Question 1

Why learn Machine Learning with Python?

Accepted Answer

Machine learning and AI have taken centre stage as more and more brands realise the possibilities of these tools in the post-COVID world. Per Gartner, AI/ML jobs are one of the most difficult to hire for recruiters. Additionally, according to the World Economic Forum, the demand for AI and machine learning professionals is projected to grow by 40%, or 1 million jobs, soon. So, it is easy to get hired if you equip yourself with the required ML skills.

Some of the benefits of learning through machine learning with Python full course include the following:

Validates your machine learning skills needed for improving career opportunities
Improved potential for a better salary
Expanded knowledge base
It reels in better job opportunities
Machine Learning engineers earn a pretty penny
Demand for Machine Learning skills is only increasing
Most of the industries are shifting to Machine Learning

Question 2

What are the 6 best tips to learn Python Programming as a beginner?

Accepted Answer

The following are some tips to help you learn basic Python skills: Consistency is Key: Code every day. Consistency is very important when you are learning a new programming language. Commit to it and code every day. While it may be hard to believe, it is a fact that muscle memory plays a big role in programming. It may seem like a daunting task at first, but do not give up. Start small with coding for 25 minutes each day and progressively increase your efforts from there on out.  Write it out: As you move further in your journey as a programmer, there will be moments when you wonder if you should have taken notes from the beginning. Let us tell you-you should! Several studies have proven over the past several years that writing down a particular thing with your own hands, is the key to long term retention of the concept. This tip is especially beneficial for those programmers who are learning Python with the aim of becoming full-time developers. Another benefit of writing down stuff by hand is that it helps you plan out your code on paper before you move to actually implementing it on your computer, where visualization of your code is an issue at the beginning of your coding journey. Go interactive!: The interactive Python shell is one of the best learning tools, irrespective of whether you are writing code for the first time, learning about Python data structures such as dictionaries, list, strings, etc., or debugging an application. In order to initialize the Python shell, simply open your terminal and type in Python or Python3 (as the case may be) into the command line and hit Enter. Assume the role of a Bug Bounty Hunter: It is inevitable that you will run into bugs. The best way to pick up basic Python programming skills is to sit down and solve the bugs on your own. Do not let the bugs frustrate you. Instead, take up the challenge as a means of learning Python in the best possible way and take pride in becoming a Bug Bounty Hunter. Surround yourself with other people who are learning: Coding may seem like a solitary activity, but it actually brings out the best results when it is done in a collaborative manner. It is important for you to surround yourself with other people who are learning Python as well as this not only gives you a boost and keeps you going, but also helps you receive helpful tips and tricks from other, along the way. While you can find the best course for machine learning with Python to get your hands dirty, our Machine Learning with Python course at upGrad KnowledgeHut will have co-learners and instructors who will encourage you to learn. Opt for Pair programming: Pair programming is a technique in which two developers work on a particular piece of work/ code together. One programmer acts as the Driver, while the other acts as the Navigator. The driver of the code is the one who is actually writing the code, while the Navigator is the developer who guides the entire process, gives reviews and feedback as well as confirms the correctness of the code while it is being written. Pair Programming not only helps a developer learn mutually from other developers but also exposes him/her to multiple ways of thinking and ideas and gives them a fresh perspective on debugging, problem solving or even on writing the code itself.

Question 3

Can I study Machine Learning without Python?

Accepted Answer

Most of the knowledge providers you find online conduct machine learning with Python courses, R, or MATLAB. However, you must not learn machine learning with Python only. You can learn ML using programming languages such as R, Java, C+, C++, C#, VB, Scala, MATLAB, etc. Most courses, including our machine learning with Python certification course at upGrad KnowledgeHut, train this branch of AI with Python because Python’s scikit-learn package is beneficial for your learning.

Question 4

Is Machine Learning with Python worth it?

Accepted Answer

While you will get a list of programming language choices, Python is considered the best to build machine learning models. The reasons are manifold. Python is a language that learners find easy to use. It comes with an extensive framework library and flexibility. If you are not familiar with Python much, our machine learning with Python training course at upGrad KnowledgeHut can help you. Here are the program highlights for your reference:

Machine Learning with Python Highlights	Certification Course Details
Instructor-Led Sessions	34+ Hours
Assignments, MCQs	80 Hours
Hands-On Practice	45 Hours
Projects	10 Real-World, Live
Skill Level Update	Fundamental to Advanced
Code Reviews	By Professionals

Question 5

Is Python or C++ better for machine learning?

