{"id":23548,"date":"2018-08-03T04:00:44","date_gmt":"2018-08-03T04:00:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=23548"},"modified":"2026-04-27T17:27:32","modified_gmt":"2026-04-27T11:57:32","slug":"python-machine-learning-tutorial","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-machine-learning-tutorial\/","title":{"rendered":"Python Machine Learning Tutorial &#8211; Tasks and Applications"},"content":{"rendered":"<p><span style=\"font-weight: 400\">In this Python <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>Machine Learning Tutorial<\/strong><\/a>, we will introduce you to machine learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10 tech giants that adapt <a href=\"https:\/\/data-flair.training\/blogs\/python-tutorial-for-beginners\/\"><strong>Python<\/strong><\/a> Machine Learning to improve what they do.<\/span><\/p>\n<p>So, let&#8217;s start the<em> Python Machine Learning Tutorial<\/em>.<\/p>\n<div id=\"attachment_23561\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23561\" class=\"wp-image-23561 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01.jpg\" alt=\"Python Machine Learning\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Python-Machine-Learning-Tutorial-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23561\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Tasks and Applications<\/p><\/div>\n<h3><strong>Introduction to Machine Learning With Python<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">In this Python Machine Learning Tutorial, Machine Learning is also termed ML. It is a subset of <a href=\"https:\/\/data-flair.training\/blogs\/artificial-intelligence-introduction\/\"><strong>AI<\/strong> <strong>(Artificial Intelligence)<\/strong><\/a> and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/python-statistics\/\">Do you know about statistics in Python<\/a><\/strong><\/p>\n<p><strong>Benefits of using machine learning with Python:<\/strong><\/p>\n<ul>\n<li><strong>Ready to use tools:<\/strong> Instead of writing everything from scratch, you can use libraries like SciKit &#8211; learn and pandas to handle data and create models immediately.<\/li>\n<li><strong>Fast results:<\/strong> You can write or test data in a faster way, which is why experts use it for creating prototypes.<\/li>\n<li><strong>Big help network:<\/strong> As everyone uses AI in Python, it&#8217;s now easier to find solutions for every problem online.<\/li>\n<\/ul>\n<h3><strong>Tasks in Machine Learning Using Python<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">With Python Machine Learning, we divide the tasks of <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-algorithm\/\">Machine Learning Algorithms <\/a><\/strong>in Python into two broad categories- Supervised and Unsupervised.<\/span><\/p>\n<div id=\"attachment_23580\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23580\" class=\"wp-image-23580 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01.jpg\" alt=\"Python Machine Learning Tutorial\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Tasks-in-Machine-Learning-Using-Python-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23580\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Tasks\u00a0of Machine learning<\/p><\/div>\n<h4><strong>a. Supervised Learning<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Here, a learning signal\/ feedback is available to the system; we give it to sample data to learn from. The computer holds example inputs and desired outputs with the goal of learning a general rule that maps inputs to outputs. One such example of Python Machine Learning will be to search for images on Facebook using keywords centered around the contents of the image. Under Supervised Learning, we have the following kinds of Python machine Learning-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Semi-Supervised Learning-<\/strong> The computer receives an incomplete training signal. This is a training set with some target outputs missing.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Active Learning-<\/strong>\u00a0The computer can secure training labels for only some instances. It also needs to make an optimal choice of objects to secure labels.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Reinforcement Learning-<\/strong>\u00a0In this, the training data comes as feedback on how a program acts in a dynamic environment. Examples of this include driving a vehicle or playing against an opponent.<\/span><\/li>\n<\/ul>\n<p>Many supervised learning techniques utilize cross-validation procedures that allow one to estimate or check their models\u2019 validity. Cross-validation is a method of splitting the data in the given assessment and training portions for a model into variant partitions. This process assists in the adjustment of model parameters and the prevention of overfitting.<\/p>\n<p>Further, techniques like grid search or random search in feature selection occur as hyperparameter tuning for refining the model\u2019s performance.<\/p>\n<p><strong>Steps involved in Supervised Machine Learning-<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Training<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">testing<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Among many Supervised Machine Learning Algorithms for beginners, we observe that here we list some-<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-applications\/\">Let&#8217;s discuss Machine Learning Applications<\/a><\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Decision trees<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Support Vector Machines<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Na\u00efve Bayes<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">k-nearest neighbor<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Linear regression<\/span><\/li>\n<\/ul>\n<h4><strong>b. Unsupervised Learning<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">In unsupervised learning, the Python Machine Learning Algorithm receives no labels; we only give the machine a set of inputs. It must rely on itself to find structure in its input. This kind of learning can be a goal or a means toward future learning. We can classify unsupervised learning as-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Clustering-<\/strong> The act of grouping data inherently. One example of this will be to group consumers by their shopping habits so they can target the right consumers to advertise.