Python is essential for those looking to get into machine learning and artificial intelligence. It is one of the easiest languages to learn and its range of dynamic semantics is unparalleled. It is easy to read and has reduced the cost of program maintenance. Artificial intelligence allows computers and software to ‘learn’ and identify patterns in order to predict outcomes and make conclusions without human interference or supervision. An example of this is the auto-reply feature on Gmail which ‘reads’ emails and predicts the reply. A machine learning engineer develops intelligent algorithms using data that has to be collected, assembled, and arranged first.
Learning Python is not just important, it is essential to machine learning and AI. There are several courses available online where you can get a Python certification and you should pick one that suits your level of expertise. If you are an absolute beginner, you should choose a course that will help you master the basics of Python. You will also learn how to use popular scientific libraries that support Python users.
The next step involves learning about Python in the scientific computing environment. As a machine learning engineer, one of your main tasks will be to work with large amounts of data. Python allows for intricate statistical modeling of said data. It works well with other programs and tools and allows for a wide range of interaction across different players.
An important area with Python learning is classification. Engineers have to be able to develop a model that classifies, identifies, and describes data classes in order to be able to classify unknown data in the future. It is one of the main forms of supervised learning and is an essential tool in your development of AI. Different types of classifier models include support vector machines, logistic regression, neural networks, and decision trees.
Regression is just as useful as classification and it also is an important form of supervised learning. However, unlike classification where there are distinct finite classes, regression works with predicting continuous numerical data.
When you are faced with data that does not have pre-defined classes, then your best tool is clustering. Simply put, clustering puts together data that are similar and separates the ones that differ. This type of data pooling is a form of unsupervised learning.
One of the best ways to learn the different aspects of Python is to learn by doing. There are several places online where you can practice your knowledge. You can also connect with other engineers and programmers and join a community to discuss and learn from others. Kaggle exercises and competitions are recommended to beginners who are looking for a challenge to flex their theoretical skills.
For those who are serious about machine learning, joining a reputed machine learning course will set you on the right path. The right machine learning training is intensive and allows you to learn hands-on with live projects. However, it is still recommended that you have some previous knowledge about Python, math, and statistics before venturing into these intensive courses.
Tag: machine learning course
How do you learn math quickly for machine and deep learning?
Synopsis
Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI?
Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a machine learning course provides you with an understanding of the same.
Today, individuals who are proficient after completely a machine learning certification are highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective. It is a lucrative career to pursue one that involves complex and challenging problems which need to be solved in creative ways.
Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is a programming language. So do you pursue a machine learning training, and also learn math quickly at the same time?
Here are a few ways to understand how math is applicable in AI
Learn the Basics
Important sections such as Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming. There are plenty of online resources that are useful repositories when it comes to learning math for deep learning.
Invest Sufficient Time
Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code.
Dismiss The Fear
One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help.
Conclusion
Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence.
What Are The Prerequisites For Artificial Intelligence?
Artificial intelligence keeps changing in its definition as does its scope and capabilities. A few decades ago, simple calculators were considered artificial intelligence since math problems were previously only solved by the human brain. Today, artificial intelligence powers home automation systems and gadgets like Google Home, Siri, and Alexa. We see new AI being released almost every week with juggernauts like Google and Facebook it improve the user experience. The auto-reply feature with suggested replies on Gmail is an example of artificial intelligence where the responses are ‘taught’ to the machine.
Having a good foundation is imperative if you want to foray into artificial intelligence. It isn’t as simple as attending a machine learning course to be a valuable employee in the field of AI. People who are interested in artificial intelligence can take several paths to learn the various AI skills necessary for the subject. Based on your previous knowledge and skill level, you should chart your own course.
The prerequisites of artificial intelligence will give you a good foundation to stand upon when you are learning the key concepts. You will have to have a good foundation in calculus, linear algebra, and statistics in order to help you to develop algorithms. You will also need a good knowledge of Python and Python for data science track as it is the predominant language used in machine learning.