Accepted Answer

Both languages are extensively used in machine learning. Both have benefits and drawbacks. Here is a comparison table to assist with your decision-making:

Features	Python	C++
Community of developers	Yes, More than C++	Large
ML Libraries	Yes, More than C++	Yes
Flexibility, Debugging Ease	Yes, More than C++	Yes
Memory Management	Yes	Yes, Faster and More Control
High Performance	Yes	Yes, More than Python
Suitable for Complex and Large-Scale Applications	Yes	Yes, More than Python

Our course at upGrad KnowledgeHut is one of the best machine learning with Python courses. You can explore the curriculum to understand how Python can extract value from of your work.

Question 6

What are the Top Videos, Tutorials on Machine Learning [2023-24]?

Accepted Answer

Whether you are a novice or expert at machine learning, videos are a fantastic way to gain more expertise. There is a plethora of free resources available throughout the internet, the most accessible of which is YouTube. But the sheer volume of results that pop up may overwhelm learners.

Here’s a great place to get started with the top videos and tutorials on deep learning and Machine Learning:

What is Machine Learning | Machine Learning Process and Models | Machine Learning Types | AI and ML

Another way is to find the best online course for machine learning with Python available and nose-dive into it. Our upGrad KnowledgeHut offers Machine Learning with Python course that will assist you in building a strong ML foundation.

Question 7

Why Study the complete machine learning course with Python from upGrad KnowledgeHut?

Accepted Answer

Python is one of the most popular languages for the purpose of Machine Learning. At upGrad KnowledgeHut, we have curated some of the best resources and videos from all over the internet. Most resources that are included as a part of the advanced machine learning with Python course at upGrad KnowledgeHut are drawn from some of the top-notch Python conferences such as PyCon as well as PyData, etc., created by some of the world’s top Data Scientists, making it one of the best machine learning with Python certification courses around the world.

Much of the resources that are offered to learners at upGrad KnowledgeHut are hands-on tutorials. These tutorials are all accompanied by extensive code to help participants implement the algorithm or the program taught in that particular tutorial. Real-world data sets are also included along with the tutorials offered by upGrad KnowledgeHut.

Question 8

How can I gain mastery over Machine Learning with Python Course?

Accepted Answer

Whether you are opting for a machine learning with Python free online course or a paid one, below are some major topics the course should help you master:

Programming skills: You will be coding your algorithm and using code to prepare your data which is to be injected into the model you coded. It becomes, therefore, essential that you are comfortable with your programming skills.

Explore ML: It is recommended that you try to gather as much knowledge from different sources. Try to take online courses or classroom ones that can increase the breadth and width of your knowledge.

Data: Data is the most important part of your ML project because it is data upon which we apply our ML magic. Learn about different kinds of data, how to clean data, prepare data and work with data to gain insights. Learn about features (engineering) as well.

Libraries: This is not the most important part but an important one at least. There are tons of open source and free libraries out there for ML, but it is imperative that we choose and get ourselves acquainted with the libraries that are suitable, reliable, and easy to implement. For eg: Pandas, SciPy, etc.

Deep learning: After you have mastered basic ML stuff, it is time to move up to the advanced part of AI which is Deep learning. Deep learning is, in a way, adding the brain to your algorithms as it replaces the need for a human expert or engineer to supervise it.

Polishing and Implementation: You cannot aim to master ML without implementing it in real life. Keep polishing your mathematical and statistical skills as these are the ones that will be frequently used throughout your ML journey. Finally try some hands-on assignments from online courses that are available for free and get them checked with an expert to improve your skills. The most effective way is to participate in competitions like Kaggle etc.

Question 9

How long does it take to learn Machine Learning with Python?

Accepted Answer

In theory, you can learn machine learning with Python in three to four months. However, it will take you more than six months to practice and get a good grip. Even if the machine learning with Python programming certification you have selected is just for four weeks, you need to keep on practising after the course to become an expert in ML.

Question 10

What practical skills can I expect to have after completing the Machine Learning with Python course?

Accepted Answer

upGrad KnowledgeHut’s Machine Learning with Python certification course is one of the best machine learning with Python courses. This course is focused on helping you gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals become job-ready across industries. At the end of the course, you will be able to:  Build Python programs: distribution, user-defined functions, importing datasets and more Manipulate and analyse data using Pandas library Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot Build data distribution models: variance, standard deviation, interquartile range Calculate conditional probability via Hypothesis Testing Perform analysis of variance (ANOVA) Build linear regression models, evaluate model parameters, and measure performance metrics Use Dimensionality Reduction Build Logistic Regression models, evaluate model parameters, and measure performance metrics Perform K-means Clustering and Hierarchical Clustering Build KNN algorithm models to find the optimum value of K Build Decision Tree models for both regression and classification problems Use ensemble techniques like averaging, weighted averaging, max voting Use techniques of bootstrap sampling, bagging and boosting Build Random Forest models Find optimum number of components/factors using scree plot, one-eigenvalue criterion Perform PCA/Factor Analysis Build Apriori algorithms with key metrics like Support, Confidence and Lift Build recommendation engines using UBCF and IBCF

Question 11

Who is the Machine Learning with Python course for?