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Association-<\/strong> In association, we identify rules explaining large sets of our data. One example will be to associate books with the author\/ category.<\/span><\/li>\n<\/ul>\n<p>Unsupervised learning often implies the use of processes that help to reduce the dimensions of data as well as the needed features. Techniques such as PCA and t-SNE are used to map and analyze data in the higher-dimensional space. These approaches are useful when dealing with underlying patterns and limitations of computational processes.<\/p>\n<p><span style=\"font-weight: 400\">Of the many Unsupervised Machine Learning Algorithms, we observe here are a couple-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">K-means clustering<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Hierarchical clustering<\/span><\/li>\n<\/ul>\n<h3><strong>Steps in Python Machine Learning\u00a0<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">We follow the following steps in Machine Learning Using Python-<\/span><\/p>\n<p>1. Collecting data.<\/p>\n<p>2. Filtering data.<\/p>\n<p>3. Analyzing data.<\/p>\n<p>4. Training algorithms.<\/p>\n<p>5. Testing algorithms.<\/p>\n<p>6. Using algorithms for future predictions.<\/p>\n<h3><strong>Applications of Python Machine Learning<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Let&#8217;s learn Applications of Machine Learning with Python:<\/span><\/p>\n<div id=\"attachment_23578\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23578\" class=\"wp-image-23578 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01.jpg\" alt=\"Python Machine Learning Tutorial \" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Applications-of-Machine-Learning-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23578\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Applications of Machine learning<\/p><\/div>\n<h4><strong>a. Fighting and filtering webspam and malware<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">With rule-based spam filtering, the latest tricks by spammers can go unnoticed. e-mail clients make use of machine learning to ensure their spam filters stay updated. Other than that, imagine getting to Google and searching for something only to find irrelevant listings right at the top.<\/span><\/p>\n<p><span style=\"font-weight: 400\"> To fight these situations, Google uses \u2018deep learning\u2019, a neural network that takes data from users and from NLP, and determines the nature of the email in question. Some spam-filtering techniques under ML are Multi-Layer Perceptron and C 4.5 Decision Tree Induction.<\/span><br \/>\n<a href=\"https:\/\/data-flair.training\/blogs\/python-machine-learning-techniques\/\"><strong>Let&#8217;s have a look at Python ML Techniques<\/strong><\/a><\/p>\n<h4><strong>b. Refining search-engine results<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Suppose you went up to Google and typed in the keywords \u201cDIY lampshade\u201d. If you visit one or more of the top listings and stay for a while, Google assumes it did a good job serving your request. If, however, you end up on the third page and have not visited any result, Google knows it could have done better. So, it improves search results next time.<\/span><\/p>\n<h4><strong>c. Virtual Personal Assistants<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">With assistants like Siri, Alexa, and Google Now, the term virtual personal assistant needs no explanation. This helps find information for you, make calls, set alarms, and check the weather, among other things they can do. And to make this easy for you, all they need you to do is use your voice and command them to do it for you. <\/span><\/p>\n<p><span style=\"font-weight: 400\">When you\u2019ve got your hands filthy, or if you\u2019ve just woken up and do not wish to lay your eyes on the light of a screen, this comes in handy. Not to forget the huge importance of this for those handicapped.<\/span><\/p>\n<p><span style=\"font-weight: 400\">How you are involved with them helps them collect and refine that information. This is machine learning, and this is how they generate better results next time.<\/span><\/p>\n<h4><strong>d. Social Media Services<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">On social media, features like \u2018People You May Know\u2019 and \u2018Face Recognition\u2019 work via machine learning. Considering your activity, like the profiles you visit, the people you befriend, and the people you tag, Facebook curates a list of suggestions for you to enrich your experience and make you stay.<\/span><\/p>\n<h4><strong>e. Online customer support<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Some websites will pop up a live chat option to make your stay, in case you need a query to be answered. For some, it isn\u2019t live but is a chatbot. Such a bot pulls information from the website and delivers it to the customer. The machine learning algorithms make it possible to improve this experience.