Machine Learning Course

Advanced Course to Learn Machine Learning

Landscape of Machine Learning

  • 2.3 Million Machine Learning Jobs will be created by 2020
  • Average Salary Base for Machine Learning Jobs is $146,085
  • 10,000+ Monthly Machine Learning Job Openings
  • Initial Salary 5,00,000 – 12,00,000 INR Per Annum
  • Machine Learning Engineers Rule The Top 10 AI Jobs List
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13

In-Depth Advanced Modules 

4

Hands-on Projects

1-1

Career Mentoring 

39+

Hours of Live Classes

50+

Placement Partners

l

13+

Assignments

Exclusive Offers!

100%

Certification Validity 

100%

Job Assistance

Know more about our Machine Learning Course

Why Should You Take this Machine Learning Course?

Machine Learning is one of the hottest career choices today. It is one of the fastest-growing tech employment areas with jobs created far outnumbering the talent pool available.

According to Gartner, 2.3 million Machine Learning Jobs will be generated by 2020. Indeed job trends report also reveals that in terms of most in-demand, AI jobs, Machine Learning Engineer tops the chart with 29.10% increase in job postings.

Today, every industry is going gaga after Artificial Intelligence. This makes it ideal to take up a Machine Learning Course.

By bringing better career opportunities, Online Machine Learning Courses have become the shining star of the moment. 

Who is this Course For?

– People with knowledge of Python Programming
– Candidates with an understanding of Statistics, Algebra & Calculus

Our Machine Learning Online Course Enrollments Map

  • Students 40% 40%
  • IT Professionals 60% 60%

13 Modules

Machine Learning Online Course Curriculum Details

  • Graphically Displaying Single Variable
  • Measures of Location
  • Measures of Spread
  • Displaying relationship – Bivariate Data
  • Scatterplot
  • Measures of association of two or more variables
  • Covariance and Correlation
  • Probability
  • Joint Probability and independent events
  • Conditional probability
  • Bayes’ Theorem
  • Prior, Likelihood and Posterior
  • Discrete Random Variable
  • Probability Distribution of Discrete Random Variable
  • Binomial Distribution
  • Continuous Random Variables
  • Probability Distribution Function
  • Uniform Distribution
  • Normal Distribution
  • Point Estimation
  • Interval Estimation
  • Hypothesis Testing
  • Testing a one-sided Hypothesis
  • Testing a two-sided Hypothesis
  • Applications of Machine Learning
  • Supervised vs Unsupervised Learning
  • Python libraries suitable for Machine Learning
  • Regression – Features and Labels
  • Regression – Training and Testing
  • Regression – Forecasting and Predicting
  • Regression – Theory and how it works
  • Regression – How to program the Best Fit Slope
  • Regression – How to program the Best Fit Line
  • Regression – R Squared and Coefficient of Determination Theory
  • Model evaluation methods
  • Classification Intro
  • Applying K Nearest Neighbors to Data
  • Euclidean Distance theory
  • Decision Trees
  • Regression Trees
  • Random Forests
  • Boosting Algorithm
  • Principal Component Analysis
  • Linear Discriminant Analysis
  • Vector Basics
  • Support Vector Machine Fundamentals
  • Constraint Optimization with Support Vector Machine
  • Beginning SVM from Scratch in Python
  • Support Vector Machine Optimization in Python
  • Visualization and Predicting with our Custom SVM
  • Kernels Introduction
  • Soft Margin Support Vector Machine
  • Handling Non-Numerical Data for Machine Learning
  • K-Means with Titanic Dataset
  • K-Means from Scratch in Python
  • Finishing K-Means from Scratch in Python
  • Hierarchical Clustering with Mean Shift Introduction
  • Introduction Naive Bayes Classifier
  • Naive Bayes Classifier with Scikit
  • Introduction into Text Classification using Naive Bayes
  • Python Implementation of Text Classification
  • Content-based recommender systems
  • Collaborative Filtering
  • Text Preprocessing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization
  • Text to Features (Feature Engineering on text data)
  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Phrase Detection
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • Statistical features
  • TF – IDF
  • Frequency / Density Features
  • Readability Features
  • Word Embeddings
  • Important tasks of NLP
  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching
  • Important NLP libraries
Learn more about our Machine Learning Course Curriculum

15+ Hrs of Hands-on Assignments

Hands-on Machine Learning Course Assignments 

Well researched assignments have the potential to take the participants on an exciting journey to execute their learnings. That’s our mantra at Digital Vidya.