Whatever math skills you might have already, you might want to brush up on them before foraying into Artificial Intelligence. There are many courses available online that will go into depth about the various concepts used in AI. If you are getting into AI to solve a problem, then you can rely on existing libraries to help you with the math required. However, if you are looking to get into research or deep into machine learning, you will have to get an in-depth knowledge of math.
The next steps involve learning and soaking up as much machine learning concepts and theory as you can. It will help you on many fronts including planning and collecting data, interpretation of model results, and creating better models.
The next step should focus on data cleaning, exploration, and preparation. As someone who will be working with machine learning, you will have to have a good quality of feature engineering and data cleaning on the original data you have. This is a very important step and will regularly feature in your work in the future. You should spend as much time as you can here, doing practice tests and runs.
For practice, you should participate in as many Kaggle competitions as you can. These are generally easy and will help you work with multiple scenarios and typologies. With machine learning, the more practice you have, the better you are.
As a beginner, these are the steps you will have to take in order to understand the basics of artificial intelligence. If you are interested in a deeper understanding of the subject, then you can opt of Deep Learning and Machine Learning with Big Data.
What Is The Best Way To Learn Artificial INtelligence For a Beginner
10 Essential Qualities For The Age Of Artificial Intelligence
What are some really interesting Machine Learning projects for beginners?
We are witnessing an era of the data revolution. Every organization across the world are trying to make use of data to improve their business. As a result, the demand for skilled Data scientists is skyrocketing. We know that Machine Learning is an important part of Data Science and the Best way to learn it is, of course, practicing it. Any professional taking a Machine learning course should be doing their own projects. Practicing your lessons will help you get familiar with the common ML libraries. Here are a few projects you could try along with your machine learning training.
1. Iris Flowers Classification ML Project
It is the “Hello world” of Machine Learning. This project involves classifying the flowers into 3 different species according to the size of their petals. You can use the Iris Flowersdataset which consists of the numeric attributes of each flower. This data set is considered to be the best available in this classification genre. You will have to use Supervised Machine Learning algorithms to load and handle this data. Also, you can work on this small data without any special transformation or scaling.
2. BigMart Sales Prediction ML Project
The product of this project is a regression model that can predict the sales in 10 BigMart outlets spread across 10 different cities. You can use the BigMart Dataset which consists of the sales data of 1559 products from 10 different outlets. Using Unsupervised Machine Learning algorithms, you can predict the sales of each 1559 product in each outlet.
3. Analysis of Social Media Sentiment Using Twitter Dataset
There are huge amounts of data created by our social media platforms on a regular basis. By mining these data we can understand a lot about the trends, public opinions, and sentiments going on the world. Among them, the data created by Twitter is fund to be best suited for beginners. Using the Twitter data set which consists of around 3 MB data, you can find out what is world talking about the various topics such as movies, elections or sports. This project will help you develop skills in social media mining and classifiers.
4. Recommender system with Movielens Dataset
The modern customers are looking for more customized content everywhere. The applications like Netflix and Hulu are using recommender systems to find content matching each of their customers. This project is about making such a recommender system. You can use the Movielens Dataset which contains around 1.000,200 movie ratings of 3,900 movies made by 6,040 users. You can start building this recommender system with a World-cloud visualization of the movie titles.
5. Stock Prices Predictor
If you would like to work in the finance domain, this project is an excellent choice for you. The aim of this project is to build a predictor system which can learn about the performance of a company and forecast its stock price. You will have to deal with a large variety of data such as prices, volatility indices, fundamental indices and many more. The dataset required for this project can be found at Quandl.
These projects will introduce you to some challenges and their solutions in machine learning. Your machine learning certification will be complete only with such a hands-on experience with ML.
How do you build a career in Machine Learning after completing the ML Foundation Course?