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/train-test-set-in-python-ml\/\">Let&#8217;s discuss Train and Test Set in Python ML<\/a><\/strong><\/p>\n<h4><strong>f. Product recommendations<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Shopping giants like Jabong and Amazon curate a list of products similar to the ones you\u2019re visiting. They also mail you shopping suggestions. This is machine learning behind the scenes; it pays attention to your past purchases, wishlist, cart contents, brand preferences, and so on.<\/span><\/p>\n<h4><strong>g. Online fraud detection<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Companies like PayPal use ML to fight against issues like money laundering. They compare millions of transactions to differentiate between those legitimate and illegitimate.<\/span><\/p>\n<h4><strong>h. Video Surveillance<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">With ML, video surveillance systems can detect a possible crime ahead of time. Risky behavior, like people standing motionless for a while monitoring a situation, napping on a bench, and following another individual, can alert human attendants. When this can prevent a mishap and save a life, incidents like these help improve such surveillance services.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/future-of-machine-learning\/\">Let&#8217;s know why we should learn Machine Learning<\/a><\/strong><\/p>\n<h4><strong>i. Automatic Translation<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">ML makes it possible to translate text from one language to another. The algorithm learns how words fit together and uses that to improve the translation. It is also possible to text on images. This is done with neural networks to identify letters in the images. It translates the text and then puts it back onto the picture.<\/span><\/p>\n<h3><strong>Companies Using Python Machine Learning<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Of many others, the following 10 companies make use of machine learning tools and technologies to grow and improve their functions.<\/span><\/p>\n<div id=\"attachment_23563\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23563\" class=\"wp-image-23563 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01.jpg\" alt=\"Python Machine Learning Tutorial\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/Companies-Using-Python-Machine-Learning-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23563\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Companies<\/p><\/div>\n<h4><strong>a. Apple<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Apple was the first to ship a voice assistant on a smartphone. And with HomePod, it aspires to take this a step further.<\/span><\/p>\n<div id=\"attachment_23612\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/apple-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23612\" class=\"wp-image-23612 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/apple-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"467\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/apple-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/apple-1-150x140.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/apple-1-300x280.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23612\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Apple<\/p><\/div>\n<p><span style=\"font-weight: 400\">With the rising competition, it is the technology and the end user that benefits. Apple paid $200 to purchase Lattice Data, which can convert unstructured data into a structured form using ML. It also develops in-house machine learning systems.<\/span><\/p>\n<h4><strong>b. Google<\/strong><\/h4>\n<div id=\"attachment_23613\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/google-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23613\" class=\"wp-image-23613 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/google-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/google-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/google-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/google-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23613\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211; Google<\/p><\/div>\n<p><span style=\"font-weight: 400\">Google offers multiple cloud-based services to developers. One of these is the Google Cloud AI machine learning tools. Recently, Google launched an AI chatbot that will answer messages for you. This is like a sophisticated auto-response email.<\/span><\/p>\n<h4><strong>c. Microsoft<\/strong><\/h4>\n<div id=\"attachment_23614\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/microsoft-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23614\" class=\"wp-image-23614 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/microsoft-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/microsoft-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/microsoft-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/microsoft-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23614\" class=\"wp-caption-text\">Python Machine Learning Tutorial &#8211;\u00a0Microsoft<\/p><\/div>\n<p><span style=\"font-weight: 400\">Microsoft purchased LinkedIn a few years ago at $26 billion and has lately been the third-biggest spender on acquisitions. Maluuba, a Canadian tech company that houses a very impressive deep learning research lab for Natural Language Understanding.