Mastering Machine Learning Python by Mishra Anshuman (14 results)

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Paperback. Condition: new. Paperback. This ambitious two-volume work, "Mastering AI and Machine Learning with Python: From Fundamentals to Advanced Deep Learning," aims to be a definitive guide for anyone seeking to understand, implement, and master the intricate world of Artificial Intelligence (AI) and Machine Learning (ML) using the versatile Python programming language. Spanning a projected 10,000 words across both volumes (with Volume 1 detailed below), this book meticulously progresses from foundational concepts to cutting-edge deep learning techniques, providing readers with a robust theoretical understanding coupled with practical implementation skills.Volume 1: Foundations and Core Machine Learning TechniquesVolume 1 lays the essential groundwork for embarking on the journey of AI and ML. It is structured to take individuals with varying levels of prior knowledge - from complete beginners to those with some programming experience - and equip them with the core competencies required to understand and apply fundamental machine learning algorithms.Chapter 1: Introduction to AI and Machine LearningThis introductory chapter serves as a compass, orienting the reader within the broad landscape of AI and its subfields. It begins by clearly delineating the concepts of Artificial Intelligence, Machine Learning, and Deep Learning, highlighting their relationships and distinctions.Understanding AI, Machine Learning, and Deep Learning: This section meticulously unpacks these often-interchangeable terms. It defines AI as the overarching field focused on creating intelligent agents capable of performing tasks that typically require human intelligence. Machine Learning is then presented as a subset of AI, where systems learn from data without being explicitly programmed. Finally, Deep Learning is introduced as a subfield of ML that utilizes artificial neural networks with multiple layers (deep neural networks) to extract complex patterns from large datasets. The chapter will use analogies and real-world examples to solidify these definitions, ensuring a clear understanding of the hierarchy and unique characteristics of each field.Real-World Applications of AI: To underscore the practical relevance and transformative power of AI, this section delves into a diverse range of real-world applications. It will explore how AI is revolutionizing industries such as healthcare (diagnosis, drug discovery), finance (fraud detection, algorithmic trading), transportation (autonomous vehicles), entertainment (recommendation systems), manufacturing (predictive maintenance), and customer service (chatbots). Each application will be briefly described, highlighting the specific AI techniques employed and the tangible benefits realized. This section aims to inspire the reader and contextualize the learning journey ahead.The Role of Python in AI Development: This crucial segment emphasizes why Python has emerged as the lingua franca of AI and ML. It will discuss Python's key advantages, including its clear and concise syntax, extensive ecosystem of powerful libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), large and active community support, and its versatility for various stages of the AI development lifecycle - from data preprocessing to model deployment. The chapter will briefly introduce some of these key libraries, setting the stage for their detailed exploration in subsequent chapters.Overview of TensorFlow and PyTorch: As two of the most prominent deep learning frameworks, TensorFlow and PyTorch are introduced in this section. The chapter will provide a high-level overview of their functionalities, key features, and their respective strengths and weaknesses. It will touch upon their roles in building and training neural networks, their support for har Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. This ambitious two-volume work, "Mastering AI and Machine Learning with Python: From Fundamentals to Advanced Deep Learning," aims to be a definitive guide for anyone seeking to understand, implement, and master the intricate world of Artificial Intelligence (AI) and Machine Learning (ML) using the versatile Python programming language. Spanning a projected 10,000 words across both volumes (with Volume 1 detailed below), this book meticulously progresses from foundational concepts to cutting-edge deep learning techniques, providing readers with a robust theoretical understanding coupled with practical implementation skills.Volume 1: Foundations and Core Machine Learning TechniquesVolume 1 lays the essential groundwork for embarking on the journey of AI and ML. It is structured to take individuals with varying levels of prior knowledge - from complete beginners to those with some programming experience - and equip them with the core competencies required to understand and apply fundamental machine learning algorithms.Chapter 1: Introduction to AI and Machine LearningThis introductory chapter serves as a compass, orienting the reader within the broad landscape of AI and its subfields. It begins by clearly delineating the concepts of Artificial Intelligence, Machine Learning, and Deep Learning, highlighting their relationships and distinctions.Understanding AI, Machine Learning, and Deep Learning: This section meticulously unpacks these often-interchangeable terms. It defines AI as the overarching field focused on creating intelligent agents capable of performing tasks that typically require human intelligence. Machine Learning is then