Each assignment of Digital Vidya’s Machine Learning Course is designed with a focus to provide the best practical experience. Our module assignments to learn Machine Learning focus on enhancing the confidence of our participants.

Our Assignments are close to the actual occurrences in the industry out there. These assignments will be a propeller to helping you learn Machine Learning practically. 

Statistics: Probability, Hypothesis Testing
Multiple Linear Regression & Quadratic Regression Analysis
Introduction to Trees, Decision Trees, Ensemble Learning (Random Forest)
Classification Introduction, Logistics Regression & Text Analysis Using Classification Algorithms
Unsupervised Learning, Unsupervised Learning Techniques-K Means Clustering, Hierarchical Clustering
Bias-Variance Trade-off, Model Evaluation Techniques
Logistic Regression Model Tuning
Know the complete offering of our Machine Learning Course

Capstone Projects on Offer

Best in Class Capstone Projects to Learn Machine Learning

To learn Machine learning in the best possible and hands-on method, Digital Vidya’s Machine Learning Course comes with best in class capstone Projects. At the end of each batch, we hold a Capstone Project competition that is open for our students. Successful participants win prizes and recommendations from their lead trainers.

Natural Language Processing

Duration: 3 Weeks

Project Description:

This is one of the most applied areas for AI, Data Science, and Machine Learning across domains and industries. The real world is filled with mostly messy text data, and handling text is an important step towards making smarter algorithms. Using IMDB dataset from the movie domain, the learner will apply the most common concepts of NLP.

Key Takeaway:

This project will empower the learners to build intermediate skills in the natural language processing domain. A few of the fundamentals of working with textual data covered in this project are:

  1. Remove stop words
  2. Apply Stemming and Lemmatization
  3. Create a cluster of words
  4. Build a sentiment analysis model and a clustering model

Healthcare Analysis

Duration: 3 Weeks

Project Description:

Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. For this project, we will use the large EEG database at UCI Machine learning repository. This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. One fascinating question is whether the patterns are different for an alcoholic and regular subject?

Key Takeaway:

This capstone project focuses on EEG data analysis, giving an opportunity for students to learn through complexities in dealing with such complex real-world data. The project contains the following exercises:

  1. Parse and store in an easily understandable and readable form
  2. Exploratory data analysis to better understand the data
  3. Using Statistical concepts like Hypothetical testing
  4. Identify features to predict whether a subject is alcoholic or not
  5. Use machine learning algorithms to develop a suitable classifier

Bank Marketing

Duration: 3 Weeks

Project Description:

The banking industry is working in a very competitive environment and needs to strategize to grow its business.  This project is related to the marketing campaigns related to term deposits, making an interesting multi-disciplinary work that mixes both the finance and the marketing domain.

Key Takeaway:

The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data science process. The data is a real-world data with unclean and null values.

The objective is to:

  1. Build the model to predict if a customer will subscribe
  2. Identify influential factors to form marketing strategies
  3. Improve long-term relationship with the clients

Deep Learning Based Project

Duration: 3 Weeks | Price: ₹5000 (Including Tax)

Project Description:

E-Commerce has experienced considerable growth since the dawn of the internet as a commercial enterprise. Deep Learning excels at identifying patterns in unstructured data and can predict the class of an uploaded image applied on eCommerce context. This project is an attempt to replicate virtual store assistance through image recognition over an eCommerce Fashion MNIST dataset.

Key Takeaway:

This project focuses on the implementation of Neural Networks to solve complex unstructured data problems. The objective is to:

  1. Build the model to classify the various categories (analytic vertical) of clothing/fashion related images.
  2. Understanding the implementation of deep learning concepts through Tensorflow and Keras.
  3. Model optimization by tuning hyper-parameters and implementing dropout layers.
Get more details about the Capstone Projects of this Machine Learning Course

Machine Learning Course Schedule

Online Live Machine Learning Classes

39+ Hours of in-depth live sessions

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Duration of Our Machine Learning Training

13 Weeks

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Machine Learning Course Assignments

25+ Hours

Prerequisites

Python Programming, Statistics, Calculus, & Algebra

 

Upcoming batches of Python Machine Learning Online Training

 

 

23rd Feb 2020 (Sunday)

10 AM -1:30 PM (IST)

Know the complete offering of our Machine Learning Course

2 Tools

Machine Learning Tools You’ll Learn

You will master Python and Jupyter Notebooks by the end of this course.