<\/span><\/p>\n<h4><strong>d. Twitter<\/strong><\/h4>\n<div id=\"attachment_23615\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/twitter-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23615\" class=\"wp-image-23615 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/twitter-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/twitter-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/twitter-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/twitter-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23615\" class=\"wp-caption-text\">Python\u00a0Machine Learning Tutorial &#8211; Twitter<\/p><\/div>\n<p><span style=\"font-weight: 400\">Ever since Facebook changed its algorithm to favor posts from friends and family over news articles from reputed sources, Twitter\u2019s profitability has risen. Here, machine learning makes it possible to find out what people might be interested in and curate content for them.<\/span><\/p>\n<h4><strong>e. Intel<\/strong><\/h4>\n<div id=\"attachment_23617\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/intel-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23617\" class=\"wp-image-23617 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/intel-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/intel-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/intel-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/intel-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23617\" class=\"wp-caption-text\">Python\u00a0Machine Learning Tutorial &#8211; Intel<\/p><\/div>\n<p><span style=\"font-weight: 400\">Intel is the largest chipmaker in the world. In the last few years, it acquired Nervana Systems (manufacturer of chips for data center servers) at a cost of $400 million. Nervana chips can transfer data at around 2.4 terabytes per second at a low latency.<\/span><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/advantages-and-disadvantages-of-machine-learning\/\">Have a look at the advantages &amp; disadvantages of Machine learning<\/a><\/strong><\/p>\n<h4><strong>f. Baidu<\/strong><\/h4>\n<div id=\"attachment_23620\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/baidu-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23620\" class=\"wp-image-23620 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/baidu-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/baidu-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/baidu-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/baidu-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><p id=\"caption-attachment-23620\" class=\"wp-caption-text\">Python\u00a0Machine Learning Tutorial &#8211; Baidu<\/p><\/div>\n<p><span style=\"font-weight: 400\">Baidu is a Chinese search giant and takes a keen interest in Natural Language Processing. It also aims to develop a functioning voice-activated search facility. Recently, it acquired Kitt.ai, which has a portfolio of chatbots and voice-based applications. Very easily, Baidu is the 10th largest spender on acquisitions.<\/span><\/p>\n<h4><strong>g. IBM<\/strong><\/h4>\n<div id=\"attachment_23621\" style=\"width: 4474px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23621\" class=\"wp-image-23621 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1.png\" alt=\"Python Machine Learning\" width=\"4464\" height=\"1944\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1.png 4464w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1-150x65.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1-300x131.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1-768x334.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/ibm-1-1024x446.png 1024w\" sizes=\"auto, (max-width: 4464px) 100vw, 4464px\" \/><\/a><p id=\"caption-attachment-23621\" class=\"wp-caption-text\">Python\u00a0Machine Learning Tutorial &#8211; IBM<\/p><\/div>\n<p><span style=\"font-weight: 400\">Back in the 1990s, IBM challenged Garry Kasparov, Russia\u2019s greatest chess player, to a match against Deep Blue, a computer by IBM. Kasparov won the first match and flunked the next few. Later, computer Watson AI beat contestants on the quiz show <\/span><i><span style=\"font-weight: 400\">Jeopardy!<\/span><\/i><span style=\"font-weight: 400\">. More recently, the machine won the ancient board game \u2018<\/span><i><span style=\"font-weight: 400\">Go<\/span><\/i><span style=\"font-weight: 400\">\u2019 in a recent human-vs-machine contest.<\/span><\/p>\n<h4><strong>h. Salesforce<\/strong><\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/salesforce-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-23623 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/salesforce-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/salesforce-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/salesforce-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/salesforce-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Salesforce is the sixth-largest buyer of AI companies over the last five years, CB Insights claims. Recently, it said it had a year of \u2018Einstein\u2019 technology- one that analyzes each aspect of a customer\u2019s relationship with a company.<\/span><\/p>\n<h4><strong>i. Pindrop<\/strong><\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pindrop-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-23624 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pindrop-1.png\" alt=\"Python Machine Learning\" width=\"500\" height=\"223\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pindrop-1.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pindrop-1-150x67.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/pindrop-1-300x134.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Pindrop claims to present a pioneering technology for recognizing fraudulent activity over the phone channel. In what it calls \u2018phoneprinting\u2019, for every call, it analyzes 1,300 unique call features and creates an audio fingerprint for each. Such features include noise, location, number history, and call type. It flags suspicious calls and can spot ID spoofing, voice distortion, and social engineering.<\/span><\/p>\n<h4><strong>j. Qubit<\/strong><\/h4>\n<div id=\"attachment_23625\" style=\"width: 320px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/qubit.