Accepted Answer

Machine Learning course with Python is ideal for:

Anyone interested in Machine Learning and using it to solve problems

Software or Data Engineers interested in quantitative analysis with Python

Data Analysts, Economists or Researchers

Our online machine learning with Python course at upGrad KnowledgeHut is designed to suit learners that come from various backgrounds and interests. If you fall into one of the above categories, you can benefit more from the course.

Question 12

I am a novice in Machine Learning. Is this course suitable for me?

Accepted Answer

Learn Machine Learning with Python through a curriculum designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the online machine learning with Python training course covers everything you need to know, whether you’re a novice or an expert.

To facilitate the development of practical machine learning with Python skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The Machine Learning with Python syllabus is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.

The final modules deep dive into Machine Learning and take learners through machine learning algorithms in Python, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases, all in all making it the best machine learning with Python course.

Question 13

Can I pursue this course along with my full-time job?

Accepted Answer

Yes, our Machine Learning with Python certification course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. You can consider this machine learning with Python crash course that will help build your foundation and make you relevant in the competitive data-related jobs market.

Question 14

How many hours of study will be required every week?

Accepted Answer

The complete Machine Learning with Python course content requires daily training hours. In addition to the training hours, we recommend spending about two hours every day, for the duration of the course. This daily self-practice will help you retain most of the learnings. If you implement the learnings at work, there is nothing better than that.

Question 15

What technical skills are required to learn ML and become a Machine Learning Engineer?

Accepted Answer

In order to thoroughly understand the concepts of Machine Learning and to develop successful Machine Learning projects, it is important to know the following:

Programming languages: Being comfortable working with programming languages such as Python, Java, Scala, etc., is an important prerequisite before you embark upon your Machine Learning journey. A learner of Machine Learning must be able to make use of these programming languages, at least in a basic manner, so as to be able to grasp Machine Learning skills more thoroughly. Knowledge of formatting data, processing the data in order to make it compatible with the machine learning algorithm etc. are also skills that will come in handy on your journey to mastering Machine Learning concepts and their application in real life.

Database skills: A prior knowledge and experience of working with MySQL as well as relational databases is also a prerequisite to fully gauging and understanding Machine Learning concepts. During the course of their Machine Learning journey, learners will have to make use of data sets obtained from various different data sources simultaneously. Programmers must be able to read data available from different sources, and then convert this data obtained into a format that is both readable by as well as compatible with the machine learning framework that they are working on at the moment.

Machine Learning visualization tools: There are several tools that are available for visualizing the data used in Machine Learning. Basic knowledge and understanding of some of these tools will turn out to be helpful while you are applying the concepts of Machine Learning in real life.

Knowledge of Machine learning frameworks: Several statistical, as well as mathematical algorithms, are made use of in order to design a Machine Learning model to learn from the input data and come to a prediction for a given data set. Knowledge of one or more of these frameworks including Apache Spark ML, Scala NLP, R, TensorFlow etc. is a prerequisite for a thorough understanding of Machine Learning concepts.

Mathematical skills: Mathematics is at the heart of Machine Learning. This is because it is through these mathematical algorithms and concepts that the data is processed, analyzed and used in order to form a Machine Learning model. The following is a list of some of the mathematical concepts that a student of Machine learning must know in order to understand and implement machine learning models and concepts successfully:

Optimization

Linear algebra

Calculus of variations

Probability theory

Calculus

Bayesian Modeling

Fitting of a distribution

Probability Distributions

Hypothesis Testing

Regression and Time Series

Mathematical statistics

Statistics and Probability

Differential equations

Graph theory

Our Machine Learning with Python course syllabus at upGrad KnowledgeHut assists you in imbibing these skills to help you stay ahead of your peers.

Question 16

What is the Machine Learning with Python prerequisites?

Accepted Answer

There are some prerequisites for attending this online Machine Learning with Python certificate course and they are given below:

Sufficient knowledge of at least one coding language is required.

Minimalistic and intuitive, Python is best-suited for Machine Learning training.

If you fulfill these requirements, you will be able to maximize the value of the course and become a skilled data professional in no time.

Question 17

What are the software and system requirements for Machine Learning with Python Projects?

