Mastering Machine Learning PDF Download with Python in Six Steps helps you to become a master in machine learning with Python.
If you know a little bit about Machine Learning then it is very easy for you to understand and you can easily catch it. Either wise, no need to worry beginners can also learn.
In the 6 steps, you get the full details about Mastering Machine Learning with Python in Six steps.
Here, 7 Chapters are there and each chapter has its own topics. In the end, the conclusion is there for a brief description.
Let’s start the discussion about the 7 Chapters of Mastering Machine Learning with Python in Six steps.
Chapter 1- Getting Started in Python Python is a high-level language and very easy to learn and understand, especially for beginners. Here, you get every knowledge about Python in detail. How Python works, their program to build, its features, function, methods, and many more.
Each Chapter has two parts, one part is for theory and another part is for practical. Topics covered in Chapter 1 are- the rising star, version of Python 2.7.x, or Python 3.4.x, the Installation process of Windows, OSX, LINUX, Graphical installer, Command-line installer, etc.
How do you grab all the installation processes also there, How does Python run in the system, Python identifier, keyboard, code blocks, basic data types, comments, use of list, tuples, dictionary, sets, statements, operators, control structure, how to access the elements of sets, set operation, user-defined functions, its syntax, the scope of variables, module, argument, file input/output, how to open the file, exception handling, endnotes, and how to make our first program.
Chapter 2- Introduction to Machine Learning Machine Learning is a subfield of computer science. We study some topics under Machine Learning are- pattern, recognition, AI, etc.
Some of the topics that come inside Chapter 2 are- the history and evolution of Machine Learning, the evolution of AI, AI’s different forms, descriptive analytics, diagnostics analytics, predictive analytics, prescriptive analytics, data science, the difference between statistics, data mining, data science, and data analytics.
Categories of Machine learning- are two types – supervised and unsupervised machine learning, frameworks for building systems of Machine learning, the standard process of cross-industry for data mining, details of SEMMA, Python packages of Machine learning, data analysis packages, data structures, how to read, view, and write data, a summary of basic statistics, basic operations, global functions, object-oriented, graphs, core libraries of machine learning, and endnotes.
Chapter 3- Fundamentals of Machine Learning In this chapter, you get to know about the different algorithms of machine learning of two different Python Packages.
The topics come under are- the machine learning perspective of data, scales of measurement, feature engineering, how to deal with missing data, handling categorical data, normalizing data, feature generation or construction, details about EDA, pair plot, different supervised learning, explanation of errors, VIF, and multicollinearity, over and underfitting, linear models, decision trees, SVM, Key parameters, forecasting of time-series, ARIMA, Unpervised learning flow of the process, elbow method, Hierarchical clustering, key parameters, PCA, and endnotes.
Chapter 4- Model Diagnosis and Tuning For machine learning systems, here, we discuss pitfalls that should be aware of.
Some of the topics covered in this chapter- Optimal Probability Cutoff Points, Rare Event or Imbalanced Datasets, Which Resampling Technique Is the Best, Bias and Variance, K-Fold Cross-Validation, Stratified K-Fold Cross-Validation, Ensemble Methods, Bagging, Extremely Randomized Trees, etc. How Does the Decision Boundary Look, Boosting, Boosting, Xgboost, Ensemble Voting – Machine Learning’s Biggest Heroes United, Hard Voting vs. Soft Voting, Stacking, Hyperparameter Tuning, and Endnotes.
Chapter 5- Text Mining and Recommender Systems It is also known as data text mining, which works to convert unstructured data text into structured text that helps the user to get meaningful patterns and new sights.
The topics covered in this chapter are- Text Mining Process Overview, Data Assemble (Text), social handle, Data Preprocessing (Text), Converting to Lower Case and Tokenizing, Removing Noise, Part of Speech (PoS) Tagging, Stemming, Lemmatization, N-grams, Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Data Exploration (Text), Model Building, Text Similarity, Text Clustering, Latent Semantic Analysis (LSA), Topic Modeling, Text Classification, Sentiment Analysis, Deep Natural Language Processing (DNLP), Word2Vec, Recommender Systems, and Endnotes.
Chapter 6- Deep and Reinforcement Learning It helps you to solve multi-level problems. Deep learning first gets the training set and then after applying it to a new set of data whereas Reinforcement learning is a dynamic learning process by adjusting actions.
Some of the topics that come under this chapter are- Artificial Neural Networks (ANN), What Goes Behind, When Computers Look atan Image, Why Not a Simple Classification Model forImages, Perceptron – Single Artificial Neuron, Multilayer Perceptrons (Feedforward NeuralNetwork), Restricted Boltzmann Machines (RBM), MLP Using Keras, Autoencoders, Convolution Neural Network (CNN), Visualization of Layers, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Transfer Learning, Reinforcement Learning, and Endnotes.
Chapter 7- Conclusion Mastering Machine Learning with Python in six steps is very beneficial for you because here, you get full details about it in just 6 steps. Each Chapter explanation is here with several different topics along with the practical portion. Here, only the chapter name is discussed and the topics that come under inside the chapters are explained, If you want more details about each topic in 6 chapters because chapter 7 is a conclusion part then feel free to visit the pdf form of Mastering Machine learning with Python in Six steps.
I hope you grab more information related to the article that provides benefits in your future.