Machine Learning Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition) (Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain etc.)（Z-Library）

Author： Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain, Ritesh Kumar, Lakhwani, Dr Kamlesh

Concepts of Machine Learning with Practical Approaches. Key Features ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. Description The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. What you will learn ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. Who this book is for This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cas

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【One-Line Pitch】 Concepts of Machine Learning with Practical Approaches. Key Features ● Includes real-scenario examples to expla… 【Book Arc】 - **Opening (~0%–12%)**: Key Features Includes real-scenario examples to explain the working of Machine Learning algorithms.; ython libraries like Scikit-learn, pandas, and matplotlib. - **Early (~12%–35%)**: cture of the data and to match that data to models that can be understood and used by humans.; ne does not cover all the points shown in the graph. - **Middle (~35%–65%)**: however, the working of both algorithms are different.; ween the item sets will be preserved by this tree structure. - **Late (~65%–88%)**: The term refers to what the system recommends to users as a whole.; We define more broad categories of fields for the most common recommended system applications based on these specific application domains. - **Ending (~88%–100%)**: 0 Target attribute class count(Yes/No) {'yes': 2, 'no': 1} Total no of instances/records associated with S-rain is: 3 Experiment No.; h curve through points in a scatter plot. 【Key Takeaways】 - **Key Features Includes real** (Opening): Key Features Includes real-scenario examples to explain the working of Machine Learning algorithms. - **ython libraries like Scikit** (Opening): ython libraries like Scikit-learn, pandas, and matplotlib. - **Did you know that BPB…** (Opening): Did you know that BPB offers eBook versions of every book published, with PDF and ePub files available? - **cture of the data and…** (Early): cture of the data and to match that data to models that can be understood and used by humans. - **ne does not cover all…** (Early): ne does not cover all the points shown in the graph. - **Decision trees are des…** (Early): Decision trees are designed to mimic human decision-making abilities, making them simple to understand. 【Reading Tips】 - Use Passage locations below to jump into the text and set reading anchors - If this is a brief outline, click Regenerate (top right) for a synthesized guide 【Coverage Limits】 Compressed outline without the model (~22 index chunks). Full structured guide needs AI available.

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ython libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. Who this book is for T...

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cture of the data and to match that data to models that can be understood and used by humans. Arthur Samuel stated that Learning gives computers the ability...

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to three types: Binary, Multinomial, and Ordinal. Structure In this chapter, you will learn the following topics: Unsupervised Learning Clustering Hierarchic...

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referred to as supervised machine learning because the way an algorithm’s learning process is done, it is a training data set or in other words, we can say t...

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t the return received after every occurrence of this state. Average overall returns. Set the value of the state as that computed average. Repeat step 3. Repe...

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ological neuron's connections are represented as weights. A positive weight indicates an excitatory connection, while a negative weight indicates an inhibito...

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0 Target attribute class count(Yes/No)= {'yes': 2, 'no': 1} Total no of instances/records associated with S-rain is: 3 Experiment No. 6 AIM: Assuming a set o...

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ane 51 I information criterion (IC) 117 input variable 31 K k-means clustering about 130 algorithm 67 example flowchart 68 K-Nearest Neighbors (K-NN) algorit...

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