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23625\" class=\"wp-image-23625 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/qubit.png\" alt=\"Python Machine Learning\" width=\"310\" height=\"110\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/qubit.png 310w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/qubit-150x53.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/07\/qubit-300x106.png 300w\" sizes=\"auto, (max-width: 310px) 100vw, 310px\" \/><\/a><p id=\"caption-attachment-23625\" class=\"wp-caption-text\">Python\u00a0Machine Learning Tutorial-\u00a0Qubit<\/p><\/div>\n<p><span style=\"font-weight: 400\">Qubit has an AI-powered personalized shopping app, Aura. This has a database of products in a range of categories like fashion, clothing, and cosmetics. Pending patents suggest an Instagram-like feed of product images.<\/span><\/p>\n<p>So, this was all in the Python Machine Learning Tutorial. Hope you like our explanation.<\/p>\n<h3><span style=\"font-weight: 400\">Conclusion<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Hence, in this Tutorial, we discussed what Python Machine Learning and tasks in Python and Machine Learning, along with its applications. Also, we saw companies using Machine Learning with Python. By now, we realize machine learning is powerful. Let\u2019s delve into the world of ML and learn something new. Still, if you have any doubts, ask in the comments tab.<\/span><br \/>\n<strong>See also &#8211;\u00a0<\/strong><br \/>\n<strong><a href=\"https:\/\/data-flair.training\/blogs\/python-ml-data-preprocessing\/\">Data Processing in Python ML<\/a><\/strong><br \/>\n<strong><a href=\"https:\/\/en.wikipedia.org\/\">For reference<\/a><\/strong><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1785,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250416015631\\\/https:\\\/\\\/en.wikipedia.org\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 00:03:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-13 04:52:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-18 05:07:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-21 13:23:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-26 12:22:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-31 12:10:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-07 10:52:35&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-13 14:47:13&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-17 21:08:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-21 01:31:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-27 02:58:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-30 07:54:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-04 03:22:35&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-07 14:25:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-12 11:42:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-17 04:43:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-20 19:05:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-24 10:43:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-02 17:43:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-07 16:38:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-11 08:19:52&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-14 09:10:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-17 09:14:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-22 14:56:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-25 14:58:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-28 18:42:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-01 15:37:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-07 13:44:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 03:18:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-20 15:29:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-25 04:48:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-28 11:51:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-06 07:05:52&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-15 00:12:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-19 10:24:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-24 04:01:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 05:04:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-01 09:30:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-04 15:01:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-11 07:17:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-14 08:11:13&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-14 08:11:13&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this Python Machine Learning Tutorial, we will introduce you to machine learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":23561,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36,46],"tags":[16489,8436,8469,8472,10359,10669,16488,13976,15166],"class_list":["post-23548","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","category-python","tag-learn-python-machine-learning","tag-machine-learning-algorithms","tag-machine-learning-tutorial","tag-machine-learning-using-python","tag-python-and-machine-learning","tag-python-machine-learning","tag-python-machine-learning-steps","tag-supervised-learning","tag-unsupervised-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python Machine Learning Tutorial - Tasks and Applications - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn Introduction to Machine Learning With Python, steps in machine Learning with Python, companies using Python ML with applications etc.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/python-machine-learning-tutorial\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Machine Learning Tutorial - 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