Machine Learning is indeed one of the most up-and-coming subjects in computer science and its applications. In the past ten years, many tech conglomerates have invested millions of dollars into the field. For example, Google, one of the most significant tech gizmos ever, has come up with its machine learning and AI learning products platform called "Google Cloud."
Indeed machine learning and AI hold a bright future for humanity which we are closing. The development speaks for itself. The discoveries and innovations in the field are so significant that a full ethics board is under preparation for the rules and regulations that must apply to the creation of such near sentient Ais and related projects.
With such a hot market and innovation that exceeds itself every day, it is the perfect moment to invest your time in understanding and learning about how this works and, more specifically, how you can become a fully qualified machine learning and AI development, expert. After all, that's why you're here!
Although machine learning is a very new and upcoming topic, it has many applications in our lives going forward. Most sectors of industry use machine learning to make better UI's, Apps, Software, and hardware! A couple of these examples would be any and every virtual assistant who is shipped inside a device. Such as Google Assistant. Or the many robots which are used in the production of the Tesla factory. Let's take a deeper look into such scopes-
Quantum Computing –
Quantum computing holds great potential for machine learning and its future. This specific type of computing specializes in handling large pools of tasks and data and perform them simultaneously. A concept like a machine learning can boost our developments in quantum computing and help increase the quality and efficiency of analyzing data and profound insights.
Healthcare and Pharma –
⦁ Disease Prediction: The traditional approach for disease prediction includes a limited number of variables such as age, weight, height, gender, etc. but, a machine learning enhanced method of predicting disease can take multiple factors into account, including various studies that have been published about it. This can help humanity considerably as it can provide a safe testing environment for numerous diseases that still have no answer or illness that might be on the horizon.
⦁ Drug Discovery: Drug development is gradual and not economical. Let's take the covid 19 vaccine as an example. It took us 9-12 months to develop an introductory testing phase where we figured out the structure of the vaccine. Machine learning can run millions of tests at the same time, maybe even more! And it learns from the data it collects by running tests. This could mean that we could run multiple tests, see various drug effects on different cell lines and genes, and determine possible side effects.
⦁ Electronic Health Records: This would primarily focus on streamlining the methods of data collection and management. Since health records' data are stored in many different formats, types, and languages, machine learning can help develop a standardized language for all EHRs. Machine learning-fueled EHRs can smooth out and improve the method of distinguishing clinical examples, which can prompt better forecast results.
Manufacturing –
Machine learning devices can help a business from various perspectives. These devices can be utilized for different estimating purposes, for example, checking the wellbeing and status of all machinery or anticipating energy utilization levels. This can help an assembling line or business to design in a like manner. This makes things more productive, yet in addition, saves costs.
Makers are just in the beginning phases of taking on machine learning. In 2020, just 9% of study respondents were utilizing computerized reasoning in their business measures.
Automotive -
We all know who Elon Musk is and what Tesla does. But did you know other companies harness the intelligence of machine learning? Mercedes, Waymo, and Honda are many of the other automotive brands that use self-driving algorithms. These self-driving algorithms combine machine learning and neural networks to form an entire 360' degree perception around your car. This is what helps these cars to make immaculate turns. Every turn you take, the vehicle gets more intelligent and learns to make the next turn smoother.
Tesla recently used their self-driving algorithm and has started to prepare a new neural network that will be used to power the Tesla bot. As new technologies show up, machine learning algorithms can be used more productively and effectively.
But first me we must learn about the scope and potential of what the future holds.
⦁ Software Engineer –
Programmer occupations require a solid fitness for composing code; it rotates around the making of code that upholds the improvement of machine learning calculations. The more significant part of it spins around organizing programs.
Machine learning courses prepare applicants to have the option to compose programming programs for various purposes, including working frameworks, network circulation, and changing over programs into executable records. These different situations should likewise go through thorough testing, and in case messes are discovered, a programmer should inspect the code to find and fix the issue.
Salary Scope- "The compensation for a computer programmer begins at $69,000, and the average yearly compensation is $104,000. Computer programmers, at the top end, can make as much as $153,000."
⦁ Software Developer/App Developer–
Most software development and app development jobs revolve around creating flow charts and idea boards for engineers. In a way, they are the masterminds behind the product. They also test software and apps for bugs and glitches to finalize products for release.
Salary Scope– "The starting salary for a software developer is $58,000, with a median pay of $81,829, and a high-end wage of $120,000."
⦁ Designer in Human-Centered Machine Learning-
Human-centered machine learning is occupied with developing systems that can process information and recognize patterns. This design field in computers efficiently eliminates the need for extra codes that account for every single function and scenario that must be performed or, in other words, allow is to 'learn.'
This specific method makes the product or program smarter and creates a much more human-like experience for users.
Salary Scope: This position pays $69,000 at the low end, an average of $97,000 per year, and can top out at $125,000.
