# Essential Steps to Mastering Machine Learning with Python

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One of the world’s most popular programming languages today, Python is a great tool for Machine Learning (ML) and Artificial Intelligence (AI). It is an open-source, reusable, general-purpose, object-oriented, and interpreted programming tool. Python’s key design ideology is code readability, ease of use and high productivity. The latest trend shows that the interest in Python has grown significantly over the past five years. Python is the top choice for ML/AI enthusiasts when compared to other programming languages.

Image source: Google Trends - comparing Python with other tools in the market

## What makes Python a perfect recipe for Machine Learning?

Python can be used to write Machine Learning algorithms and it computes pretty accurately. Python’s concise and easy readability allows the writing of reliable code very quickly. Another reason for its popularity is the availability of various versatile, ready-to-use libraries.

It has an excellent library ecosystem and a great tool for developing prototypes. Unlike R, Python is a general-purpose programming language which can be used to build web applications and enterprise applications.

The community of Python has developed libraries that adhere to a particular area of data science application. For instance, there are libraries available for handling arrays, performing numerical computation with matrices, statistical computing, machine learning, data visualization and many more. These libraries are highly efficient and make the coding much easier with fewer lines of codes.

Let us have a brief look at some of the important Python libraries that are used for developing machine learning models.

• NumPy: One of the fundamental packages for numerical and scientific computing. It is a mathematical library to work with n-dimensional arrays in Python.
• Pandas: Provides highly efficient, easy-to-use DataFrame for DataFrame manipulations and Exploratory Data Analysis (EDA).
• SciPySciPy is a functional library for scientific and high-performance computations. It contains modules for optimization and for several statistical distributions and tests.
• Matplotlib: It is a complete plotting package that provides 2D plotting as well as 3D plotting. It can plot static and interactive plots.
• Seaborn: Seaborn library is based on Matplotlib. It is used to plot more elegant statistical visualization.
• StatsModels: The StatsModels library provides functionalities for estimation of various statistical models and conducting different statistical tests.
• Scikit-learnScikit-Learn is built on NumPy, SciPy and Matplotlib. Free to use, overpowered and provides various range of supervised and unsupervised machine learning algorithms.

One should also take into account the importance of IDEs specially designed for Python for Machine Learning.

The Jupyter Notebook  -  an open-source web-based application that enables ML enthusiasts to create, share, quote, visualize, and live-code their projects.

There are various other IDEs that can be used like PyCharm, Spyder, Vim, Visual Studio Code. For beginners, there is a nice simple online compiler available – Programiz.

## Roadmap to master Machine Learning Using Python

1. Learn Python: Learn Python from basic to advanced. Practice those features that are important for data analysis, statistical analysis and Machine Learning. Start from declaring variables, conditional statements, control flow statements, functions, collection objects, modules and packages. Deep dive into various libraries that are used for statistical analysis and building machine learning models.
2. Descriptive Analytics : Learn the concept of descriptive analytics, understand the data, learn to load structured data and perform Exploratory Data Analysis (EDA). Practice data filtering, ordering, grouping, multiple joining of datasets. Handle missing values, prepare visualization plots in 2D or 3D format (from libraries like seaborn, matplotlib) to find hidden information and insights.
3. Take a break from Python and Learn Stats - Learn the concept of the random variable and its important role in the field of analytics. Learn to draw insights from the measures of dispersion (mean, median, mode, quartiles and other statistical measures like confidence interval and distribution functions. The next step is to understand probability & various probability distributions and their crucial role in analytics. Understand the concept of various hypothesis tests like t-tests, z-test, ANOVA (Analysis of Variance), ANCOVA (Analysis of Covariance), MANOVA (Multivariate Analysis of Variance), MANCOVA (Multivariate Analysis of Covariance) and chi-square test.
4.  Understand Major Machine Learning Algorithms

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Different algorithms have different tasks. It is advisable to understand the context and select the right algorithm for the right task.

Types of ML ProblemDescriptionExamples
ClassificationPick one of N labelsPredict if loan is going to be defaulted or not
RegressionPredict numerical valuesPredict property price
ClusteringGroup similar examplesMost relevant documents
Association rule learningInfer likely association patterns in dataIf you buy butter you are likely to buy bread (unsupervised
Structured OutputCreate complex outputNatural language parse trees, images recognition bounding boxes
RankingIdentify position on a scale or statusSearch result ranking

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A. Regression (Prediction):  Regression algorithms are used for predicting numeric values. For example, predicting property price, vehicle mileage, stock prices and so on.

