The financial crisis of NBFCs a major concern
For a long time, various corporations, including insurance firms, had made investments through short-term instruments in the Infrastructure Finance Company IL&FS, which has to led to a significant liquidity crunch today. Amidst this scenario, the Non-Banking Finance Companies (NBFCs) have been majorly affected by the current liquidity crisis in India. The relationship between the Government and RBI is going through a rough phase as well due to the prevailing circumstances. Adding to the tension is the ban on using Aadhaar information for microlending during December 2018.
Interference of RBI to save IL & FS from the liquidity crunch
The reports from the Ministry of Corporate Affairs (MCA) states that the total debts of IL&FS as of 2017-2018 balance sheet stands at INR 63,000 crores today. The NBFCs were expecting a ray of hope from the RBI, but to their surprise, the reserve bank imposed more rigid rules and regulations for risk management, and asset-liability structures. In the last quarter of 2018, the RBI had announced to inject INR 40,000 crore to help the soaring funds through Government securities into the system.
The problems faced by NBFCs are mostly attributed to their dependencies in short-term borrowings and long-term lending loans to builders and real estate players. Therefore RBI’s ruling enforces more disciplined liquidity management in the future is a welcoming approach. However, the point to be concerned is the unknown course of action for the NBFCs to get out of the present liquidity crisis without which implementing new measures is difficult.
The financial crunch of the NBFCs has affected the loans against the property market in the fiscal year 2019 in India. A secured loan where one party pledges a property with a lender and borrow against it is a Loan Against Property (LAP). In a report from the reporting agency, India Ratings and Research stated that the weak LAP in FY19 is mainly due to lack of strong emotions on the property market and the liquidity crunch faced by NBFCs.
An insight into global M&A
The United Nations Conference on Trade and Development’s (UNCTAD) World Investment Report of 2019 release states that there is a substantial decline in the global FDI by 13% in 2018 which is a third consecutive decline. The slide in global FDI is USD 1.3T in 2018 from USD 1.5T in 2017. However, India witnessed a 6% growth in FDI in 2018 to 42B. This growth is attributed to the activities in cross-border mergers and acquisitions, communication, production, and financial service sector.
The growth of e-commerce in India is expected to increase tentatively by a large extent. It is estimated that India’s e-commerce transactions to reach USD200B by 2026. Further, the trending online retail businesses coupled with telecommunication growth has leveraged the increase in cross-border M&As in India to USD 33B in 2018 from USD 23B in 2017.
The domestic M&A emerging as a life saver
A blockbuster merger was by the American multinational retail corporation, Walmart and India’s largest fashion e-commerce giant Flipkart. The telecommunication alliances and deals were worth USD 2B that collectively associates deals from Vodafone and American Tower. India’s blooming year for M&As was 2018, after which the first quarter of FY2019 has been low. The reason for this subdued effect is attributed to the gloomy global M&A market.
The quarterly report figures indicated a fall in M&A in Q1CY19 to $9.9B from $21.6b in Q1CY18. However, the domestic deals were a breather for India, the most significant being the merger between Bandhan bank and Gruh Finance, which was a $3.2B deal. Another agreement was between GMR airports, and Tata group led Consortium, which amounted to $1.2B. While Japan and Germany were favorite partners for cross-border M&A, the US remained at the top of the chart with 14 inbounds and 14 outbound deals with India. The Indian business executives are high on confidence that one-third of them are expected to undertake M&A in 2019.
To Sum Up
The backup of domestic consolidation for India and continued support of interests from FDI is considered a root cause for having a stable M&A in the future. Given the weak sentiment in the bond market, the current liquidity crisis may remain stubborn for NBFCs at the present moment.
Get more interesting about Current Liquidity Crisis and M&A, by applying for an Investment Banking Courses
Category: Management
What jobs can you get with a Data Analytics degree?
The Data Analytics industry is one of the fastest growing sectors, proving to be a job provider to thousands of potential professionals every year.
Therefore, upon the successful completion of a Data Analytics degree, there are various job options that you can explore. Some of these have been deeply detailed in the following paragraphs. Let’s have a look.
- Gaining a Big Data Analytics course or degree can give you a winning career as a Business Analyst – As a Business Analyst, you will be handling responsibilities such as Database management, cleaning up of data sets and organizing them.
Creation of data visualizations, that convey information in an engaging visual manner to the audience. You will also be responsible for building models that explain the interaction of various variables, and this will be used for companies future references. - Operations Research Analyst – An Operations Research Analyst methodically uses data mining, data modeling, data optimization, and statistical analysis to help companies, corporates and organizations run cohesively and efficiently. Their major responsibility also includes streamlining of operations processes, minimizing waste and optimizing source models. Operations Research Analysts are also called as Operations Analysts, Operations Business Analysts, and Business Operations Analysts.
- Quantitative Analyst – A Quantitative Analyst usually handles the finance department using, applying and implementing trading strategies and assesses risk factors and help/guides in generating maximum profits.
