Machine Learning with Python
Leverage Python to build intelligent programs! This book equips you with machine learning techniques. Train algorithms to learn from data and solve real-world problems.
Why you should have this book
Level up your coding skills
Build strong coding abilities & tackle projects with confidence.
Become a confident programmer
Grasp key concepts & avoid common pitfalls. Be unstoppable.
Solid foundation
Learn once, code anywhere. Unlock your programming potential.
Harness Data Power: Explore Machine Learning with Python
Through engaging explanations, practical exercises, and real-world applications, you'll gain a thorough understanding of fundamental concepts like:
- Supervised learning: Explore algorithms like linear regression and decision trees that learn from labeled data to make predictions for new, unseen data points.
- Unsupervised learning: Discover techniques like clustering and dimensionality reduction that uncover hidden patterns and structures within unlabeled data.
- Model evaluation and deployment: Learn how to assess the performance of your machine learning models and effectively deploy them in real-world scenarios.
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By the end of this journey, you'll be well-equipped to not only understand machine learning concepts but also confidently apply them to solve real-world problems. This book empowers you to become an active participant in the exciting world of data science and use machine learning to unlock the potential of your data.
Master Python for Powerful Machine Learning
Imagine machines that can learn and adapt on their own. "Machine Learning with Python" provides you with the essential Python skills and knowledge to make this a reality. This practical guide equips you to master the fundamentals of machine learning and build powerful applications using Python.
Through hands-on projects and real-world case studies, you'll delve into the core concepts of machine learning:
- Building and training machine learning models: Learn how to use popular Python libraries like scikit-learn and TensorFlow to create and train models that can learn from data.
- Fine-tuning and optimizing models: Discover techniques for improving the performance of your models, ensuring they produce accurate and reliable results.
- Applying machine learning to real-world problems: Explore how machine learning can be used in various domains, from image and speech recognition to recommendation systems and natural language processing.
In today's data-driven world, the ability to extract meaningful insights from data is crucial. "Machine Learning with Python" empowers you to unlock the power of data and delve into the fascinating world of machine learning. This comprehensive guide equips you with the essential skills and knowledge to leverage Python, a versatile and widely used programming language, for building intelligent systems that learn from data.
By the end of this journey, you'll be well-equipped to confidently build and deploy machine learning models using Python. "Make Machines Learn" empowers you to become a skilled machine learning practitioner and make a real impact with your data science expertise.
Table of contents
Chapter 1: Introduction
1.1 Introduction to Machine Learning
1.2 Role of Machine Learning in Software Engineering
1.3 Overview of Python for Machine Learning
Chapter 2: Python and Essential Libraries
2.1 Python Crash Course
2.2 NumPy for Numerical Computation
2.3 Pandas for Data Manipulation
2.4 Matplotlib and Seaborn for Data Visualization
2.5 Scikit-learn for Machine Learning
Chapter 3: Data Preprocessing
3.1 Data Cleaning
3.2 Feature Engineering
3.3 Handling Categorical Data
3.4 Data Scaling and Normalization
3.5 Train-Test Split
Chapter 4: Supervised Learning
4.1 Regression Analysis
4.2 Classification Techniques
4.3 Evaluation Metrics for Supervised Learning
4.4 Practical Exercises of Chapter 4: Supervised Learning
Chapter 5: Unsupervised Learning
5.1 Clustering Techniques
5.2 Dimensionality Reduction
5.3 Evaluation Metrics for Unsupervised Learning
5.4 Practical Exercises of Chapter 5: Unsupervised Learning
Chapter 6: Introduction to Neural Networks and Deep Learning
6.1 Perceptron and Multi-Layer Perceptron
6.2 Backpropagation and Gradient Descent
6.3 Overfitting, Underfitting, and Regularization
6.4 Practical Exercises of Chapter 6: Introduction to Neural Networks and Deep Learning
Chapter 7: Deep Learning with TensorFlow
7.1 Introduction to TensorFlow
7.2 Building and Training Neural Networks with TensorFlow
7.3 Saving and Loading Models in TensorFlow
7.4 Practical Exercises of Chapter 7: Deep Learning with TensorFlow
Chapter 8: Deep Learning with Keras
8.1 Introduction to Keras
8.2 Building and Training Neural Networks with Keras
8.3 Saving and Loading Models in Keras
8.4 Practical Exercises of Chapter 8: Deep Learning with Keras
Chapter 9: Deep Learning with PyTorch
9.1 Introduction to PyTorch
9.2 Building and Training Neural Networks with PyTorch
9.3 Saving and Loading Models in PyTorch
9.4 Practical Exercises of Chapter 9: Deep Learning with PyTorch
Chapter 10: Convolutional Neural Networks
10.1 Introduction to CNNs
10.2 Implementing CNNs with TensorFlow, Keras, and PyTorch
10.3 Practical Applications of CNNs
10.4 Practical Exercises of Chapter 10: Convolutional Neural Networks
Chapter 11: Recurrent Neural Networks
11.1 Introduction to RNNs
11.2 Implementing RNNs with TensorFlow, Keras, and PyTorch
11.3 Practical Applications of RNNs
11.4 Practical Exercise of Chapter 11: Recurrent Neural Networks
Chapter 12: Advanced Deep Learning Concepts
12.1 Autoencoders
12.2 Generative Adversarial Networks (GANs)
12.3 Practical Exercise of Chapter 12: Advanced Deep Learning Concepts
Chapter 13: Practical Machine Learning Projects
13.1 Project 1: Predicting House Prices with Regression
13.2 Project 2: Sentiment Analysis with Naive Bayes
13.3 Project 3: Image Classification with Convolutional Neural Networks
Chapter 14: Future Trends and Ethical Considerations
14.1 Reinforcement Learning
14.2 Explainable AI
14.3 Ethical Considerations in Machine Learning
14.4 Future Trends in Machine Learning for Software Engineering
What our readers are saying about this book
Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.
"Machine Learning with Python and AI" is a fantastic guide if you are getting into the world of machine learning. AI is an incredibly relevant topic now and dominates most conversations in various industries around the world. If you are interested in diving into this world or brushing up on your subject matter this guide is for you!
"Machine Learning with Python" is an excellent book for beginners and intermediate level data scientists who want to learn the fundamentals of machine learning and its practical application. The book covers the theoretical concepts of machine learning and provides a hands-on approach to implementing machine learning algorithms using Python. Well-structured book!
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