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B. Linear Regression – predicting a response variable, which is numeric in nature, using one or more features or variables. Linear regression model is mathematically represented as:

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Various regression algorithms include:

• Linear Regression
• Polynomial Regression
• Exponential Regression
• Decision Tree
• Random Forest
• Neural Network

As a note to new learners, it is suggested to understand the concepts of – Regression assumptions, Ordinary Least Square Method, Dummy Variables (n-1 dummy encoding, one hot encoding), and performance evaluation metrics (RMSE, MSE, MAD).

• Classification We use classification algorithms for predicting a set of items’ classes or a categorical feature. For example, predicting loan default (yes/no) or predicting cancer (yes/no) and so on.

Various classification algorithms include:

• Binomial Logistic Regression
• Fractional Binomial Regression
• Quasibinomial Logistic regression
• Decision Tree
• Random Forest
• Neural Networks
• K-Nearest Neighbor
• Support Vector Machines

Some of the classification algorithms are explained here:

• K-Nearest Neighbors – simple yet often used classification algorithm.
• It is a non-parametric algorithm (does not make any assumption on the underlying data distribution)
• It chooses to memorize the learning instances
• The output is a class membership
• There are three key elements in this approach – a set of labelled objects, eg, a set of stored records, a distance between objects, and the value of k, the number of nearest neighbours
• Distance measures that the K-NN algorithm uses - Euclidean distance (square root of the sum of the squared distance between a new point and the existing point across all the input attributes.

Other distances include – Hamming distance, Manhattan distance, Minkowski distance

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Example of K-NN classification. The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 (solid line circle) it is assigned to the red triangles because there are 2 triangles and only 1 square inside the inner circle. In other words the number of triangles is more than the number of squares If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle). It is to be noted that to avoid equal voting, the value of should be odd and not even.

• Logistic Regression – A supervised algorithm that is used for binary classification. The basis for logistic regression is the logit feature aka sigmoid characteristic which takes any real value and maps it between zero and 1. In other words, Logistic Regression returns a probability value for the class label.
1. If the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it as 0 or NO

1. For instance, let us take cancer prediction. If the output of the Logistic Regression is 0.75, we can say in terms of probability that, “There is a 75 percent chance that the patient will suffer from cancer.”

Decision Tree – Is a type of supervised learning algorithm which is most commonly used in the case of a classification problem. Decision Tree algorithms can also be used for regression problems i.e. to predict a numerical response variable. In other words, Decision Tree works for both categorical and continuous input and output variables.

• Each branch node of the decision tree represents a choice between some alternatives and each leaf node represents a decision.

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As an early learner, it is suggested to understand the concept of ID3 algorithm, Gini Index, Entropy, Information Gain, Standard Deviation and Standard Deviation Reduction.

• Random Forest – is a collection of multiple decision trees. It is a supervised learning algorithm, that can be used for both classification & regression problems. While algorithms like Decision Tree can cause a problem of overfitting wherein a model performs well in training data but does not perform well in testing or unseen data, algorithms like Random Forest can help avoid overfitting.
• It achieves uncorrelated decision trees throughout the concept of bootstrapping (i.e. sampling with replacement) and features randomness.

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As a new learner it is important to understand the concept of bootstrapping.

• Support Vector Machine – a supervised learning algorithm, used for classification problems. Another flavour of Support Vector Machines (SVM) is Support Vector Regressor (SVR) which can be used for regression problems.
• In this, we plot each data item as a point in n-dimensional space
• n here represents the number of features

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The value of each feature is the value of a particular coordinate.

Classification is performed by finding hyperplanes that differentiate the two classes.

It is important to understand the concept of margin, support vectors, hyperplanes and tuning hyper-parameters (kernel, regularization, gamma, margin). Also get to know various types of kernels like linear kernel, radial basis function kernel and polynomial kernel

• Naive Bayes – a supervised learning classifier which assumes features are independent and there is no correlation between them. The idea behind Naïve Bayes algorithm is the Bayes theorem.

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### C. Clustering

Clustering algorithms are unsupervised algorithms that are used for dividing data points into groups such that the data points in each group are similar to each other and very different from other groups.