Accepted Answer

To attend the complete Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements:

Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors

4 GB RAM

10 GB of free space

Software Requirements:

Web browsers, such as Google Chrome, Microsoft Edge, or Firefox

System Requirements:

32 or 64-bit Operating System

8 GB of RAM

Question 18

How do you start machine learning with Python certification step-by-step?

Accepted Answer

Our instructors for the Machine Learning with Python course at upGrad KnowledgeHut have meticulously created a curriculum that will help familiarize you with the AI tool. Understand the theoretical part as well as get hands-on experience in one of the best machine learning with Python certification courses.

Question 19

What projects will I get to work on during ML with Python online training?

Accepted Answer

Our introduction to one of the best Machine Learning with Python certification online courses will give you an opportunity to work on a live, real-world capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.

Question 20

What are the most fundamental steps required to complete a machine learning with Python project?

Accepted Answer

To execute a successful advanced machine learning with Python project for one of the best machine learning with Python course online programs at upGrad KnowledgeHut, you need to complete the following steps:   Proper definition and structuring of the problem   Preparing the data   Evaluation of algorithms available in order to solve the problem.   Improvisation of the results obtained by the application of the chosen algorithm Presentation of the results obtained

Question 21

Why is Python Programming Language necessary to learn Machine Learning?

Accepted Answer

Although Python is not a necessity for machine learning, it will certainly make your life easier when dealing with Machine Learning concepts and their applications. Python for Machine Learning has several advantages. Python’s simple language syntax simplifies how you validate data and streamlines your scraping, processing, refining, cleaning, arranging, and analyzing processes. This assists in minimizing conflicts among programmers and improves collaborative efforts. At upGrad KnowledgeHut, our machine learning with Python certificate online course enables you to work on ML with Python.

Question 22

What are the best Python libraries essential for applied machine learning in Python?

Accepted Answer

Due to its vast open-source community, there are tons of libraries for you to play with. Nonetheless, depending on their ease of implementation, performance, the open-source community and so on, our certificate in machine learning with Python course has compiled a list of Python libraries that are best for machine learning.

scikit-learn: Used majorly for data mining, data analysis and data science as well

SciPy: Contains packages for Mathematics, engineering, and science (manipulation)

NumPy: Provides much more than just efficiency - free and fast vector and matrix operations

Keras: When one thinks Neural network, one goes to get the help of Keras

TensorFlow: Uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks

pandas: Provides high-level data structures. Significantly helpful, while during data extraction and preparation

Matplotlib: It helps in the visualization of data by plotting graphs in 2D

PyTorch: If NLP is our aim, PyTorch is our go-to library

Will I get any certification on completion of Machine Learning with Python course? (SEO updated)

After adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate machine learning with Python from upGrad KnowledgeHut.

In addition, you will get to showcase your advanced Machine Learning with Python skills by working on live projects, thus adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

Question 23

What will I learn after this machine learning with Python certification?

Accepted Answer

All machine learning with Python certification programs include the fundamentals of machine learning and Python that will help you accelerate your career as a data science practitioner. At the end of this machine learning with Python training, students will be able to:   Possess a good understanding of the fundamental challenges and issues facing Machine Learning studies and the application of Machine Learning. Some of these challenges include quality of data, model complexity, model selection etc.   Appreciate the underlying mathematical relationships across and within Machine Learning algorithms as well as the paradigms of unsupervised as well as supervised learning Will be able to design, implement and analyse the various Machine Learning algorithms in a range of real world applications, models and projects Possess an understanding of the strengths and weaknesses of several applications of Machine Learning projects Be able to understand, analyse and implement Machine Learning algorithms Be able to apply the theoretical Machine Learning concepts to solve real life problems

Question 24

What if I have more questions about the training?

Accepted Answer

Should you have any more questions about machine learning with Python certified course, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

Question 25

What are the modes of training offered for Machine Learning with Python certification course?

Accepted Answer

Undergo our Machine Learning with Python complete course at upGrad KnowledgeHut which is delivered via live, instructor-led training sessions. 

You can listen, learn, ask relevant questions and get all your doubts clarified from your instructors, who are experienced Data Science and Machine Learning industry experts. Their insights will assist you in mitigating challenges as a DevOps professional in the real world.

Question 26

Who are the instructors?

Accepted Answer

Most Machine Learning with Python courses online with certificates are delivered by leading practitioners who bring trending insights, best practices, and case studies from their experience to the training sessions. At upGrad KnowledgeHut, we add a bit of extra punch. Our instructors are industry-recognized experts with over 10 years of experience in Machine Learning.

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Question 27

Where will the workshops be held?

Accepted Answer

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet connection, from anywhere across the world, can access the course and benefit from it. Just an update, we do not provide any machine learning with Python professional certificate. However, we provide a completion certificate that you can share on social media.