They are deeply involved in the usage and designing of mathematical models that give financial firms and organizations to price and trade securities accordingly.
You will require skills such as a great aptitude in mathematical statistics and finances, calculus, and machine learning. These will be the basics of your career as a successful Quantitative Analyst. - Market Research Analyst – Studying market trends and conditions, and observing them carefully to forecast the profitability and revenue of a certain new product or new service is a job role carried out by Market Research Analysts.
With their skill sets, tools and techniques they research and are able to predict market trends, market downfalls, measure the precise market success of various products and services, and thus identify potential markets where the said product/service can become a future success.
This helps organizations, corporates, and global companies understand market trends and make a fruitful profit for themselves, while making a positive impact on the society at large, with their respective product/service.
Through individual coaching, guidance, and mentorship, you can explore many career advantages through a valid degree course in Data Analytics. These degrees usually have strategic career partnerships with industry relevant global companies and organizations (data analytics course with placement) that will help you mold you step by step process as a Data Analyst.
You will also gain deep practical learning through internships and first-hand exposure in a corporate set up. Networking and socializing for career connects is an important task which you will be able to do through interacting with professionals and experts during your internship.
This will then help you walk down your successful path as a Data Analysts under whatever focus/stream you may later choose to focus on.
Can you become a Data analyst by online tutorials?
In an age where tutorials and lectures are heavily sought after both online and offline, it is easy to see why online tutorials are on-demand, especially to those who are already occupied with jobs with heavy schedules and those professionals who experience time constraints to attend an actual full-time offline course. Although the teaching methods, means, and experience of that of an online tutorial may be quite different, if you are a good self-starter and self-learner, it is quite an engaging and educative activity you can invest your time in regularly.
Let us understand how to learn Data Analytics through online tutorials will guarantee you in becoming a Data Analyst professional. Some of these points are discussed below –
- Avail Online Big Data Analytics course for a minimum fee– Regular online classes, engaging, recorded lectures and practical projects help you gain great insight and enhance your skills regarding your subject matter. There are various online options for you to register and enroll for a course in Data Analysis. It sometimes has payment requests and you will need to pay the required fee for accessing these classes. To maintain a certain quality and standard some of these courses are priced with a standard fee structure.
- A wide variety of knowledge base in Data Analysis to choose from – You can choose from various types of Data Analysis courses that have the online classes option. From the IBM Data Science Professional Certificate to Applied Data Science with Python to Business Analytics to learning the Data Scientist’s Toolbox, the choices for you to pick from are vast and varies, giving you the opportunity to truly specialize and focus on your favorite subject matter.
- Globally recognized online courses – Not only do you have the benefit of investing only a small amount for your Data Analysis certification course, but you will also have global validation for the said course(s) This added advantage makes your knowledge base, skills, tools and techniques learned under the course internationally relevant. This naturally means a great score of career options and job opportunities will now be open to you.
- Free courses – Sometimes there are courses offered absolutely free of cost. Data Analysis has several such courses offered free of cost. The option of the syllabus may be limited but you will gain a little above the general knowledge of the certification course and will be able to become relevant with the skills and knowledge you achieve through this online engagement.
From the above factors it is evident that through practical application, patience and practice, you can forge into a professional Data Analyst career with online support and tutorials. If you expand your knowledge base, there are further professional certifications and degrees to be awarded too. This is available online as well. However, the fee and eligibility criteria may vary accordingly.
So, go on, search for that perfect online course or online tutorial and equip yourself in becoming the best Data Analyst you know. With basic know-how, a minimum investment of money and time, practice and consistent efforts, turn your Data Analyst dream into reality!
How do you balance Machine Learning theory and practice?
Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications, the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn’t feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don’t be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.
How Machine Learning is Reshaping Location-Based Services?
Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.
Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.
Here are some scenarios where AI and ML are set to make our lives better through location-based services.
- Smart real-time gaming options without geographical boundaries.
- Automatic driver-less transport.
- Use of futuristic smartphone-like cyborgs.
- Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
- Thermostats and smart grids for energy distribution to mitigate damage to our environment.
- Robots and elderly care improvements.
- Healthcare and diagnosis of diseases like cancer, diabetes, and more.
- Monitoring banking, credit card and financial frauds.
- Personalized tools for the digital media experience.
- Customized investment reports and advice.
- Improved logistics and systems for distribution.
- Smart homes.
- Integration of face and voice integration, biometrics and security into smart apps.
So how can machine learning actually impact the geo-location empowered services?
Navigational ease:
Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.
ML can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.
Better applications:
Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.
Greater use of smart recommendatory technology:
And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets. The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.
In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.
To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.
How is Big Data Analytics Used For Stock Market Trading?
How is big data analytics used for stock market trading?
Big Data Analytics is the winning ticket to compete against the giants in the stock market. Data Analytics as a career is highly rewarding monetarily with most industries in the market adopting big data to redefine their strategies. Online stock market trading is certainly one area in the finance domain that uses analytical strategies for competitive advantage.