Some of the clustering algorithms include:

• K-means – An unsupervised learning algorithm in which the items are grouped into k-cluster
• The elements of the cluster are similar or homogenous.
• Euclidean distance is used to calculate the distance between two data points.
• Data points have a centroid; this centroid represents the cluster.
• The objective is to minimize the intra-cluster variations or the squared error function.

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Other types of clustering algorithms:

• DBSCAN
• Mean Shift
• Hierarchical

### d) Association

Association algorithms, which form part of unsupervised learning algorithms, are for associating co-occurring items or events. Association algorithms are rule-based methods for finding out interesting relationships in large sets of data. For example, find out a relationship between products that are being bought together – say, people who buy butter also buy bread.

Some of the association algorithms are:

• Apriori Rules - Most popular algorithm for mining strong associations between variables. To understand how this algorithm works, concepts like Support, Confidence & Lift to be studied.
• ECLAT - Equivalence Class Clustering and bottom-up Lattice Traversal. This is one of the popular algorithms that is used for association problems. This algorithm is an enhanced version of the Apriori algorithm and is more efficient.
• FP Growth - Frequent Pattern Growth Algorithm - Another very efficient & scalable algorithm for mining associations between variables

### e) Anomaly Detection

We recommend the use of anomaly detection for discovering abnormal activities and unusual cases like fraud detection.

An algorithm that can be used for anomaly detection:

• Isolation Forest - This is an unsupervised algorithm that can help isolate anomalies from huge volume of data thereby enabling anomaly detection

### f) Sequence Pattern Mining

We use sequential pattern mining for predicting the next data events between data examples in a sequence.

• Predicting the next dose of medicine for a patient

### g) Dimensionality Reduction

Dimensionality reduction is used for reducing the dimension of the original data. The idea is to reduce the set of random features by obtaining a set of principal components or features. The key thing to understand in this is that the components retain or represent some meaningful properties of the original data. It can be divided into feature extraction and selection.

Algorithms that can be used for dimensionality reduction are:

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Principal Component Analysis - This is a dimensionality reduction algorithm that is used to reduce the number of dimensions or variables in large datasets that have a very high number of variables. However it is to be noted that though PCA transforms a very large set of features or variables into smaller sets, it helps retain most of the information of the dataset. While the reduction of dimensions comes at a cost of model accuracy, the idea is to bring in simplicity in the model by reducing the number of variables or dimensions.

### h) Recommendation Systems -

Recommender Systems are used to build recommendation engines. Recommender algorithms are used in various business areas that include online stores to recommend the right product to its buyers like Amazon , content recommendation for online video & music sites like Netflix, Amazon Prime Music and various social media platforms like FaceBook, Twitter and so on.

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Recommender Engines can be broadly categorized into the following types:

• Content-based methods — recommends items to a user based on their profile history. It revolves around customer’s taste and preference.
• Collaborating filtering method — it can be further subdivided into two categories
• Model-based — a stipulation wherein user and item interact. Both user and item interaction are learned from interactions matrix.
• Memory-based — Unlike model-based it relies on the similarity between the users and the items.
• Hybrid methods — Mix content which is based on collaborative filtering approaches.

Examples:

1. Movie recommendation system
2. Food recommendation system
3. E-commerce recommendation system

5. Choose the Algorithm  Several machine learning models can be used with the given context. These models are chosen depending on the data (image, numerical values, texts, sounds) and the data distribution

6. Train the model — Training the model is a process in which the machine learns from the historical data and provides a mathematical model that can be used for prediction. Different algorithms use different computation methods to compute the weights for each of the variables. Some algorithms like Neural Network initialize the weight of the variables at random. These weights are the values which affect the relationship between the actual and the predicted values.

7. Evaluation metrics to evaluate the model Evaluation process comprises understanding the output model and evaluating the model accuracy for the result. There are various metrics to evaluate model performance. Regression problems have various metrics like MSE, RMSE, MAD, MAPE as key evaluation metrics while classification problems have metrics like Confusion Matrix, Accuracy, Sensitivity (True Positive Rate), Specificity (True Negative Rate), AUC (Area under ROC Curve), Kappa Value and so on.

