BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand. JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students. Here is a small article on the Future of Machine Learning, hope you will find it useful. Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results. Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.

1. Machine Learning: Need of Machine Learning, Its Challenges and its
Applications
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well
updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth
Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Introduction
Now a days there are so many companies investing on AI and Machine Learning. The three fundamental
forces are working behind to motivate these companies to invest more and more and day by day. Firstly,
and utmost important force is the number of devices connected to internet and producing data on a
regular interval every second. The devices are the laptops we all are using on daily basis, smart watches,
all interconnected household or handheld or personal devices like refrigerators, microphones, TV, ACs
etc. Each of these devices is capturing data and then sending it to cloud. There are more than 60 billion of
IoT based interconnected and over the time each of these devices would start becoming smarter and
smarter. These devices use Artificial Intelligence and Machine Learning to make better decisions.
The second force is the cost of storing of data It is not that only data is generating in huge volume through
these devices, but the cost of storing the data has also gone down drastically. Earlier in 1980’s where the
cost of storage was in dollars has now come down in cents.
The entire universe music can be stored in only 500$ in present time.
The third force is the computational cost which is continuously falling down. In today’s time we are
generating data if terabytes, nano bytes, petabytes which can be stored in cheaper cost and can be
computed in a very less time and at a very low price.
With the advent of Cloud technologies and the features they provide to us, there is no limit to how much
computation can you do at a very low cost.
These are the three forces which are driving machine learning as a technology which is still in its first
phase. These technologies have just started to change our lives and over the time these will become more
and more important in future.
Machine Learning
Machine Learning is a field of Computer science in which computer systems are able to learn from past
experiences, examples, environment. With help of various Machine Learning Algorithms, Computers are
provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provides the technique of predicting the future outcomes or classifying
information from the given input to the Machines so that the appropriate decisions can be taken.
Definition Given by Mitchell:

2. “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 tasks in T, as measured by P, improves with
experience E.”
These three parameters are the main components of all Machine Learning Algorithms.
ML is a field of AI consisting of learning algorithms that −
1. Improve their performance (P)
2. At executing some task (T)
3. Over time with experience (E)
Task(T)

3. Task T can be said here as any real-world problem which is to be solved. Few real-world problems
are weather forecasting, predicting Sales of Product according to change of weather, best price
predictions of a product or house, finding best marketing strategy to gain maximum profit, finding
best supply chain process to recover from Covid and many more.
A task T is said to be a ML based task when it is based on the process and the system must
follow for operating on data points.
Examples of ML based tasks are:
• Classification
• Regression
• Annotation
• Clustering and many more.
Experience (E)
Experience is basically the knowledge gained from data points provided to the algorithm or
model. The developed Model when provided with the dataset, it can be used as per the
requirement in future.
As the Model will run iteratively it keeps on learning from its inherent pattern. This learning that
model acquires over the time is called experience(E).
Here the Experience gained by Machines are exactly same as we human being gain experiences
over the time from our learnings, failures, feedback, environment and other attributes like
situation, relationships etc.
Ways to Learn and Gain Experience in ML are:
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
Basically, the experience gained by designed Machine Learning Model or algorithm will be used
to solve the task T.
Performance (P)
Performance is the most important outcome as any ML algorithm is supposed to perform task
and gain experience with the passage of time. The measure which tells whether ML algorithm is
performing as per expectation or not is its performance (P). P is basically a quantitative metric
that tells how a model is performing the task, T, using its experience, E. There are many metrics
that help to understand the ML performance. These are:
• Accuracy Score
• F1 Score
• Confusion Matrix
• Precision
• Recall
• Sensitivity etc.
Need of Machine Learning:

4. The data has been exploded and generated by users in a very high speed with great Volume. The
data has so much of hidden facts which are if analyzed properly can help us to reach to some
affective conclusion which can results in positive way. Machine Learning algorithms are capable
enough to perform fast calculations on complex and huge amount of data to produce results.
These outcomes and different evaluations help all organizations to predictions the future results.
As a human being it is a very tedious, time consuming and difficult task to perform such complex
calculations of on voluminous data generated in velocity and variety. This is why need for
machine learning is increasing day by day.
Machine Learning Algorithms helps to train machines in different ways as per our requirement by
giving the huge amount of data to it. Machines explore the data and processes it to construct a
model which iteratively used to predict the outcomes automatically. The performance of the
machine depends upon the accuracy of outcome on the amount of data processed and the cost
of function incurred. The ability of Machine learning algorithms of finding out hidden patterns and
extracting useful information from data has increased the dependency of we all human beings
and companies. Fast Computational ability on rapidly incrementing and explosion of variety of
complex data producing affective predictions is the main reason behind dependency and the
need to learn Machine Learning.
Challenges in Machines Learning
Still there is so much of work is going on in the area of Machine Learning as ML has not
been able to overcome number of challenges. The challenges at present ML is facing and
still so much of work is to be done in that area are –
Quality of Data:
The huge volume of data is generating is a fast speed and in variety and from different
sources. To implement appropriate ML algorithm on good quality of data is the biggest
challenge. Maximum time spent by Data Scientists and analysts is in data preprocessing
and feature extraction. To do accurate prediction it is very important to use good quality
of data and low quality of data leads to wrong predictions only.
Consumption of Time:
Various preprocessing ML steps required before creating a model is more time
consuming. Data Acquisition, Feature Extraction and Information retrieval are most time
consuming and need to redefine with better solutions.
Technology Experts:
Still people are learning and there is lack in awareness as it is still in its infant stage. It is
difficult for the companies to get Experts in various domains related to ML to perform
analysis and to get timely and accurate predictions.
Difficulty in Obtaining Objective:
People are still not clear about the advantages of ML in Business Area. They are still not
able to decide the clear objective with well-defined Goal.
Overfitting or Underfitting:

5. It is difficult to create an Ideal Model and represent a problem with solution. Many times,
the model suffers from overfitting or underfitting issues and not able to produce accurate
results.
Curse of Dimensionality:
The datasets are having so many numbers of features and selecting appropriate feature
using different feature techniques and removing unwanted features using Dimensionality
reduction is a tedious task. Even after performing these steps, we are not able to extract
exactly the features which helps us to produce ideal model.
Difficulty in Deployment:
The model once created is difficult to deploy due to the complexity of it. It quite difficult
to be deployed it in real life even if tested using different ML algorithms and selected the
one with better accuracy.
Applications of Machines Learning
Machine Learning is the most rapidly growing technology and according to researchers we are in the
golden year of AI and ML. It is used to solve many real-world complex problems which cannot be solved
with traditional approach. Following are some real-world applications of ML −
• Emotion analysis
• Sentiment analysis
• Error detection and prevention
• Weather forecasting and prediction
• Stock market analysis and forecasting
• Speech synthesis
• Speech recognition
• Customer segmentation
• Object recognition
• Fraud detection
• Fraud prevention
• Recommendation of products to customer in online shopping.
The Future of Machine Learning
Machine Learning has brought the drastic change and increased its importance in business applications.
Connected AI systems will enable ML algorithms to “continuously learn,” based on newly emerging
information on the internet. This advent has forced the hardware vendors to enhance the CPU powers so
that machines can accommodate the ML data processing. Hardware Vendors started designing their
machines again to justice to the computation and storing power of ML.
A Market Research Engine report says that the Global Service Robotics market is expected to reach almost
$24 billion by 2022. The market is projected to have a compound annual growth rate (CAGR) of more than
15%.

6. Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be
built with commercials instead of open-source platform.
Improved Unsupervised Algorithms:
The dependency on ML is potentially increased because of Improved Unsupervised Algorithms are one of
the applications of ML that you can witness in the coming days. Being used in multiple
industries, improved unsupervised ML algorithms will certainly shape the future of Machine Learning.
Modified ML algorithms has increased the efficiency of different applications these are:
Better and Accurate Search Results on the Web Engine
Search engines will boost both the user experiences and the host experiences rapidly in fast progress.
With further neural network growth and development blended with evolving deep learning techniques,
the future search engines will be far better in providing responses and perceptions that are significantly
germane to the searchers, explorers of the web
Ability to customize the products far more precisely than ever before with algorithms to break down
exactly how their products are used, maximizing value for both the organization and the clients. With
more advancements and discoveries in the dynamic field of machine learning and its algorithms, for the
clients on a larger scale, we shall start to see exact targeting and fine-tuned customization in the near
future.
Quantum Machine Learning algorithms have great potential that can completely transform the future of
ML. Increased adoption of Quantum Computing by businesses is one of the major applications of Machine
Learning trending now.
Machine Learning personalization algorithms are used to offer product recommendations to customers.
Using Machine Learning algorithms to render enhanced personalization is an important ML application
that is worth noticing.
The future of Machine Learning is the rise of Robots and Robotic Automation also. Use of robots has been
increased potentially to carry out business operations. These robots use machine learning algorithms to
perform tasks and to execute tasks in a faster manner.
This is also why the businesses across the globe are adopting robotic techniques to increase their
productivity.
The machine Learning revolution will stay with us for a long and so will be the future of Machine Learning.
The thought machine is the greatest blessing of AI to civilization. ML applications these days are becoming
more interactive and intelligent than ever before. Self-driving cars, automated assistants, autonomous
factory workers, and smart cities have recently shown that smart machines are feasible.
Top Companies using Machine Learning:
1. Google – Neural Networks and ‘Machines That Dream’
2. Facebook – Chatbot Army
3. IBM – Better Healthcare
4. Salesforce – Intelligent CRMs

7. 5. Pinterest – Improved Content Discovery
6. Twitter – Curated Timelines
7. HubSpot – Smarter Sales
8. Baidu – The Future of Voice Search
9. Yelp – Image Curation at Scale
10. Edgecase – Improving Ecommerce Conversion Rates
Top Companies Hiring ML experts
1. Amazon
2. TCS
3. Google
4. Salesforce
5. Accenture
6. Wipro
7. Microsoft
8. Intel Corporation
9. Cognizant Technology Solutions
10. Quantifi
Job Opportunities as an ML expert
• Machine Learning Researchers.
• AI Engineer.
• Data Mining and Analysis.
• Machine Learning Engineer.
• Data Scientist.
• Business Intelligence (BI) Developer
• Natural Language Processing (NLP) Scientist
• Human-Centered Machine Learning Designer
• Software Engineer
• Software Developer
• Computational Linguist
BCA Department of JIMS Vasant Kunj-II encourages students to learn latest technologies from various
sources and get expertise in this area.
The Future of ML will go long. We emphasize our students to various projects based on different
applications of AI. We motivate them and guide them. Students uses latest tools to design various Models
like Jupyter Notebook of Anaconda, Gocolab- Google Collaboratory, VSCode.

8. Faculties keep on sharing small articles on the upcoming technologies to give them overview of that. We
wish them success in their future endeavor.
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well
updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth
Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
To Know More About BCAJIMS Click here: https://www.jimssouthdelhi.com/course-bca.html
BCA Admission Open 2022
Keyboard Shortcuts used in Jupyter Notebook
Machine Learning and Role of Models
Dr. Arpana Chaturvedi
HOD-IT
JIMS Vasant Kunj-II