Language: Python

Python is becoming the first choice for Data Scientists. The learners will be learning to use all the relevant libraries, NumPy, Pandas, scikit-learn, Matplotlib.

Tool: Jupyter Notebook

An open-source web application that contains live code, visualizations and narrative text. Learners will be using this for all their data science work.

Machine Learning Training Schedule

Why Learn Machine Learning Online?

Digital Vidya has a legacy of 10+ years in which we have trained 38,000+ Professionals from 55+ Countries of the World. 

We counsel 100s of candidates every day : )

Global Trainers

Learn directly from expert Machine Learning Trainers recognised for their expertise Globally.

Attend from Anywhere

Enjoy the power of mobility and learn while you are on the move. 

No Need to Travel

Save time that would otherwise be wasted in commuting. 

Interactive & Practical

15+ hours of assignments ensure that you learn hands-on.

No Time Constraints

Say bye-bye to time constraints. Revise at your own pace. 

Lifetime Updates

Get access to revised content for your entire life. 

Industry Expert as Your Machine Learning Trainer

Top Machine Learning Trainers Known Across the Industry

Get trained by world-renowned Machine Learning Trainers! All our Online Machine Learning Course trainers have 10+ years of industry experience. Btw, in case you don’t like the training, you can opt out within 3 days and request a full refund.

Get Complete Details of Digital Vidya’s Machine Learning Course

Industry Expert as Your Machine Learning Course Advisor

Our Machine Learning Course Advisers

Get Complete Details of Digital Vidya’s Machine Learning Course

50+ Placement Partners

100% Interview Guarantee Offer

Digital Vidya offers a 100% Interview Guarantee for its Online Machine Learning Course.

We have a dedicated placement cell, which works closely with our participants for their placement needs. Here is a snapshot of our placement process.

Resume Creation

On successful completion of the course, which includes submission of assignments & attaining necessary certifications, we work with the candidates to create an effective resume.

01

Job Application

The updated resume is shared with relevant organisations and agencies including our partners. On shortlisting, we help the candidate to pass the initial round of discussion.

02

Interview Readiness

Based on the organizations needs & candidates ability, we train them to maneuver themselves to crack the interview. This stage helps the candidate to be 100% ready.

03

Selection & Joining

After a successful interview, we guide the candidate from accepting the offer to joining the organization for a successful career. We help him to stand out at his workplace.

04

For further information on Placement Support

Qualifications for our Interview Guarantee Offer

– BE/B.Tech Computer Science/IT/ MCA / MSc. IT / MA. Statistics / MA. Mathematics
– CGPA of 6.0 & above (50% & above in MCA / MSc IT)
– 60% of Marks in 10th and 12th Exams
– Immediately Available to Join the Organization
– Successful Completion of the Course along with Aptitude & Coding Tests

Note: Students from Non-Metro/Non-IT cities need to be self-located in the hiring locations during the placement process.

2 Certifications

Machine Learning Certifications

Digital Vidya's Machine Learning Certification

How to get this Certificate?

On successful completion of all assignments and the project, the participant will get a Machine Learning Certificate issued by Digital Vidya. He has to have a minimum 80% attendance too.

Vskills Machine Learning Certification

How to get this Certificate?

On successful passing of the Vskills examination, the participant will get a Machine Learning Certificate issued by Vskills. (A nominal examination fee involved) 

For further information on Machine Learning Certifications

Our Machine Learning Course Student Reviews

It was a tremendous journey right from the beginning. A huge opportunity opened in front of us in the Data Science world.

Mohan Kumar

Senior Software Engineer

Great experience. Easy and organized learning, great approach.

Prerna Sathiyal

Student

Digital Vidya gave me a comprehensive knowledge of Data science within a very short period of time.

Rahul God

Founder COO

Case studies and projects improved my skills and gave me the confidence to call myself a data scientist.

Arvind S

Academician

This course is best for beginners and it will give you complete exposure of every field of Data Science and Machine Learning.

Anshul Singh

Student

It was a great experience. Got to learn many new things going on in the present industry.

Lipi Sahu

Student

Reviews of Expert Industry Leaders

Digital Vidya is doing a great job at bringing data analytics to the rest of the world!

Akshay Sehgal, General Manager

Digital Vidya is doing a great job of bringing people from diverse set of experiences to one platform for creating the best of Data Science skill pipeline.

