8 Best Python Libraries for AI and ML for Beginners

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Machine Learning (ML) and Artificial Intelligence (AI) are spreading across various industries, and most enterprises have started actively investing in these technologies. With the expansion of volume as well as the complexity of data, ML and AI are widely recommended for their analysis and processing. AI offers more accurate insights, and predictions to enhance business efficiency, increase productivity, and lower production costs.

Libraries in programming languages are collections of prewritten code that users can use to optimize their tasks. Python libraries for AI and Machine learning are used by developers to perform complex tasks without the need to rewrite the code.

Python Libraries for AI and ML

In fact, one of the main reasons why the popularity of machine learning is growing tremendously is due to machine learning and deep learning libraries. The reason why Python libraries are preferred for developing sophisticated machine learning and deep learning models is because of their perfect combination of shorter development time, consistent syntax, and flexibility. Here are the most common and popular Python libraries for AI and ML.

1. TensorFlow

TensorFlow is an open-source and free software library mainly used for differential programming. It is a math library that is used by machine learning applications and neural networks. It helps in performing high-end numerical computations. 

While TensorFlow was mainly developed by Google’s Brain team for their internal use, it was released to the public in November 2015, under Apache License 2.0. It can run on a large number of platforms including GPUs, CPUs, and TPUs (Tensor Processing Unit, which is a hardware chip built with TensorFlow).

TensorFlow can handle deep neural networks for image recognition, handwritten digit classification, recurrent neural networks, NLP (Natural Language Processing), word embedding, and PDE (Partial Differential Equation). TensorFlow Python ensures excellent architecture support to allow easy computation deployments across a wide range of platforms, including desktops, servers, and mobile devices.

Abstraction is the major benefit of TensorFlow Python towards machine learning and AI projects. This feature allows the developers to focus on the comprehensive logic of the app instead of dealing with the mundane details of implementing algorithms. With this library, python developers can now effortlessly leverage AI and ML to create unique responsive applications, which respond to user inputs like facial or voice expression.

Following are the features of TensorFlow:

• Open-source Library: It is an open-source library that allows rapid and easier calculations in machine learning. It eases the switching of algorithms from one tool to another TensorFlow tool. With the help of python, it provides the front-end API for the development of various machines and deep learning algorithms.
• Easy to run: We can execute TensorFlow applications on various platforms such as Android, Cloud, IOS and various architectures such as CPUs and GPUs. This allows it to be executed on various embedded platforms. TensorFlow has its own designed hardware to train the neural models known as Cloud TPUs (TensorFlow Processing unit).
• Fast Debugging: It allows you to reflect each node, i.e., operation individually concerning its evaluation. Tensor Board works with the graph to visualize its working using its dashboard. It provides computational graphing methods that support an easy to execute paradigm.
• Effective: It works with multi-dimensional arrays with the help of a data structure tensor which represents the edges in the flow graph. Tensor identifies each structure using three criteria: rank, type, shape.
• Scalable: It provides room for prediction of stocks, products, etc with the help of training using the same models and different data sets. It also allows for synchronous and asynchronous learning techniques and data ingestion. The graphical approach secures the distributed execution parallelism.
• Easy Experimentation: TensorFlow transforms the raw data to the estimators-a form of data neural networks understand. TensorFlow feature columns allow the bridge between raw data and estimators to train the model. This adds the agility to the model for fast developmental insights.
• Abstraction: TensorFlow provides a defined level of abstraction by reducing the code length and cutting the development time. The user needs to focus on logic disregarding the proper way of providing input to functions. A user can choose the model according to the system’s requirements.
• Flexibility: TensorFlow provides the process of resolving complex topologies with the support of Keras API and data input pipelines. Keras provides easy prototyping and suits best for object-oriented neural networks. TensorFlow eases the mechanism of machine learning with the assistance of such characteristics. It allows the user to create and manipulate the system to create different types of real-time models.

2. Keras

Keras is the most useful library for Deep Learning. It runs on top of TensorFlow and Theano. It focuses on fast experimentation and is a neural network library. Keras uses TensorFlow and Theano as its backend. Keras is a leading open-source Python library written for constructing neural networks and machine learning projects. It can run on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK), Theano, or TensorFlow. It offers almost all standalone modules including optimizers, neural layers, activation functions, initialization schemes, cost functions, and regularization schemes. It makes it easy to add new modules just like adding new functions and classes. As the model is already defined in the code, you don’t need to have a separate model config file.