It is only after the evaluation, the model can be improved or fine-tuned to get more accurate predictions. It is important to know a few more concepts like:

• True Positive
• True Negative
• False Positive
• False Negative
• Confusion Matrix
• Recall (R)
• F1 Score
• ROC
• AUC
• Log loss

When we talk about regression the most commonly used regression metrics are:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Root Mean Squared Logarithmic Error (RMSLE)
• Mean Percentage Error (MPE)
• Mean Absolute Percentage Error (MAPE)

We must know when to use which metric. It depends on the kind of data and the target variable you have.

8. Tweaking the model or the hyperparameter tuning  - With great models, comes the great problem of optimizing hyperparameters to build an improved and accurate ML model. Tuning certain parameters (which are called hyperparameters) is important to ensure improved performance. The hyperparameters vary from algorithm to algorithm and it is important to learn the hyperparameters for each algorithm.

9. Making predictions  - The final nail to the coffin. With all these aforementioned steps followed one can tackle real-life problems with advanced Machine Learning models.

Steps to remember while building the ML model:

• Data assembling or data collection  - generally represents the data in the form of the dataset.
• Data preparation - understanding the problem statement. This includes data wrangling for building or training models, data cleaning, removing duplicates, checking for missing values, data visualization for understanding the relationship between variables, checking for (imbalanced) bias data, and other exploratory data analysis. It also includes splitting the data into train and test.
• Choosing the model  -  the ML model which answers the problem statement. Different algorithms serve different purposes.
• Training the model  -  the idea to train the model is to ensure that the prediction is accurate more often.
• Model evaluation -  evaluation metric to measure the performance of the model. How does the model perform against the previously unseen data? The train/test splitting ratio -  (70:30) or (80:20), depending on the dataset. There is no exact rule to split the data by (80:20) or (70:30); it depends on the data and the target variable. Some of the data scientists use a range of 60% to 80% for training and the rest for testing the model.
• Parameter tuning - to ensure improved performance by controlling the model’s learning process. The hyperparameters have to be tuned so that the model can optimally solve the machine learning problem. For parameter tuning, we either specify a grid of parameters known as the grid search or we randomly select a combination of parameters known as the random search.
• GridSearchCV -  It is the process to search the best combination of parameters over the grid. For instance, n_estimator could possibly be 100,250,350,500; max_depth can be 2,5,11,15 and the criterion could be gini or entropy. Though these don’t look like a lot of parameters, just imagine the scenario if the dataset is too large. The grid search has to run on a loop and calculate the score on the validation set.
• RandomSearchCV - We randomly select a combination of parameters and then calculate the cross-validation score. It computes faster than GridSearch.

Note: Cross-validation is the first and most essential step when it comes to building ML models. If the cross-validation score is good, we can say that the validation data is a representation of training or the real-world data.

• Finally, making predictions -  using the test data, of how the model will perform in real-world cases.

Conclusion

Python has an extensive catalogue of modules and frameworks. It is fast, less complex and thus it saves development time and cost. It makes the program completely readable particularly for novice users. This particular feature makes Python an ideal recipe for Machine Learning.

Both Machine Learning and Deep Learning require work on complex algorithms and several workflows. When using Python, the developer can worry less about the coding, and can focus more on finding the solution. It is open-source and has an abundance of available resources and step-by-step documentation. It also has an active community of developers who are open to knowledge sharing and networking. The benefits and the ease of coding makes Python the go to choice for developers. We saw how Python has an edge over other programming tools, and why knowledge of Python is essential for ML right now.

Summing up we saw the benefits of Python, the way ahead for beginners and finally the steps required in a machine learning project. This article can be considered as a roadmap to your mastery over Machine Learning.

### KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

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It is also behind the SAS Institute for Data Science. Hence, SAS is the organisation you would want to go to if you're aiming for a long-term career in data science. Conclusion To conclude, big data and data Analytics are a field of endless opportunities. By investing in the right credential, one can pave the way to a viable and lucrative career path. Beware though, there are lots of companies that provide certifications, but only recognised and reputed credentials will give you the opportunities you are seeking. Hiring companies look for these certifications as a mark of authenticity of your hands-on experience and the amount of work you can handle effectively. Therefore, the credential you choose for yourself plays a vital role in the career you can have in the field of Data analytics. Happy learning! 5631 Top Data Analytics Certifications What is data analytics?In the world of IT, every s... Read More ## Why Should You Start a Career in Machine Learning? If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it. Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques. We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses. This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation. Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment. The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of$112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch \$20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

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