Ambuj Kathuria, Head – Data & Analytics

Creating a talent pool in India with Practical hands-on experience in Analytics and Data Science is the need of the hour. Platforms like Digital Vidya are critical to filling this gap.

Ravi Vijayaraghavan, President and Head – Analytics and Decision Sciences

Machine Learning Course FAQs

Who can do a Machine Learning Course?

The Machine Learning Online Course is perfect for people who want to build their career in the Artificial Intelligence industry. We recommend this course to students BE/BTech/MCS/MCA, software professionals, IT professionals, Data Professionals and engineers.

What is the Salary of a Machine Learning Engineer?

Your salary will completely depend on your skills. Machine Learning jobs for freshers may vary between ₹ 699,807- 891,326. With a good knowledge of data analysis, algorithms and a few years of experience, you may expect a salary of ₹ 1,759,777 monthly or ₹ 9, 00,000 per annum.

What is Machine Learning exactly & how does it work? What are the major Algorithms in Machine Learning?

Machine Learning is a subset or application of Artificial Intelligence. It provides systems the ability to learn and improve from experience automatically, without being programmed explicitly.

Machine Learning can also be put forth as the science of getting computers to learn and act which is similar to humans. It works in 7 stages, namely, Gathering of data, Preparing data, Choosing a model, Training of data, Evaluation, Tuning of Hyperparameters and prediction. A typical Machine Learning process covers three stages, namely, Training, Testing and Validation of the Data.

The major machine learning algorithms used on a larger scale are Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, KNN(K- Nearest Neighbours), K-Means, Gradient Boosting, Dimensionality reduction algorithms, Random forest and SVM(Support Vector Machine).

How to learn Machine Learning (ML)?

The process of learning Machine Learning starts with the basics of Python. After this you should learn Machine Learning basics and inferential & descriptive statistics. Data exploration, cleaning, and preparation are the other important steps to learn next.
An effective way is to get registered into a good Machine Learning course. This will include learning from modules, practical sessions, and assessments. You need to study more in order to go ahead for Advanced Machine Learning. This will include Deep Learning, ensemble modeling, and Big Data.

What are the best ways to study Machine Learning (ML) recommended by Ben Hamner, Kaggle CTO?

Ben Hammer, Kaggle CTO, in a recent quora session on AI highlighted the best ways to study Machine Learning. Below is the summary of his recommendations
1.Pick a problem you are interested in
2.Make a quick, dirty, hacky end to end solution to your problem.
3.Evolve and improve your initial solution
4.Write up and share your solution
5.Repeat #1-4 across a diverse set of problems.
6.Seriously compete in a Kaggle competition(If you’ve not already done so)
7.Apply machine learning professionally in various projects.
8.Help teach others about machine learning.

Following the above 8 steps can surely help you learn machine learning with ease

What are the steps required for executing a successful Machine Learning project with Python (ML Project)?

One of the best ways considered to learn Machine Learning is by building as well as understanding small projects end-to-end by trying yourself. Some of the easy steps that are used in creating a successful Machine Learning project with Python are
· Make sure that you realize as well as describe the problem
· Examine as well as formulate the data
· Once you have examined & formulated, apply the algorithms
· It is very important for you to reduce the errors
· Now it’s time for you to view the result
If you can do all these things, then it is easy for you to execute a Machine Learning project successfully.

What are the major & minor differences between Deep Learning and Machine Learning (explained in simple way)?

Deep Learning is different from Machine Learning in a number of ways. One of the minor differences is that of data representation. Machine Learning algorithms depend on structured data while deep learning depends on a network of data.
A major difference between both the processes is that machine learning is a part of artificial intelligence while deep learning is a part of machine learning. The machine learning algorithms are capable of changing themselves without any intervention of humans. In comparison to this, there can be different levels of algorithms in case of deep learning and each of these levels has different interpretations of data.

Why is Python Programming Language necessary to learn Machine Learning?

Python is one of the most important programming languages that can help you work with Machine Learning algorithms very easily. Python is very essential because of its large number of libraries. Due to libraries like numpy and pandas, data manipulation can easily be completed that helps in bringing the data to a point where Machine Learning models can be developed easily. Python is also known for offering clear as well as brief code.Python is used by professionals world wide especially for Machine Learning. Therefore, Python is highly considered as one of the favoured languages for learning and teaching Machine Learning.

Is it necessary to intimately understand kernel methods for Machine Learning model?