Question 28

When will the classes be held?

Accepted Answer

Schedules for the upcoming workshops in our Machine Learning with Python full course at upGrad KnowledgeHut can be found on Schedules/Tuition page.

Question 29

What platform do you use for video conferencing?

Accepted Answer

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software to join the best course for machine learning with Python at upGrad KnowledgeHut.

Question 30

How do I join the class?

Accepted Answer

You will receive a registration link from us to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey at upGrad KnowledgeHut’s best online course for machine learning with Python.

Question 31

Will I have other participants in the class?

Accepted Answer

Yes, there are other participants who actively participate in the live, instructor-led training sessions in our machine learning with Python free online course at upGrad KnowledgeHut. They remotely attend online training from their office space or comfort of their home, or any place of their choosing. We understand that our learners come from various backgrounds, and most are busy professionals. So, we offer learning flexibility in whichever way possible.

Question 32

How does the 24/7 support work?

Accepted Answer

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger of your machine learning with Python full course.

Question 33

What if I miss a class?

Accepted Answer

If you miss a class, you can access the class recordings from your machine learning with Python programming certification course at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Question 34

Does it take 2–3 hours to set up Python and Libraries on your laptops when you are starting Machine Learning projects with Python?

Accepted Answer

No, it does not take 2 to 3 hours to set up Python and its libraries on the laptop when you start your online machine learning with Python course at upGrad KnowledgeHut. Depending upon the space available on your disk as well as the speed and version of your operating system, the installation of Python and its libraries on your laptop should not take more than 15 minutes.

Question 35

Can I switch the start date to my Machine Learning with Python training?

Accepted Answer

Yes, you can switch the start date to your online Machine Learning with Python training. However, you need to give notice at least 24 hours before. We have several learners undergoing this training, so subject to availability of seats we can book a seat for you in the desired batch.

Question 36

Why learn Machine Learning? What are the top reasons to learn Machine learning?

Accepted Answer

Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness the immense amounts of data generated unknowingly every day and put it to use for key decisions. Big and small data is reshaping technology and business as we know it and will continue to do so (at least soon).

In the state of Machine Learning in companies and your daily life machine learning is no more just a mere niche of the tech world but is a new field of work and research altogether. Tech experts have been increasingly making use of Machine Learning over the years. Surge pricing at Uber, Walmart product recommendations, social media feeds displayed by both Facebook and Instagram, Google Maps, detecting financial fraud at financial institutions, etc. - all these and many more functionalities are now being performed with the help of powerful Machine Learning algorithms, increasingly without human interference. Every individual is making use of one or the other product of Machine Learning, whether he knows it or not. In such a scenario, learning about Machine Learning is an inevitable step that any professional, especially someone involved in the field of Information Technology and Data Science, must take in order to not become irrelevant.

Some of the benefits of learning Machine Learning include the following:

It reels in better job opportunities: According to a report published by Tractica, services driven by Artificial Intelligence were worth $1.9 billion in the year 2016 and this number is expected to rise to the neighborhood of around $19.9 billion by the end of the year 2025. Machine Learning is the bandwagon that every corporation in the world is hitching its wagon to. With each industry in the world looking at expansion in the domain of Machine Learning and Artificial Intelligence, knowledge of the same is bound to attract more and brighter career opportunities in the present scenario and in the future as well.

Machine Learning engineers earn a pretty penny: The valuation of a Machine Learning expert can be compared to that of a top NFL quarterback prospect. According to a study published by Glassdoor, the average income of a machine learning engineer is pegged at $97,000 to $100,000 per year.

Demand for Machine Learning skills is only increasing: There exists a huge gap between the demand and the availability of Machine Learning engineers. This skill gap is increasingly being lamented by the Chief Information Officers (CIOs) of some of the biggest corporations around the world. What this means, essentially, is that the demand as well as the pay for professionals with Machine Learning skills is only going to increase in the future.

Most of the industries are shifting to Machine Learning: Most industries in operation around the world are dealing with a humongous amount of data that is only increasing every single day. The benefits reaped by a thorough analysis of this data is a fact that companies are fast taking cognizance of. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors.

At such a time, all industries from the financial services sector to government agencies, the healthcare industry and oil and gas mega-corporations to the transportation sector - every industry is a ripe field for work in the field of Machine learning. Learn about ML in our machine learning with Python crash course at upGrad KnowledgeHut to stay ahead in the game.

Question 37

Is machine learning in demand?

