepts behind ML, which they believe would enhance understanding.
  • ✍️ Code Explanations: A desire for more detailed explanations of certain functions and models within the code is a recurring suggestion.
  • 📚 Assignments: Users have suggested the addition of assignments to provide a practical feel of ML algorithms without relying on Python libraries.
  • 🤫 Accent and Accessibility: A few users pointed out that the instructor's accent might be challenging for some, and closed captions would be helpful.
  • 📚 Python Tutorial Placement: Some learners believe the placement of the Python tutorial within the course could be improved for better learning flow.
  • 🛠️ Code Consistency: Concerns have been raised about the instructor's coding approach, with some users finding it to be inconsistent and potentially confusing.

User Experience:

The user experience appears to be largely positive, with many learners finding the course content valuable and well-presented. However, there are notable concerns regarding the practicality and up-to-date nature of the code examples. The course's length, with appendices totaling several hours, is seen as extensive by some users, who question its necessity for completing the course and obtaining a certificate.

In conclusion, this machine learning course is highly regarded for its comprehensive approach to teaching ML, but users advocate for updates in code practices, more detailed explanations, and additional hands-on practice through assignments. It's a solid choice for learners looking for both theoretical and practical knowledge in the field of machine learning.



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