⦁ Data Scientist–
This field isn't just about being the best at reading and writing code. It also revolves around your ability to handle statistics. Many programming languages integrate statistics into their work, such as R, Python, and SQL. Being fluent in all these languages is a must for aspirants. A data scientist will also be involved in information analysis. This is related to the inspection, cleaning, and modeling of data.
Salary Scope:
The low-end annual wage for a data scientist is $87,000.
The average pay is $121,000.
The high end of the salary span is $158,000.
⦁ Computational Linguist –
Machine learning products directly correlate with voice-recognition software that many of us use in many different ways, such as Google Assistant, Siri, Automatic Reply Systems, etc. Computers frequently make mistakes in understanding many modern-day functions, and it is a computer linguists' job to make sure the computer can adapt and learn to understand various languages.
Computational linguists also help computers to understand written language and translate it into spoken words or save those as patterns to increase their knowledge base for use in the future for faster and better retrieval. The goal, in every case, is to aid the machines in comprehending language.
Salary Scope: A computational linguist's starting salary is approximately $65,000 per year, averages $91,000, and can pay as much as $120,000.
Now, how do you start in any of the fields mentioned above?
Well, the answer is simple. By learning! Many websites provide courses with certification which help you get started. Today we will be looking at the best courses provided by Coursera.
⦁ Machine Learning (By Stanford University)-
This course offers a wide introduction to machine learning, statistical pattern recognition, and data mining.
Topics include:
⦁ Administered learning (parametric/non-parametric calculations, support vector machines, pieces, neural organizations).
⦁ Unaided learning (bunching, dimensionality decrease, recommender frameworks, profound learning).
⦁ Best practices in machine learning (predisposition/change hypothesis; advancement measure in machine learning and AI). The course will also draw from various contextual investigations and applications so that you'll figure out how to apply learning calculations to building canny robots (insight, control), text understanding (web search, against spam), computer vision, clinical informatics, sound, database mining, and different regions.
Skills Gained - Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning
Provider – Andrew Ng
Cost - ₹4,394
More about this course here - https://www.coursera.org/learn/machine-learning
⦁ Machine Learning (By the University of Washington)-
This Specialization from driving analysts at the University of Washington acquaints you with the energizing, appealing field of Machine Learning. Through a progression of pragmatic, contextual investigations, you will gain applied involvement with significant spaces of Machine Learning, including Prediction, Classification, Clustering, and Information Retrieval. You will figure out how to dissect vast and complex datasets, make frameworks that adjust and work on over the long run, and construct intelligent applications that can make expectations from data.
Skills Gained - Data Clustering Algorithms, Machine Learning, Classification Algorithms, Decision Tree, Python Programming, Machine Learning Concepts, Deep Learning, Linear Regression, Ridge Regression, Lasso (Statistics)Regression Analysis, Logistic Regression
Provider– Emily Fox & Carlos Guestrin
Cost – ₹3,649 per month till completion. *
More about this course here– https://www.coursera.org/specializations/machine-learning
⦁ IBM Machine Learning (By IBM)-
This program comprises 6 courses furnishing you with a robust hypothetical agreement and significant act of the main calculations, uses, and best practices identified with Machine Learning. You will track and code your activities utilizing the absolute most applicable open-source systems and libraries.
Even though it is suggested that you have some foundation in Python programming, measurements, and direct polynomial math, this moderate series is reasonable for any individual who has some computer abilities is premium in utilizing data, and has an enthusiasm for self-learning. We start tiny, give a robust hypothetical foundation and code-along labs and demos, and move toward more perplexing points.
As well as acquiring a Professional Certificate from Coursera, you will likewise get a computerized Badge from IBM perceiving your capability in Machine Learning.
Skills Gained - Data Science, Deep Learning, Artificial Intelligence (AI), Machine Learning, Python Programming, Feature Engineering, Statistical Hypothesis Testing, Exploratory Data Analysis, Regression Analysis, Supervised Learning, Linear Regression, Ridge Regression
Provider – Mark J Grover & Miguel Maldonado
Cost - ₹2,904 per month till course completion*
More about this course here– https://www.coursera.org/professional-certificates/ibm-machine-learning
⦁ Deep Learning (By DeepLearning.AI)-
In this Specialization, you will fabricate and train neural organization designs like Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and figure out how to improve them with techniques like Dropout, BatchNorm, Xavier/He introduction, and that's only the tip of the iceberg. Prepare to dominate theoretical ideas and their industry applications utilizing Python and TensorFlow and tackle certifiable cases like discourse acknowledgment, music amalgamation, chatbots, machine interpretation, regular language handling, and then some.
AI is changing numerous businesses. The Deep Learning Specialization gives a pathway to you to make the conclusive stride in the realm of AI by assisting you with gaining the information and abilities to step up your profession. En route, you will likewise get vocation exhortation from profound learning specialists from industry and the scholarly community.