Accepted Answer

Per LinkedIn, machine learning job postings have been increasing by over 300% in the past 6 years. Furthermore, the World Economic Forum claims that the demand for AI and ML experts is projected to grow by 40%, i.e., 1 million jobs, soon. Our machine learning with Python course content at upGrad KnowledgeHut is up-to-date to help you stay ahead of your peers and be a job-ready and skilled professional.

Question 38

Why is Machine Learning important in our society?

Accepted Answer

The concept of Machine Learning deals with computers and systems taking in a huge amount of data, analyzing it and solving problems through training on that data in order to obtain the best possible outcome for a task or problem. It is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as why a particular approach to a problem actually works.

It's easy and it works: Machines are able to work faster than human brains do and as such, are able to solve problems faster than we ever can. If, for example, there exist a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.

Being used in a wide range of applications today: Machine Learning has several practical applications in real life. It is the very solution that the world was looking for a variety of problems. Machine learning drives businesses in that it helps them save time and money as well as effort. It allows people to get more things done in a more efficient, effective and appropriate manner. Every industry, starting from health care, nursing, transport, and customer service to government and financial institutions is benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.

At upGrad KnowledgeHut, our machine learning with Python course syllabus keeps these things in mind. So, when you complete the course successfully, you become a skilled ML professional.

Question 39

Can I get a job with this ML Python certification?

Accepted Answer

Our course can assist you in gaining the skills needed for an ML professional. The instructors will share their industry experience and insights, clear your doubts, and provide feedback. Once you complete the course successfully, you will be more equipped to secure a job than someone who is trying to learn ML with Python from machine learning with Python free online courses.

Question 40

Where are Machine Learning and Deep Learning techniques being applied right now?

Accepted Answer

As both machine learning, as well as deep learning are part of the Artificial intelligence domain, their main application is the replacement of decision-making by humans. Some of the popular applications of machine learning and deep learning are:

Medical Diagnosis: Cancer cell detection, gene printing, and more.

Information Retrieval: Search engines including both text as well as image search

Natural Language Processing: Recommendation systems, photo tagging, and sentiment analysis

Finance: Stock predictions, trading, financial banking decisions

Computer Vision: Real-time facial recognition, augmented reality, self-driving cars

Customer Experience: Chatbots

Languages: Translations of text as well as images, language recognition of different dialects

If you want to stay updated on the latest ML requirements, upGrad KnowledgeHut offers one of the best machine learning with Python certification courses to fulfill your learning needs.

Question 41

Do you need a PhD to learn Machine Learning with Python?

Accepted Answer

You do not need a PhD in order to get an online machine learning with Python certificate from our Machine Learning with Python course at upGrad KnowledgeHut. There does not arise a need for you to possess a PhD level understanding of the concepts and applications of Machine Learning, in order for you to take up this course. In the same way that a theoretical Computer Science programmer does not require a background of education in the field of Computer Science, you do not need a very deep and intimate understanding of the theoretical concepts of Machine Learning, in order for you to be able to gain a substantial amount of knowledge about the practical applications of the same.

Question 42

What is the salary of a fresher in machine learning?

Accepted Answer

An entry-level junior machine learning engineer’s salary can be within ₹4 LPA to ₹10 LPA in India. In the US, it can range from $97,000 to $100,000 for entry-level to experienced professionals, per Glassdoor. These are all average salaries and can vary based on region, job demands in the area where you are aspiring to work your experience, and applicable skills. Our Machine Learning with Python course at upGrad KnowledgeHut is one of the best machine learning with Python certification online courses that enables you to acquire those relevant skills so that you can be a job-ready professional.

Question 43

What is the average salary for Machine Learning Engineers with a machine learning with Python certification?

Accepted Answer

Before starting a new job, it is important to know and understand the pay scale a particular career choice offers to you, and how easy (or difficult) it is to obtain a higher salary. In the area of Machine Learning, there are job opportunities to earn quite a generous salary. However, like most other jobs, the more experience you have, the higher your salary. Per Glassdoor, the range of salary in the field of Machine Learning can be pegged from ₹4 LPA to ₹10 LPA in India for an amateur Machine Learning engineer who is just starting, to ₹15.3 LPA for an experienced and expert Machine Learning engineer. In the US, it can range from $97,000 to $100,000 for ML professionals. Once you undergo a machine learning with Python certification course, employers will get a better validation of your capabilities.

Question 44

What is the future of ML?

Accepted Answer

Per MarketsandMarkets, the artificial intelligence (AI) market is estimated to grow up to USD 1339.1 billion by 2030. The CAGR is 35.7% in this period. Governments and various industries, including healthcare, finance, manufacturing, and retail, will rely on AI and heavily invest in digital transformation and usage of the technology. ML professionals with relevant skills will be in high demand. Our machine learning with Python professional certificate course at upGrad KnowledgeHut can help you acquire or hone these skills to equip you for the future.