Keras makes it simple for machine learning beginners to design and develop a neural network. Keras Python also deals with convolution neural networks. It includes algorithms for normalization, optimizer, and activation layers. Instead of being an end-to-end Python machine learning library, Keras functions as a user-friendly, extensible interface that enhances modularity & total expressiveness.

Following are the salient features of Keras:

• Modularity: Keras is modular. It considers a model in the form of a graph or a sequence. Keras allows you to save the model you are working on. Keras provides a save() method to save the current model. You can even use the model in the future.
• Large Dataset: Keras contains a large predefined dataset. It provides you with a variety of datasets. You can use this dataset to directly import and load it. Let’s say, IMDB DATA. It contains around 25k reviews of the movies. This dataset contains binary numbers (0 & 1) to review each movie. 0 represents negative sentiment and 1 represents positive sentiment.
• Train from NumPy Data: Keras uses the NumPy array to train and evaluate the model. It makes use of the fit() method. The fit() method fits the model to the training data. This training process may take some time. fit() method had three arguments batch_size, validation_data and epochs.
• Evaluation and Prediction: Keras has evaluate() and predict() methods. These methods can use the dataset of NumPy. After testing the data, the evaluation of the result is done. These methods are used to evaluate our models.
• Pre-trained Models in Keras: Keras contains a number of pre-trained models. These models can be imported from keras.applications. These models are useful for feature extraction and fine-tuning. Keras.application is a module that contains weights for image classification like VGG16, VGG19, Xception, etc.
• Encoding in Keras: Karas allows you encoding features. There is one_hot() function in Keras that enables encoding. It helps you to encode integers in one step. It also enables you to tokenize the data. This function filters out the white spaces, makes the text to lowercase, and filters out the punctuations.
• Layers in Keras: There are numerous layers and parameters in Keras. All Keras layers have a number of methods in them. These layers are useful to construct, train, configure the data. The dense layer is beneficial to implement operations. Flatten is used to flatten the input. Dropout allows dropout to the input. Reshape helps in reshaping the output in a certain shape. Input is used to initiate a Keras tensor.
• You can Obtain the Output of an Intermediate Layer: Keras is a very easy library. It enables you to obtain the output in the intermediate of a layer. To obtain output in the intermediate, you can simply create a new layer that will help you to obtain the output. Or else, you can build a Keras function that will help you to return the output of a certain layer using a certain input. Hence, Keras makes it easy for you to work with it.
• Keras is Python-Native Library: Keras is a complete Python library. It uses all the known concepts of Python. It is a library that is written in the Python language. As Keras is Python oriented, it provides you a user-friendly environment. You can implement Keras knowing the basics of Python. So, it is very simple to work with Keras.
• Pre-processing of Data: Keras provides you several functions for the preprocessing of data. ImageDataGenerator is one such method. It helps you to resize the image, change its degree, flip the image, change the height and width of the image, etc.

3. Theano

Theano is a Python library that is majorly used for fast numerical computation and it can run on both GPU and CPU. Since it is built on top of NumPy, Theano is pretty tightly integrated with NumPy and it has a similar interface as well. The library is perfect for manipulating and evaluating mathematical expressions as well as matrix calculations. With Theano, you can perform data-intensive computations that are up to 140 times faster. It also has built-in tools for validation and unit testing, making it easier to avoid any problems or bugs. 

At the core, it is a well-known scientific computing library that allows you to define, optimize as well as evaluate mathematical expressions, which deals with multidimensional arrays. The fundamental of several ML and AI applications is the repetitive computation of a tricky mathematical expression. Theano allows you to make data-intensive calculations up to a hundred times faster than when executing on your CPU alone. Additionally, it is well optimized for GPUs, which offer effective symbolic differentiation and includes extensive code-testing capabilities.

When it comes to performance, Theano is a great Python machine learning library as it includes the ability to deal with computations in large neural networks. It aims to boost the development time and execution time of ML apps, particularly in deep learning algorithms. Only one drawback of Theano in front of TensorFlow is that its syntax is quite hard for beginners.