Skills Gained- Artificial Neural Network, Convolutional Neural Network, Tensorflow, Recurrent Neural Network, Transformers, Deep Learning, Backpropagation, Python Programming, Neural Network Architecture, Mathematical Optimization, hyperparameter tuning, Inductive Transfer.
Provider- Andrew Ng
Cost- ₹3,649 per month till completion*
More about this course here– https://www.coursera.org/specializations/deep-learning
⦁ Applied Data Science with Python (University of Michigan)-
These five courses acquaint students with data science through the python programming language. This ability-based Specialization is expected for students who have an essential python or programming foundation and need to apply factually, machine learning, data representation, message examination, and informal community investigation strategies through famous python tool compartments like pandas, matplotlib, scikit-learn, nltk, and networkx to gain understanding into their data.
Skills Gained- Text Mining, Python Programming, Pandas, Matplotlib, Numpy, Data Cleansing, Data Virtualization, Data Visualization (DataViz)Machine Learning (ML) Algorithms, Machine Learning, Scikit-Learn, Natural Language Toolkit (NLTK)
Provider- Christopher Brooks
Cost- ₹3,649 - ₹14,598 depending on required time for completion *
More about this course here– https://www.coursera.org/specializations/data-science-python
⦁ Machine Learning Engineering for Production MLOps
(By DeepLearning.AI)-
In this Specialization, you will get comfortable with the capacities, difficulties, and results of machine learning engineering underway. Before the end, you will be prepared to utilize your new creation-prepared abilities to advance driving edge AI innovation to take care of genuine issues.
Skills Gained- Managing Machine Learning Production Systems, Deployment Pipelines, Model Pipelines, Data Pipelines, Machine Learning Engineering for Production, Human-level Performance (HLP)Concept Drift, Model baseline, Project Scoping and Design, ML Deployment Challenges, ML Metadata, Convolutional Neural Network.
Provider- Andrew Ng
Cost- ₹3,649 per month till completion*
More about this course here– https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops
⦁ Machine Learning for All (by the University of London)-
You will learn Machine Learning in this course without needing any programming. It introduces new affiliates to machine learning; therefore, it won't cover the programming-based machine learning tools like python and TensorFlow.
Skills Gained-
⦁ Essential modern machine learning technologies.
⦁ Ability to explain and predict how data affects the results of machine learning
⦁ Use a non-programming-based platform to train a machine learning module using a dataset
⦁ Forming an informed opinion on the benefits and dangers of machine learning to society
Provider- Dr. Marco Gillies
Cost- ₹2,159
More about this course here– https://www.coursera.org/learn/uol-machine-learning-for-all
⦁ Preparing for Google Cloud Certification: Machine Learning Engineer (by Google Cloud)-
87% of Google Cloud ensured clients feel more certain about their cloud abilities. This program gives the capabilities you need to propel your vocation and prepares to help your groundwork for the business perceived Google Cloud Professional Machine Learning Engineer affirmation.
Skills Gained- Google Cloud, Machine Learning, Feature Engineering, Tensorflow, Cloud Computing, Bigquery, Google Cloud Platform, Application Programming Interfaces (API), Inclusive ML, Data Cleansing, Python Programming, Build Input Data Pipeline
Provider- Google Cloud Training
Cost- ₹3,649 per month*
More about this course here– https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate
⦁ Mathematics for Machine Learning (by Imperial College London)-
For a lot of higher-level courses in Machine Learning and Data Science, you discover you need to clean up on the essentials in math - stuff you might have concentrated before in everyday schedule, except which was instructed in another specific circumstance, or not naturally, to such an extent that you battle to relate it to how it's utilized in Computer Science. This Specialization plans to overcome that issue, raising you to an acceptable level in basic math, assembling a natural agreement, and relating it to Machine Learning and Data Science.
Skills Gained- Eigenvalues and Eigenvectors, Principal Component Analysis (PCA), Multivariable Calculus, Linear Algebra, Basis (Linear Algebra), Transformation Matrix, Linear Regression, Vector Calculus, Gradient Descent, Dimensionality Reduction, Python Programming
Provider- David Dye
Cost- ₹3,649 per month*
More about this course here– https://www.coursera.org/specializations/mathematics-machine-learning
⦁ Advanced Machine Learning (by HSE University)-
This Specialization is a prologue to profound learning, support learning, common language understanding, PC vision, and Bayesian techniques. Top Kaggle machine learning specialists and CERN researchers will share their experience tackling certifiable issues and assist you with filling the holes among hypotheses and practice. Completing seven courses allows you to apply present-day machine learning strategies in big business and comprehend the provisos of certifiable information and settings.
Skills Gained- Recurrent Neural Network, Tensorflow, Convolutional Neural Network, Deep Learning, Data Analysis, Feature Extraction, Feature Engineering, Xgboost, Bayesian Optimization, Gaussian Process, Markov Chain Monte Carlo (MCMC), Variational Bayesian Methods
Provider- Evgeny Sokolov
Cost- ₹3,649 per month*
More about this course here– https://www.coursera.org/specializations/aml
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