Question 45

Can I cancel my enrolment? Do I get a refund?

Accepted Answer

At upGrad KnowledgeHut, we strive diligently to make sure that your learning experience with us is second to none and you are assured of the highest standards of quality. However, if for any reason your expectations are not met, we will process refunds in accordance with our Cancellation, Refund, and Deferment Policy.

Question 46

Are scholarships available for the Machine Learning with Python training?

Accepted Answer

To avail of scholarships for one of our top machine learning with Python courses online with certificate, please get in touch with us at support@knowledgehut.com or check with your Learning Advisor. The team shall send the forms and instructions to you. Based upon the responses and answers that we receive, our panel of experts take a decision on the Grant. The entire process could take about 7 to 15 days.

Question 47

Are group discounts available?

Accepted Answer

Yes, group discounts can be available in our machine learning with Python course fees and apply to groups as small as three (3) participants. The more participants that attend a training course, the greater the discount. By registering in groups, you can typically save up 20% to 30% on the course fee. For more details, please check out upcoming schedules.

Question 48

Can I pay the course fees in instalments?

Accepted Answer

Yes, instalment options are available for payment of machine learning with Python course price at upGrad KnowledgeHut. To avail of the instalment option, please get in touch with us at support@knowledgehut.com. The team will explain how the instalments work and provide timelines for your case.

Typically, the number of instalments varies from 2 to 3, but the full amount must be paid before you complete the course.

Question 49

What are Supervised Learning, Unsupervised Learning and Reinforcement Learning Algorithms?

Accepted Answer

Supervised machine learning algorithms: These are the algorithms that take the learnings from the past data and apply them to the new data made available by making use of labelled examples in order to predict events that will occur in the future.  Unsupervised machine learning algorithms: These are the type of algorithms that are made use of when the information required for the training of the system and the algorithm is either not labeled or has not been classified.  Reinforcement learning: Reinforcement learning is an area of Machine Learning that deals with the taking of suitable steps in order to maximize the outcomes or rewards in particular situations. Reinforcement learning differs from supervised learning in that in Supervised learning, the training data possesses the answer key within it so that the model being trained is trained with the correct answer itself. If you want to have an in-depth understanding of such ML basics, our machine learning with Python complete course at upGrad KnowledgeHut can assist you with it.

Question 50

What are the top different types of machine learning algorithms in Python? Describe a few examples of ML Algorithms?

Accepted Answer

To become an ML professional, you need to enroll in the best machine learning with Python course online to learn about the various ML algorithms. upGrad KnowledgeHut offers a Machine Learning with Python course that builds up the much-needed ML foundation. However, here’s a summary for your quick understanding. Machine Learning Algorithms can be classified basically into the following 3 types -  Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbours  Unsupervised Learning: Apriori, K-Means, Principal Component Analysis (PCA)  Ensemble Learning: Bagging, Boosting Supervised Learning: Using categorically classified historical data to learn the mapping function from the input variables (X) to the output variable (Y). Examples of such include - Linear Regression - The relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx Logistic Regression - Logistic Regression is just like the linear regression model; the only difference is the outcome of the regression is probabilistic, rather than exact values. A Transformation function is then applied to force this probability into a binary classification.  CART - Classification and Regression Trees (CART) is an implementation of Decision Trees. This algorithm charts the possibility of each outcome and predicts the result based on defined nodes and branches. At each non-terminal node is a single input variable (x). The splitting point on that node depicts the various outcomes that can happen to that variable, and the following leaf node represents the output variable (y). Naïve Bayes - This algorithm predicts the possibility of an outcome happening, given the basic value of some other variable. It works exactly on the principle of the Bayes theorem and is considered “naive” as it makes the assumption that all variables are independent in nature.  K-Nearest Neighbours - This algorithm charts the entire data set given, and after assigning a pre-defined value of “k” to find out the outcome for a given value of the variable, it collects “k nearest instances” of the value in the dataset and then either averages them to produce the output (for a regression model) or finds the mode of these averages (for most frequent class problem).  Unsupervised Learning: In these types of problems, only the input variables are given and not the output ones. Thus, the underlying structure of the given data sets is analysed to reveal possible associations and clusters. Examples of such algorithms include the following - Apriori - This algorithm is used in various databases containing transactions to identify frequent associations or instances of two items occurring together, then such associations are used to predict further relationships. K-Means - This algorithm groups similar data into clusters, and then associates each data point in the cluster to an “assumed” centroid of the cluster. By performing iterations of the steps to ensure the distance between the data point and the centroid is the closest, the real centroid is arrived for each cluster. PCA - Principal Component Analysis (PCA) makes the data space easier to visualize, by reducing the number of variables. It does this by mapping the maximum variances of each point onto a new coordinate system, with axes corresponding to the principal components chosen. The basic principle of orthogonality ensures that each pair of components is unrelated to each other. Ensemble Learning: Groups or ensembles of learners are more likely to perform better than singular learners. By building on this premise, these types of algorithms combine the results of each learner and then analyse them as a whole to obtain a fairly accurate representation of the actual outcome. Examples of such algorithms include the following - Bagging - This algorithm is used to generate multiple datasets (based on the original one), and then model the same algorithm on each to produce different outputs, which can then be compiled and performed to obtain the real outcome. Boosting - This algorithm is similar to the above one, but it works sequentially instead of the parallel nature of bagging. Thus, each new dataset is created by learning from the previous one’s errors and miscalculations.