ML/Machine Learning has a promising future. Chatbots, smartphones and most AI platforms essentially use ML. For example, Alexa from Amazon, Google, Facebook, and almost all large platforms point to a growing industry and an all-time high ML jobs demand. Very obviously the need for professionals in ML, AI, and Deep Learning outstrips the demand.
Programmers, graduates in Computer Applications, and even graduates in mathematics, Social Science or Economics can learn and become ML professionals by doing a certified foundation course in Data Analytics/ Data Science course.
The ML professionals essential skill set include
· Computer programming and CS Fundamentals.
· Programming languages like R, Python and some more.
· ML libraries and algorithms.
· Statistics and Probability.
· Software design and systems engineering.
Simple ways to get started with Machine Learning:
A. Read ML books and do a machine learning course with a reputed company like Imarticus which can provide you with reinforcement and certification of your practical skills. Data is the beginning and all about applying your machine learning training, programming knowledge, computer science techniques and statistics to data. R and Python are the most commonly preferred languages. While Python scores in leveraging libraries that are analytics-friendly, practical algorithms, the application development and end-to-end integration using sci-learn and Tensorflow APIs, R is preferred for advanced capabilities in data and statistical inferences analysis.
B. Hone your ML skills with ML Courses which provide ML fundamentals and basic algorithms, statistical pattern recognition and data mining. Your knowledge of statistics should include Bayesian probability, inferential and descriptive statistics for which you will find free courses by Udacity.
C. Applying your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
D. Attend hackathons (Kaggle, TechGig, Hackerearth, etc) which give you support, exposure and mentorship in ML practical ideas.
E. Build your portfolio with
- A project where you collect the data yourself
- A project where you deal with data cleaning, missing data, etc
F. Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.
The Job Scope:
ML can be the most satisfying choice of careers today which include algorithm development and research used for adaptive systems, building predictive methods for product demand and suggestions, and exploring extractable patterns in Big Data. Companies recruit for positions like
- ML Analyst
- ML Engineer
- Data Scientist NLP
- Lead- Data Sciences
- ML Scientist.
Expected payouts:
According to a Gartner report, 2.3 million ML jobs in AI are expected by 2020. Entering the ML field now, according to Digital Vidya, is a great option because the ML payouts for the new entrants vary from Rs 699,807- 891,326. With good expertise in algorithms and data analysis the range of reported salaries could be from Rs 9 lakh to Rs 1.8 crore pa.
What is the practical advice for Machine Learning?
Read, and re-read resources on introductions to Calculus, Mathematical statistics, both differential and inference, algorithm analysis, optimization, differential equations, linear algebra, Python, R and more. Does that sound difficult?
You don’t need advanced learning in them. You will however essentially need to understand how you can apply this learning to handling data analysis of the present and future of nearly every field under the ML, AI, Deep Learning and VR fields.
Here are some advantages of machine learning training in such courses.
- You get to learn ML fundamentals and basic algorithms, statistical pattern recognition, data mining, statistics including Bayesian probability, working with Python, Pandas, and R, the Sci-learn and Tensorflow APIs, and more in a well-paced, learn-at-your-convenience online and classroom training mode.
- The integrated curriculum helps you through practical industry-needed and relevant practical applications like
1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)
2. Supervised learning (neural networks, support vector machines, parametric/non-parametric algorithms, kernels)
3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).
- Most learning is through applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.
- Your certification carries weight as it declares you have applicative knowledge and our job and industry ready.
That having been said, here are some practical tips for ML and discerning learners.
- The first timers in ML rarely get things right. Don’t panic. ML skills are cultivated skills and are meant to be regularly practiced.
- Implement your learning through a model. Compare your implementation skills with others while discovering the open-source libraries, mathematical or program techniques, and tricks, math-tools, etc. that can improve your efficiency.