Kernel method is used to efficiently transform data into a much higher dimension and that too at a comparatively lesser cost. Due to this feature, the Kernel method is not just used for SVM algorithm but also used in multiple computations that involve dot products.
Kernel methods are also used as algorithms in machine learning for analysis of patterns. This is used mainly in turning or converting a linear model into a non-linear model. Thus, with so much usage and convenience of the Kernel methods, it is always a good idea to understand it intimately for the machine learning model.

What are the essential Machine Learning algorithms for Beginners?

The interest of students and professionals towards Machine Learning has increased a lot over the past few years. There are so many candidates each year who look towards Machine learning as their career goal. But cracking through it is not that easy at the first go.
Machine learning is about data and includes different types of algorithms such as Supervised learning, unsupervised learning, and reinforcement learning.
If you are a beginner, there are a few essential machine learning algorithms that you should go for and some of those are:

  • Linear Regression
  • Logistic Regression
  • CART
  • Naïve Bayes
  • KNN
  • Apriori
  • K-means
  • PCA
  • Random Forests
  • AdaBoost
What are Supervised Learning, Unsupervised Learning and Reinforcement Learning Algorithms?

Supervised Machine Learning is the task to learn the input and output of data based on a particular example. It analyses the data and also helps in coming up with new examples further.
Unsupervised Machine Learning is an algorithm that is helpful in finding and analyzing hidden patterns in the input data. Therefore, it is almost the opposite of supervised machine learning. Cluster analysis is the most common method that is used for finding hidden patterns in data.
Reinforcement Machine learning algorithm is a method where a delayed reward is received by the agent for evaluating the previous action. The most common usage of the reinforcement machine learning algorithms is found in games such as Mario and Atari.

What are the best Machine Learning Algorithms in Python for beginners?

If you are a beginner and want to jump into the field of machine learning, then it is very important for you to know Python. It is a minimalistic language that comes with a full-featured library line.
Some of the best Machine Learning Algorithms in Python for beginners are stated below:
· Decision Trees
· Linear Regression
· Support-vector machine
· K means clustering algorithm
· Naïve Bayes classifier algorithm
· Random forests
· Artificial neural networks
· Apriori algorithm
· Logistic regression
Machine learning algorithms are extremely automated and self-modifying that endures enhancing over time with minimal human interventions.

How to decide & choose a right Machine Learning algorithm (ML) to use for a specific problem statement?

Deciding the correct Machine Learning Algorithm for solving a specific problem statement is not an easy task because it depends on a number of factors such as model training & availability of the training data.
Here are some ways to do choose the right Machine Learning algorithm:

  • Classify the problem: sort by the input, classify by the output
  • You need to understand the data: Analyse the data, process the data, transform the data
  • Pick the right available algorithms
  • Apply machine learning algorithms
  • Enhance hyperparameters by choosing any of the options among three like Bayesian optimization, grid search as well as random search.
How can I practically design & implement a Machine Learning Algorithm using Python?

The ways that are required for designing a Machine Learning Algorithm using Python include: Defining the problem, Preparing the data, Evaluating the algorithms, Improving the results as well as Presenting the results.
In order to implement a Machine Learning Algorithm using Python, you need to do following things: Choose the programming language, Select the right algorithm, Choose the problem, Do proper research of algorithm as well as a unit test.
Before implementing a Machine Learning Algorithm it is very important for you to have relevant dataset on which you can easily apply the algorithms for the testing phase. This can help you to further improve your algorithm.

What are the most Essential Topics of Machine Learning one should study to master?

Machine Learning has turned out to be the most famous topic nowadays and everyone is trying to get to know more about this. With the volume of information which is available about machine learning, it is important to know the most critical aspects of the information.
Here are some of the most important topics that you need to study for Machine Learning:
· Probability
– Statistics
· System Design
· Machine Learning algorithms as well as libraries
· Data Modelling as well as evaluation
· Programming Languages
Once you study and understand these topics clearly, you can easily master machine learning.

Do you need a PhD for this Machine Learning Course with Python?

There are a lot of debates over the topic of whether a person should have a PhD or not for learning machine learning with Python. Generally, PhD is not a necessity for a machine learning course. But again, if you wish to go for a machine learning course at a foundation level, getting a PhD becomes an important factor.
Having a PhD done before getting the machine learning course with Python will help you in working on various technical issues that are not conventional or some issues that are not yet industry-ready. Of course, to have a better career in machine learning, PhD is always helpful. Hence, you need a PhD for a Machine Learning course with Python.