Question 51

What are the top essential Python libraries used to implement Machine Learning with Python?

Accepted Answer

Thanks to the large and diverse open-source community of Python and its libraries, there are some very useful libraries as below:

scikit-learn: Primarily for data mining, data analysis and in data science as well.

NumPy: Useful due to its high performance when dealing with N-dimensional arrays.

Pandas: Useful for high-level data structures and incredibly useful for data extraction and preparation.

Matplotlib: In almost every ML problem, we need to plot a graph for better data representation, Matplotlib helps in the plotting of graph in 2D.

TensorFlow: If you are using deep learning in your project then this library, created by Google, is the go-to library as it uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.

However, you need a valid certificate in machine learning with Python to get things rolling on the job front. Once you successfully complete our Machine Learning with Python course at upGrad KnowledgeHut, you will get a completion certificate to get noticed in the competitive market.

Question 52

What is Scikit-learn Python?

Accepted Answer

SciKit is short for SciPy Toolkit. SciKits are add-on packages for SciPy which are too specialized to be kept in SciPy itself. They are developed and hosted independently from the main SciPy package. One of the most famous SciKit is a scikit-learn package. Below are some of its highlights:

It contains efficient and easy-to-use tools for data analysis and data mining.

As it has a BSD license, you can use it in your own project as well as for commercial purposes too.

It is built upon existing Python libraries such as NumPy, SciPy, and Matplotlib.

These are foundational parts of Machine Learning that you should get acquainted with through proper guidance. Our instructors from machine learning with Python certification programs will help you in getting such basics strong.

Question 53

What are the top free must-read Machine Learning e-books for beginners?

Accepted Answer

Whether you're a beginner or advanced, undergoing a machine learning with Python certified course from a reputed knowledge provider like upGrad KnowledgeHut is a good way to acquire industry-relevant ML skills and knowledge. If you want to get some additional insights, below is a list of free eBooks to help you learn Machine Learning -

Machine Learning Yearning, by Andrew Ng

Programming Collective Intelligence

Machine Learning for Hackers, by Drew Conway and John Myles White

Machine Learning by Tom M Mitchell

Machine Learning Yearning, by Andrew Ng: This book, penned by Andrew Ng, is incomplete yet and is a work in progress. However, several chapters of the book have been released and can be downloaded for free over the internet in the form of an eBook. Machine Learning Yearning is a book that will aid the reader in getting up to speed with the concepts of building Artificial Intelligence Systems. The book also teaches the reader to tune his/her mind to the various decisions that are required to be taken while building a Machine Learning project.

Programming Collective Intelligence: This is one of the best books to start with while embarking upon your journey of Machine Learning. There is not one data scientist who is involved in the field without having read this book first. The book makes use of Python to deliver the concepts of Machine Learning, thereby making it a double whammy in your learning journey.

Machine Learning for Hackers, by Drew Conway and John Myles White: This is a book that is best recommended for learners who wish to develop an understanding of the basic functioning as well as the importance of Machine Learning Algorithms.

Machine Learning by Tom M Mitchell: This book, penned by Tom M Mitchell, is a great book to start your Machine Learning journey with. It gives you a nice overview of the world of Machine Learning as well as the various vital Machine Learning algorithms and theorems. One specialty of this book is that it also provides you with pseudocode summaries of the algorithms that it wishes to teach the learner. The book also contains several case studies as well as well-planned examples that help the reader develop a basic understanding of the concepts of Machine Learning easily.

Question 54

How much does Python certification cost?

Accepted Answer

The cost of a Machine Learning with Python complete course varies based on region, country, or area from which you are attending it. upGrad KnowledgeHut also offers seasonal discounts from time to time. You can go to our Tuition/Schedules page to check our current machine learning with Python course fees.

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7. Ensemble Techniques 