- Don’t get overwhelmed because leveraging your skills means research-work and doing small projects which help assimilate learning and applying the learning to practical situations whether they be smartphones, VR or chatbots. The tools in Python take care of the math while you get your hands deep into data analysis, data cleaning, and mining and data exploration and predictive analysis.
- It isn’t just about math for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.
- Apply your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
- Attend hackathons (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in ML practical ideas.
- Build your portfolio with projects
a. Where you collect the data yourself
b. Where you get exposure to data cleaning, dealing with missing data, etc.
- Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.
As in all fields, it does get easier as you progress and get adept. So why wait? Partner with Imarticus courses and get a head-start in ML. Go ahead and do a machine learning course with a reputed training institute like Imarticus.
How can you start programming machine learning and artificial intelligence?
One of the biggest developments in the world of computing over the last few years has undoubtedly been artificial intelligence. The ability for a machine to automatically learn and apply methods to improve the quality of output is one of the most in-demand jobs in the world today. Companies are willing to pay big bucks to create systems that can understand their user and make predictive techniques based on their behavior.
These techniques and systems are already being employed by some of the biggest companies in the world. If you’re looking to start your machine learning course, here are a few basics you need to know:
R or Python:
Python and R are two of the most commonly used programming languages with algorithms in all fields dependent on them. While Python is used more in the field of machine learning, it is also easy to understand and learn. Organizations have already implemented it in places to develop applications on analytics. It makes it easier for users to implement any type of algorithms as well.
R is used to create better and more statistical processes. R is generally used to create and formulate statistical processes and companies dependent on data analytics tend to use R.
Statistics:
A basic understanding of Statistics is necessary to comprehend machine learning. While you might need to know what the algorithm does, knowing how the tools can be used for the end result is also necessary. With time, you’ll be able to implement your own algorithms as well and create inferential and descriptive statistical methods at some point.
What skills would you require?
If you’re looking to get a job in the field of artificial intelligence or machine learning, there are a few essential skills, including:
- Communication skills – An ability to communicate is crucial in addition to professionally having a good knowledge of spoken English
- Education – A good graduation degree or A.I. certificate is also required to begin a career in the field of education. This is needed to create your base in this particular field.
- Programming languages – A knowledge of programming languages – understanding of python string, variables, statement, operators, conditions, modules, and sense are super necessary.
- Machine learning techniques – Knowing all about Artificial Intelligence, especially Machine Learning as it is the most lucrative field. It has helped to create powerful websites, given realistic speech popularity and more
- TensorFlow – This is a software program that is periodical for the dataflow to be streamlined to execute different duties. It is generally used for gaining practice in the systems, including the relation to nerve networks.
- Deep Learning – Knowing about deep learning is necessary to use strategies that are rooted in the evaluation of learning statistics, in order to create a unique set of rules and processes.
Hence, with the time you will be able to understand everything about the field of machine learning and artificial intelligence. With Imarticus, you can get the machine learning certification and begin your journey right away!
3 Ways in Which AI is Transforming Business Operations
The business scenario today has evolved and kept pace with technological developments. And AI has been at the helm of the change experience impacting literally every area that affects growth and development. The changing economic, geopolitical and social environments are in a state of constant flux and need businesses to adapt very quickly to tide over the changes in organizational dynamics, critical business glitches like employee retention and hiring or landscape requirements like being scalable and Agile.
Artificial intelligence can help bridge over troubled waters in many areas where human intelligence and limitations fail. Let us explore some of these critical areas where AI has and still has the potential to improve the business scenario.
The successful customer and user experience:
The experience of the customer is what tells brands apart and this differentiator is best exploited through successfully harvesting of the data and changes brought about by AI. Research and use of Walker data suggest that large multinationals like Adobe, Intuit, and EMC have benefitted greatly by entwining the customer experience into their operational daily routines of marketing, sales, and operational routines. And AI makes it possible to offer those great user-experiences crafted from forecasts and gleanings of data on why the customer buys, when and for how much, how the competition fares and their latest parleys, or what the customer wants from you.