presented as a subset of AI, where systems learn from data without being explicitly programmed. Finally, Deep Learning is introduced as a subfield of ML that utilizes artificial neural networks with multiple layers (deep neural networks) to extract complex patterns from large datasets. The chapter will use analogies and real-world examples to solidify these definitions, ensuring a clear understanding of the hierarchy and unique characteristics of each field.Real-World Applications of AI: To underscore the practical relevance and transformative power of AI, this section delves into a diverse range of real-world applications. It will explore how AI is revolutionizing industries such as healthcare (diagnosis, drug discovery), finance (fraud detection, algorithmic trading), transportation (autonomous vehicles), entertainment (recommendation systems), manufacturing (predictive maintenance), and customer service (chatbots). Each application will be briefly described, highlighting the specific AI techniques employed and the tangible benefits realized. This section aims to inspire the reader and contextualize the learning journey ahead.The Role of Python in AI Development: This crucial segment emphasizes why Python has emerged as the lingua franca of AI and ML. It will discuss Python's key advantages, including its clear and concise syntax, extensive ecosystem of powerful libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), large and active community support, and its versatility for various stages of the AI development lifecycle - from data preprocessing to model deployment. The chapter will briefly introduce some of these key libraries, setting the stage for their detailed exploration in subsequent chapters.Overview of TensorFlow and PyTorch: As two of the most prominent deep learning frameworks, TensorFlow and PyTorch are introduced in this section. The chapter will provide a high-level overview of their functionalities, key features, and their respective strengths and weaknesses. It will touch upon their roles in building and training neural networks, their su Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Paperback. Condition: new. Paperback. Chapter 9: Convolutional Neural Networks (CNNs)This chapter likely begins by revisiting the fundamental concepts of convolutional operations. It would meticulously explain how convolution works, including the roles of filters (kernels), strides, padding, and activation functions in extracting meaningful features from image data. The concept of feature maps, which represent the output of applying filters at different layers, would be thoroughly discussed, emphasizing how these maps capture hierarchical representations of visual information.The chapter would then transition into exploring various influential CNN architectures.LeNet: This pioneering CNN architecture, designed for handwritten digit recognition, would be presented as a foundational example, illustrating the basic building blocks of a CNN. Its layers, including convolutional layers, pooling layers (like average pooling), and fully connected layers, would be explained in detail. The historical significance of LeNet in the development of modern CNNs would also likely be highlighted.AlexNet: This groundbreaking architecture, which achieved remarkable success in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), would be analyzed for its key innovations. These include the use of ReLU activation functions, dropout for regularization, and the utilization of multiple GPUs for training. The impact of AlexNet on the field of computer vision and the resurgence of deep learning would be emphasized.VGG (Visual Geometry Group): The chapter would delve into the VGG networks, known for their deep and uniform architectures consisting of small convolutional filters stacked together. The concepts of VGG16 and VGG19, along with their consistent use of 33 convolutional kernels, would be explained. The advantages and limitations of VGG networks, such as their depth and large number of parameters, would likely be discussed.ResNet (Residual Network): This architecture, which addressed the vanishing gradient problem in very deep networks through the introduction of residual connections (skip connections), would be thoroughly examined. The concept of identity mappings and how they facilitate the training of extremely deep networks would be explained. Different ResNet variants (e.g., ResNet-50, ResNet-101) and their performance benefits would likely be covered.Finally, the chapter would explore the applications of CNNs in: Image Classification: This fundamental task of assigning a label to an entire image based on its content would be discussed. Different loss functions (e.g., cross-entropy) and evaluation metrics (e.g., accuracy, F1-score) used in image classification would be explained.Object Detection: This more complex task of identifying and localizing multiple objects within an image using bounding boxes would be introduced. Early object detection architectures and the fundamental challenges involved would likely be discussed, setting the stage for more advanced techniques covered in later chapters.Chapter 10: Recurrent Neural Networks (RNNs) and LSTMsThis chapter would shift focus to sequential data and how Recurrent Neural Networks (RNNs) are designed to process it. The fundamental concept of how RNNs maintain an internal state (memory) to handle sequences would be explained, along with the challenges associated with training vanilla RNNs, such as the vanishing and exploding gradient problems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.