Following are the salient features of Theano:

• Automatic Differentiation: You only have to implement the forward (prediction) part of the model, and Theano will automatically figure out how to calculate the gradients at various points, allowing you to perform gradient descent for model training.
• Transparent Use of a GPU: You can write the same code and run it either on CPU or GPU. More specifically, Theano will figure out which parts of the computation should be moved to the GPU.
• Speed and Stability Optimisations: Theano will internally reorganise and optimise your computations, in order to make them run faster and be more numerically stable. It will also try to compile some operations into C code, in order to speed up the computation.

4. PyTorch

PyTorch is a deep learning library that is used by applications like natural language processing and computer vision. Developed by Facebook, it is open-source, free, and released under the modified BSD license. The Python library for AI and ML is based on the Torch library, and that’s how it gets its name. PyTorch can easily be integrated with other Python data science stacks and it also helps developers in performing computations on tensors. 

PyTorch is a production-ready Python machine-learning library with excellent examples, applications, and use cases supported by a strong community. This library absorbs strong GPU acceleration and enables you to apply it from applications like NLP. As it supports GPU and CPU computations, it provides you with performance optimization and scalable distributed training in research as well as production. Deep neural networks and Tensor computation with GPU acceleration are the two high-end features of PyTorch. It includes a machine learning compiler called Glow that boosts the performance of deep learning frameworks.

The robust and seamless framework of PyTorch can create accurate computational graphs which can be changed even during runtime. The library also offers support for simplified preprocessors, numerous GPUs, and custom data loaders. 

Following the features of PyTorch:

• PyTorch is Fast: PyTorch offers fast, supple experimentation in addition to well-organized production through a cross front-end, dispersed training and system of tools besides libraries. Most developed python libraries have the scope of changing the field of deep learning.
• It is Open Source: PyTorch is a brainchild of Facebook’s artificial intelligence research group. It is an open source package for python, which entitles neural network exchange with a primary focus on deep machine learning. Before digging very deep into the method of programming, let me give clarity about the special features of Pytorch. Here we go
• Flexibility: PyTorch is a machine library, planned for merging in python code. It uses the math processing unit at the maximum possible extent, along with the graphical processing unit.
• Optimum Use of Memory: With the optimum utilization of memory built in, the Pytorch works with minimum resources possible. Being a neural network program it has an advantage over many machine learning programs. The researchers have made fine adjustments to the neural network system to make it easier to use. Pytorch supports different types of Tensors which are similar to the Numpy arrays with the main focus on Graphical processing units.
• It Offers Platform for Deep Learning: PyTorch is a python based library developed to offer suppleness as a development platform for deep learning. Additional prevalent deep learning frameworks toil on graphs where computational diagrams have to be constructed in advance.
• Dynamic Graphs: Dynamic graphs provided clearness for data scientists and developers.  PyTorch provides an easier approach than TensorFlow. PyTorch comes with many useful features. One of such features is using this feature you can easily perform the binding of any module.
• Simple and Precise: PyTorch is precise and simple for use and offers you an opportunity to deploy computational graphs whenever you want.

5. SciKit-Learn

Scikit-learn is another prominent open-source Python machine learning library with a broad range of clustering, regression, and classification algorithms. DBSCAN, gradient boosting, random forests, vector machines, and k-means are a few examples. 

It features a wide range of unsupervised and supervised learning algorithms. It is built on two of the basic Python libraries — SciPy and NumPy. It has numerous classification, clustering, and regression algorithms available in it like random forests, k-means, and gradient boosting. The library can also help with dimensionality reduction, preprocessing, and model selection. Developers mainly deploy the Scikit-learn library for data mining and analysis.

It is a commercially usable artificial intelligence library. This Python library supports both supervised as well as unsupervised ML. Here is a list of the premier benefits of Scikit-learn Python that makes it one among the most preferable Python libraries for machine learning:

• Clustering: for grouping unlabeled data such as KMeans.
• Cross Validation: for estimating the performance of supervised models on unseen data.
• Datasets: for test datasets and for generating datasets with specific properties for investigating model behavior.
• Dimensionality Reduction: for reducing the number of attributes in data for summarization, visualization and feature selection such as Principal component analysis.
• Ensemble methods: for combining the predictions of multiple supervised models.
• Feature extraction: for defining attributes in image and text data.
• Feature selection: for identifying meaningful attributes from which to create supervised models.
• Parameter Tuning: for getting the most out of supervised models.
• Manifold Learning: For summarizing and depicting complex multi-dimensional data.
• Supervised Models: a vast array not limited to generalized linear models, discriminant analysis, naive bayes, lazy methods, neural networks, support vector machines and decision trees.