Capital market data analysts are important members of a corporate finance team. They rely on a combination of technical skills, analytical skills and transferable skills to compile and communicate data and collaborate with their organizations to implement strategies that build profitability. If you’re interested in a career in financial analysis, there are several subfields to explore, including capital market analysis.
Organizations and corporates are using analytics and data to get insights into the market trends to make decisions that will have a better impact on their business. The organization involved in healthcare, financial services, technology, and marketing are now increasingly using big data for a lot of their key projects.
The financial services industry has adopted big data analytics in a wide manner and it has helped online traders to make great investment decisions that would generate consistent returns. With rapid changes in the stock market, investors have access to a lot of data.
Big data also lets investors use the data with complex mathematical formulas along with algorithmic trading. In the past, decisions were made on the basis of information on market trends and calculated risks. Computers are now used to feed in a large amount of data which plays a significant role in making online trading decisions.
The online trading landscape is making changes and seeing the use of increased use of algorithms and machine learning to compute big data to make decisions and speculation about the stock market.
Big Data influences online trading in 3 primary ways:
- Levels the playing field to stabilize online trade
Algorithmic trading is the current trend in the financial world and machine learning helps computers to analyze at a rapid speed. The real-time picture that big data analytics provides gives the potential to improve investment opportunities for individuals and trading firms.
- Estimation of outcomes and returns
Access to big data helps to mitigate probable risks in online trading and make precise predictions. Financial analytics helps to tie up principles that affect trends, pricing and price behaviour.
- Improves machine learning and delivers accurate predictions
Big data can be used in combination with machine learning and this helps in making a decision based on logic than estimates and guesses. The data can be reviewed and applications can be developed to update information regularly for making accurate predictions.
In a nutshell, large financial firms to small-time investors can leverage big data to make positive changes to their investment decisions. Information is bought to the fingertips in an accessible format to execute trading decisions.
If you are a trader, you will benefit from a Big Data Analytics course to help you increase your chances of making decisions. It is highly beneficial for those involved in quant trading as it can be used extensively to identify patterns, and trends and predict the outcome of events. Volume, Velocity, and Variety are the pillars of Big Data that aid financial organizations and traders in deriving information for trading decisions.
What are good ideas for Hackathon in Machine Learning?
Hackathons are not merely fetes where you can show off your skills but are also huge opportunities aimed at engaging gainfully and celebrating solving business issues and problems.
The Indian hackathons are corporate sponsored glitz-and-swag events where developers can compete and push boundaries by tackling industry-relevant issues in an environment that is supportive, has fireside learning, exposure to the latest gadgetry and quite like a convoluted career fair.
Job opportunities, internships, different vertical exposure, startup offers, mentorships, peer interactions, rights to brag and prizes abound. For the ML starters, it can be the pitch to learn on, join a community, hone skills, get ideas, and find the right tools and projects in coding, discover the best training and even get placed.
Take your pick from popular and reputed hackathons like MachineHack, TechGig, Hackerearth, Kaggle, and OpenML. Here are some hackathon ideas that can be advantageously used.
- Reach out to the online community through online ongoing hackathons where tech and ML beginners can participate, work on third-party APIs and resolutions and learn from the community. Alexa is being tweaked by Amazon in this manner.
- Permit multiple categories, levels, and submissions: Teams can participate in multiple category hackathons as individuals or by submitting multiple solutions at hackathons. This builds team spirit, allows multiple submissions in various categories and promotes working in communicative teams.
- A balanced cross-functional team yields better results: This secret in hackathons helps teams compete better, ensures better team coordination, provides a platform for the newcomers to work with the experienced and definitely satisfies learning for the whole team. Go for the prize with your team!
- Count results to be superior to techniques: All hackathon participants should use their stack wisely and well, and showcase in the prototype their algorithm and skills in programming. Many a failure occurs with developing scalable models instead of the prototype, using complex databases, algorithms, and a limited stack to develop the prototype in the specified timeframe.
- Hackathons are about prizes for quality problems: Nobody expects a complete solution. What does impress is a simple tweak, innovation or prototype that has the potential to solve and provide a scalable solution?
- The product demo is essential: While the presentation has a bearing on the success and winning becomes addictive the product demo is crucial as it sums up the learning, efforts, and technology used. The real winners are those who compete and learn from their mistakes.
- Follow the hackathon code: The opportunity to learn should not cause problems for others. Follow the guidelines and conduct codes to provide a supportive environment for all.
- Exploit the learning opportunity: To break into the ML field you will need to do a machine learning course and get practical machine learning training with a reputed institute like Imarticus. Then move on to hackathons because these are events akin to sprints where hardware/software is tweaked over the next 24 to 48 hours. The skills and tasks are graded and provide participants with the chance to come up with quick solutions without needing code understanding. Do explore the workshops and 101 sessions on coding to help pick-up the requisite skills.
On a concluding note, there will be various platforms hosting events and hackathons both offline and online which provide participants for everyone.
How Should You Learn Python For Machine Learning And Artificial Intelligence?
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.
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.
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