Machine Learning with Python for Beginners A Step-by-Step Guide with Hands-On Projects Book PDF

 Machine Learning with Python for Beginners A Step-by-Step Guide with Hands-On Projects Book PDF 2022

The 3 essential steps of machine learning:

Machine Learning is used in artificial intelligence and Analytics and Data Science. 

There are different types of machine learning: supervised, non-supervised and reinforcement. 

Supervised learning: for this learning, we have input data (Features) and the expected result (Label). 

It allows us to make predictions based on a model* that is obtained from historical data and the chosen algorithm. 

Supervised learning attempts to answer two questions: Classification: "what class?"; Regression: "how much?". 

Non-supervised learning: with this learning, you always have features, but no label, because we don’t try to predict anything. 

Based on the historical data we have, we try to see what we can learn from the data, not to mention validate the conclusions obtained with experts in the field. This type of machine learning is typically used to discover structures and models in the data. 

It can also be used for Feature Engineering during the data preparation process for supervised learning (more on this later). 

 Reinforcement learning: With this type of learning, you start with an agent (algorithm) who must choose from a list of actions.

Then, depending on the action chosen, it will receive a return from the environment (from a human in certain situations or from another algorithm):

 it is either a reward for a good choice, or a penalty for a bad action. 

The agent (algorithm) learns which strategy (or stock choice) maximizes the accumulation of rewards. Decomposition of data: Text columns sometimes contain more than one information, so we need to divide them into as many dedicated columns as necessary.

 If some columns represent categories, convert them to category type columns.

Data Aggregation: Group certain information together when relevant Scaling:

This will provide data on a common scale, if not already. It is necessary when there is a large variation in the feature ranges. Formatting and Transformation (Data Shaping & Transformation): from categorical to digital. Data enrichment: Sometimes you will need to enrich existing data with external data in order to give the algorithm more information to work with, which improves the model (for example, economic or meteorological data). Feature Engineering: View your data as a whole to see if there are links between the columns. Using graphs (charts), you can see features/features side by side and detect any link between features, and between features and labels. Sometimes you need to generate additional features from those that already exist in a classification (for example, when the chosen algorithm is unable to correctly differentiate classes). The choice of algorithm: At this stage, you can start training the algorithms, but above all: Divide your data set into three parts: training, testing and validation. The training data will be used to train the chosen algorithm(s); The test data will be used to verify the performance of the result; Validation data will only be used at the very end of the process and will not will, unless necessary, be very rarely examined and used before to avoid introducing any bias in the result. Choose the relevant algorithm(s). Try algorithms with different combinations of parameters and compare the performance of their results. As soon as you are reasonably satisfied with a model, save it even if it is not perfect, because you may never find the combination that enabled you to obtain it again. Continue testing after each new model. If the results are not satisfactory, you can start again: At the hyperparameter stage, if you’re lucky. When embarking on a machine learning process, it is important to keep in mind that our data may not allow us to achieve a better result than what we are already achieving with our traditional methods already in place. But it certainly gives us some interesting information in the data analysis process.

How to learn Python for machine learning:

How to learn Python: There are many ways to learn a language, too for natural languages like English, or a programming language like Python. Babies learn a language by listening and miming. Slowly, when they have learned the pattern and some vocabulary, they can compose their own sentences. On the contrary, when students learn Latin, they probably start with grammar rules. Guidance and self-direction, nominal and accusatory, as well as singular and collection. Then we can build a Latin sentence. Similarly, when you learn Python or any other programming language, you can either read other people’s code and try to understand it, and then change it. Or you can learn the rules of language and create a program from scratch. The latter would be beneficial if your ultimate goal is to work on the language, for example by writing the Python interpreter. But generally, the first approach is faster to get results. Ask for help: When you start from an example you saw in a book and you change it, you can break the code and prevent it from being executed. This is especially true in machine learning examples, where you have many lines of code covering data collection, preprocessing, model building, training, validation, the prediction and finally the presentation of the result in a visualized way. When you see an error in your code, the first thing you need to do is spot the few lines that caused the error. Try to check the output of each step to make sure it is in the correct format. Or try to restore your code to see what change you made started to introduce errors. It is important to make mistakes and learn from mistakes. When you try syntax and learn your way, you should encounter error messages from time to time. Try to make sense of it, it will then be easier for you to understand the cause of the error. Almost always, if the error is from a library you are using, confirm your syntax with the library documentation. If you are still confused, try searching the Internet. If you use Google, one tip you can use is to put the entire error message in double quotes when you search for it. Or sometimes, searching on StackOverflow can give you better answers.

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