6. Pandas

In machine learning projects, a substantial amount of time is spent on preparing the data as well as analyzing basic trends & patterns. This is where the Python Pandas receive machine learning experts’ attention. Python Pandas is an open-source library that offers a wide range of tools for data manipulation & analysis. With this library, you can read data from a broad range of sources like CSV, SQL databases, JSON files, and Excel.

It enables you to manage complex data operations with just one or two commands. Python Pandas comes with several inbuilt methods for combining data, and grouping & filtering time-series functionality. Overall, Pandas is not just limited to handling data-related tasks; it serves as the best starting point to create more focused and powerful data tools.

Following are the salient features of Pandas:

• Handling of Data: The Pandas library provides a really fast and efficient way to manage and explore data. It does that by providing us with Series and DataFrames, which help us not only to represent data efficiently but also manipulate it in various ways. These features of Pandas is exactly what makes it such an attractive library for data scientists.
• Alignment and Indexing: Having data is useless if you don’t know where it belongs and what it tells us about. Therefore, labeling of data is of utmost importance. Another important factor is an organization, without which data would be impossible to read. These two needs: Organization and labeling of data are perfectly taken care of by the intelligent methods of alignment and indexing, which can be found within Pandas.
• Handling Missing Data: As discussed above, data can be quite confusing to read. But that is not even one of the major problems. Data is very crude in nature and one of the many problems associated with data is the occurrence of missing data or value. Therefore, it is pertinent to handle the missing values properly so that they do not adulterate our study results. Some Pandas features have you covered on this end because handling missing values is integrated within the library.
• Cleaning Up Data: Like we just said, Data can be very crude. Therefore it is really messy, so much so that performing any analysis over such data would lead to severely wrong results. Thus it is of extreme importance that we clean our data up, and this Pandas feature is easily provided. They help a lot to not only make the code clean but also tidies up the data so that even the normal eye can decipher parts of the data. The cleaner the data, the better the result.
• Input and Output Tools: Pandas provide a wide array of built-in tools for the purpose of reading and writing data. While analyzing you will obviously need to read and write data into data structures, web service, databases, etc. This has been made extremely simple with the help of Pandas’ inbuilt tools. In other languages, it would probably take a lot of code to generate the same results, which would only slow down the process of analyzing.
• Multiple File Formats Supported: Data these days can be found in so many different file formats, that it becomes crucial that libraries used for data analysis can read various file formats. Pandas aces this sector with a huge scope of file formats supported. Whether it is a JSON or CSV, Pandas can support it all, including Excel and HDF5. This can be considered as one of the most appealing Python Pandas features.
• Merging and Joining of Datasets: While analyzing data we constantly need to merge and join multiple datasets to create a final dataset to be able to properly analyze it. This is important because if the datasets aren’t merged or joined properly, then it is going to affect the results adversely and we do not want that. Pandas can help to merge various datasets, with extreme efficiency so that we don’t face any problems while analyzing the data.
• A Lot of Time Series: These Pandas features won’t make sense to beginners right away, but they will be of great use in the future. These features include the likes of moving window statistics and frequency conversion. So, as we go deeper into learning Pandas we will see how essential and useful these features are, for a data scientist.
• Optimized Performance: Pandas is said to have a really optimized performance, which makes it really fast and suitable for data science. The critical code for Pandas is written in C or Cython, which makes it extremely responsive and fast.
• Python Support: This feature of Pandas is the deal closer. With an insane amount of helpful libraries at your disposal Python has become one of the most sought after programming languages for data analysis. Thus Pandas being a part of Python and allowing us to access the other libraries like NumPy and MatPlotLib.
• Visualize: Visualizing the data is an important part of data science. It is what makes the results of the study understandable by human eyes. Pandas have an in-built ability to help you plot your data and see the various kinds of graphs formed. Without visualization, data analysis would make no sense to most of the population.
• Grouping: Having the ability to separate your data and grouping it according to the criteria you want, is pretty essential. With the help of the features of Pandas like GroupBy, you can split data into categories of your choice, according to the criteria you set. The GroupBy function splits the data, implements a function and then combines the results.
• Mask Data: Sometimes, certain data is not needed for analysis of data and thus it is important that you filter your data according to the things you want from it. Using the mask function in Pandas allows you exactly to do that. It is extremely useful since whenever it finds data which meets the criteria you set for elimination, it turns the data into a missing value.
• Unique Data: Data always has a lot of repetition, therefore it is important that you are able to analyze data which has only unique values. This is present in the Python Pandas features and lets the user see the unique values in the dataset with the function dataset.column.unique(). Where “dataset” and “column” are the names of your dataset and column, respectively.
• Perform Mathematical Operations on the Data: The apply function in Pandas allows you to implement a mathematical operation on the data. This helps enormously, because sometimes the dataset you have, is just not of the correct order. This will be correct by simply using a mathematical operation on the dataset. This is one of the most attractive features of Pandas.