The arsenal of data forecasts and insights can personalize an individual’s experience to match his needs, budget, etc, through a more seamless integrated process that offers high satisfaction and customer loyalty. The results are most helpful in rapidly predicting markets, changing products, forecasting customer- behavior, and staying up to date with the latest offers of technology. Thus AI is the one tool that has immense potential in accumulating, understanding and changing the fortunes of business enterprises by forecasting touch-points, trends, brand preferences, pricing strategies and more.
Bettering the hiring process:
The acquisition of skilled talent is critical to all businesses. However, most processes like recruitments, interviews, talent hunting, employee-referrals, and assessments are subject to very many biases, nepotism, controls, and flaws.
For bettering the hiring process certain tasks are all important. Firstly, one has to cast the net wide. Secondly, the talents need to be matched to the job requirements and the process of pivoting in on the right candidate needs to be free of human errors and bias. Lastly, the holistic use of data using the latest developments needs to be deployed. Not surprisingly, AI aided assistants today can make short work of the recruitment process while ensuring a great supply database for recruitments and keeping in mind the specifics of talent growing into higher roles and reducing the pitfalls of employee migration and retention issues.
Retaining and engaging the employees:
Skill and talent lie at the core of the hiring process. With increased demand comes the problem of retention and employee engagement turning into a competitive minefield. Poor management practices, lack of growth on the job and employee engagement have turned into major contributors for lack of retention of employees as is evident from surveys conducted by SalesForce and Gallup.
AI has enabled cutting-edge technologies like analysis of employee sentiments, biometric trackers, and such AI-empowered techniques can aid in effective retention through timely motivation, employee empowerment, continued learning opportunities and ensuring deserving rewards, career growth, skill up-gradation and more. More engaged employees mean better retention, employee loyalty, and engagement.
Conclusion:
In parting, it is valid to note that AI helps the new operations in business which in turn can change the dynamics of a beyond satisfying customer-experience, growing engagement with employees, hiring and retention. People are assets to the company and the twist that AI and technology have brought in can easily transform companies through efficient dynamics, change and people management.
To learn all about futuristic technologies like adaptations of artificial intelligence, powering AI through effective Machine Learning, scouring the growing volumes of data through Deep Learning and beyond to futuristic technology like blockchains for fintech industries try the Imarticus Learning experience.
The Agile Scrum Tutorial are succinct with due emphasis on the practical applications of knowledge and concepts coupled with invaluable modules of self-development and soft-skill training. Besides, one gets the mentorship of certified and industry-drawn mentors and instructors. Go ahead and make the most of opportunities and jobs on offer in their placement program too. Why wait then?
First Bench – Practicing Math Learning by Machine Learning
It’s a common trend that even though a student studied the subject math in the classroom it is often difficult for him to grasp the things taught with accuracy. The same concept was realized by Salai Arjun, the founder of First Bench who claims that there should always be a balance maintained between the things being taught and the level of understanding of the things learned.
This particular urge to actively encourage a balance between math learning and understanding introduced First Bench into the market wherein individual assessment of students’ abilities are done and accordingly the future path is laid out for respective students. From the conceptualization of First Bench, their key goal is to develop an environment of interactive learning with the culmination of in-depth learning. First Bench has been operating since the past five years and has constantly been engaged in comprehending the study patterns, important behavioral feedbacks, and various other data which ultimately led to the development of the application of Machine Learning Technology and the implementation of Machine Learning Tools in their learning practices.
So, how do they actually function? With the help of Artificial Intelligence, First Bench makes use of Machine Learning Technology to operate in situations where human capability becomes limited. With a huge classroom size, it usually becomes tough for a teacher to offer attention to individual students. In such a situation, the Machine Learning Technology finds its valuable application. The Machine Learning Tools used by First Bench are highly engaged in assessing each student and providing tailor-made knowledge.