7. Numpy

NumPy or Numerical Python is linear algebra developed in Python. Almost all Python machine-learning packages like Mat-plotlib, SciPy, Scikit-learn, etc rely on this library to a reasonable extent. It comes with functions for dealing with complex mathematical operations like linear algebra, Fourier transformation, random numbers, and features that work with matrices and n-arrays in Python. The NumPy Python package also performs scientific computations. It is widely used in handling sound waves, images, and other binary functions.

Following are characteristics features of NumPy:

• High-Performance N-Dimensional Array Object: This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.
  • One Dimensional Array: The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.
  • Multidimensional Array: In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.
• It Contains Tools for Integrating Code from C/C++ and Fortran: We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.
• It Contains a Multidimensional Container for Generic Data: Here generic data refers to the parameterized data type of arrays. It can perform functions on the generic data types. The arrays in NumPy are of homogenous nature. These array elements are assigned parameters. The parameters help increase the diversity of the arrays.
• Additional Linear Algebra, Fourier Transform, and Random Number Capabilities: It has the capability to perform complex operations of the elements like linear algebra, Fourier transform, etc. We have separate modules for each of the complex functions. We have the linalg module for linear algebra functions. Similarly, we have fft functions for Fourier Transform in NumPy. We have a matrix module for applying functions on matrices. We also have special functions for plotting graphs in the matplotlib module of NumPy. Hence, it is a very diverse library to work with arrays.
• It Consists of Broadcasting Functions: The broadcasting of arrays is a very useful concept when we work with arrays of uneven shapes. It broadcasts the shape of smaller arrays according to the larger ones. The broadcasting of arrays has some rules and limitations in its implementation. For broadcasting one of the arrays needs to be one dimensional or both the arrays are supposed to be of the same shape. There are also a few other limitations on the shape of the arrays.
• It Had Data Type Definition Capability to Work With Varied Databases: We can work with arrays of different data types. We can use the dtype function to determine the data type and hence get a clear idea about the available data set. With the array definition, we have an additional dtype argument to perform array functions. The knowledge of the data type of array is very important due to the restrictions on NumPy operations.

8. Seaborn

Finally, the last library in the list of Python libraries for machine learning and AI is Seaborn – and unparalleled visualization library, based on Matplotlib’s foundations. Both storytelling and data visualization is important for machine learning projects, as they often require exploratory analysis of datasets to decide on the type of machine learning algorithm to apply. Seaborn offers a high-level dataset-based interface to make amazing statistical graphics.

With this Python machine learning library, it is simple to create certain types of plots like time series, heat maps, and violin plots. The functionalities of Seaborn go beyond Python Pandas and matplotlib with the features to perform statistical estimation at the time of combining data across observations, plotting, and visualizing the suitability of statistical models to strengthen dataset patterns.

Seaborn is built on top of Python’s core visualization library Matplotlib. It is meant to serve as a compliment and not a replacement. However, Seaborn comes with some very important features. Let us see a few of them here. The features help in:

• Built in themes for styling matplotlib graphics
• Visualizing univariate and bivariate data
• Fitting in and visualizing linear regression models
• Plotting statistical time series data
• Seaborn works well with NumPy and Pandas data structures
• It comes with built in themes for styling Matplotlib graphics

These libraries are extremely valuable when you’re working on machine learning projects as it saves time and further provides explicit functions that one can build on. Among the outstanding collection of Python libraries for machine learning, these are the best libraries, which are worth considering. With the help of these Python machine learning libraries, you can introduce high-level analytical functions, even with minimal knowledge of the underlying algorithms you are working with.