In the initial stage, the Machine Learning Tools assess individual students effectively before the commencement of any lesson. With the help of this Machine Learning Technology, this enterprise is successful in understanding the capability of each student to effectively comprehend the lessons to be taught. This type of Machine Learning Analysis takes into account the student’s knowledge about the basics and fundamentals of the topics to be learned.
Through Machine Learning Course and the proper implementation of Machine Learning Tools, the respective student’s answers are recorded. With the aid of this information, the Machine Learning Technology will conclude upon the learning course for a specific student and come up with suitable lessons. Thus, with this successful Machine Learning Analysis, a set of the learning system is devised for each student which is exclusive to individual people and is adaptive to the learning structure of the student.
In turn, what will be the results of such a Machine Learning Analysis which makes use of Machine Learning Technology? These Machine Learning tools will consistently assess the student’s performance and guarantee that each individual is able to efficiently learn the subjects and thereby, proceed to the next lessons. With this Machine Learning Analysis, it is obvious that along with the transition from one topic to another the student effectively develops the knowledge about each topic from a very basic level to a more challenging level. A result of this adaptive and efficient Machine Learning Analysis is that every student has the chance to proceed in their learning procedure by taking into account their abilities and inherent intelligence quotient.
The best thing about this is that not a single student is overlooked in this learning process. This method adopted by First Bench is an example of how AI and Machine Learning Technology are capable of adhering to individual students and their learning disabilities, thus creating a powerful learning system. With the success witnessed from the incorporation of Machine Learning Analysis a large number of schools are becoming increasingly interested in participating in such a program. Machine Learning sure has the potential to transform the complete education system in the upcoming generations.
How is Machine Learning Impacting The Education Industry?
Machines today are being used more than ever due to the simplicity of their making and their ability to learn and create value to organizations. The story is not different in the education space either. Artificial intelligence and machine learning are being used to create modules for students, which are highly personalized and intuitive. One of the biggest benefits of using machine learning is because of the computers ability to process large volumes of data both historic and real-time and analyze it for predictive outcomes. Artificial Intelligence is already being used to grade papers (multiple choice questions) fairly and effectively in many schools across the world. It is also impacting the lives of specially-abled students by providing tools and equipment to study and succeed.
The education industry is moving beyond classroom and textbook learning to create more immersive programs for their students. Digital libraries are growing at a rapid pace due to emerging technologies such as big data, cloud computing, and AI. Another great example of machine learning usage is to categorize content in a manner where the student finds it easy to build on existing knowledge. This means that it gives a student the opportunity to learn at his/her own pace and succeed while doing so, thereby greatly boosting the morale of the child.
Here are a few ways machine learning has become a game changer in the education space.
Supporting Teachers
Machine learning helps teachers program a curriculum which is highly individualized for their students. Kids today are smart and fast learners due to their increased exposure to technology. Hence the subjects also need to be contemporary and relevant. All their students’ data in terms of marks, activities can be historically analyzed to create personalized lessons, thereby matching the child’s ability to learn and succeed.
Custom-Made Subjects
One of the biggest advantages of machine learning training is a personalized learning experience for individuals. Every child learns at a different rate and is proficient in different subjects. Once data is gathered on the child’s different abilities, a machine can analyze and build a program specific to the child’s needs thereby controlling the outcome as well as the rate of learning. Another aspect of this is it will help grade students fairly and as per their ability without any human bias.
Increase Retention
Through Machine Learning teachers can identify students who are likely to forget and help them with specialized chapters and techniques to retain the subject. Learning analytics tools such as Wooclap, Yet Analytics, BrightBytes provides precise predictive solutions through different learning ecosystems. This helps educators adapt and improve their content significantly mapping it according to the students’ needs.
Conclusion
Since we are discovering more ways machines can be used effectively in classrooms, one can predict that the growth trajectory and successful integration of machines are highly possible soon.