What are the 6 Best Free Machine Learning Courses online (handpicked by our experts-2019)?

Machine Learning courses have lately seen higher demand across the world. The demand for online courses has also increased as many of the working professionals are taking up this course for a better career.
Though there are a number of options available, here are the 6 best free machine learning courses online:

  • Free Lessons: Machine Learning – Andrew Ng, Stanford University
  • Free Lessons: Introduction to Machine Learning – IIT Kharagpur
  • Free Lessons: Machine Learning with Python – Sentdex
  • Learn with Google AI

Making proper comparison and selecting the right one for training can provide you with the desirable career in machine learning.

How Do You Start Machine Learning in Python step-by-step

 To learn about Python, all you need to do is install Anaconda. This will solve all your queries related to the beginning of Python and will help you understand ML better. Once this is done, start with learning the foundation of Python Syntax.

For total beginners, here is a step-by-step guide:
1. To begin with, start reading “Automate the Boring Stuff with Python”. It will provide you the theoretical as well as practical knowledge you need.
2. Ensure your Math skills are great in order to understand Python through and through.
3. Explore Python Libraries: NumPy, Pandas, Matplotlib Scikit-Learn
4. Start investing your time and knowledge you have gained so far, in small and structured projects.for this, explore Dataquest, Python for Data Analysis, CS109.
5 .Create a portfolio after doing some structured projects.
6. Search for your dream job. Remember, errors in your project need to be corrected. For that, explore StackOverflow and Python Documentation

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

Python is the language of any developer who needs to add statistical techniques and data findings to projects. Python is useful for data scientists to develop web applications.The best thing about Python is the extensive list of libraries it has. Libraries help the developers to win over complex problems. They do not have to work upon the codes from the beginning. Here is a list of the Python libraries you can explore to gain a better insight into this industry.

Tensorflow, Theano, Keras, Scikit-Learn, PyTorch, NumPy, Pandas, Seaborn, SciPy, Matpoltlib,Statsmodels, XGBoost, LightGBM, CatBoost, ELI5, FastAi, Caffe, Gluon, Apache MXNet, NLTK, Gensim, Spacy, Bokeh, Plotly, Flask

What is Scikit-learn Python?

Scikit-learn is a free library for understanding Machine Learning using Python. It consists of algorithms like k-neighbors, vector machines, and random forest. The biggest advantage is that it supports NumPy, SciPy, numerical and scientific libraries. It provides multiple supervised and unsupervised learning algorithms. Before you use Scikit-learn, you will need to install the SciPy (Scientific Python); its foundation. It is not the best-fit library for loading, summarizing, and manipulating data. For modeling data, consider Scikit-learn your best option. This library provides the following groups of models:

Clustering, Cross-Validation, Datasets, Parameter Tuning, Feature Extraction, Manifold Learning, Feature, Selection, Supervised Models, Dimensionality Reduction, Ensemble Methods.

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

In computer programming, learning the skill that will lead you a long way is very important. Python is one such language that gives an edge to your career. But are you sure you are doing everything right in order to master this skill? While learning a language, ensure you update your knowledge about the latest libraries and tools that are continuously upgrading with time.

Here is a list of the top 6 tips to learn Python Programming as a beginner:
Tip 1 – Everyday Coding is important
Tip 2 -Maintain notes as you learn
Tip 3 -Install the interactive Python shell
Tip 4 -Give time to grasp the knowledge you have gained.
Tip 5 -Become a bug solver
Tip 6 -Learning happens when your environment has the same people around you

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

On some platforms, it is difficult to install Python for Machine Learning. From a list of packages that need to be installed post installation of Python, it can be a bit confusing task for first-time learners. Here you will learn how to install Python for Machine Learning using Anaconda. Below-mentioned is the gist of what all you need to do.

Download Anaconda (suitable according to your system)
Install Anaconda
Start Anaconda Navigator
Run and update Anaconda
Install Scikit – learn Python library
Update Scikit – learn library by applying the conda command
Download and Install Deep Learning Libraries
For downloading the Theano DL library: input; ‘conda install theano’ command
For downloading the TensorFlow DL library: input; ‘conda install -c conda-forge tensorflow’ command
For downloading the Keras DL library: input ‘pip install keras’ command

Why is Machine Learning important in our society?

Machine learning is a branch of Artificial Intelligence that minimizes human interference. Machine Learning saves time, money, and energy in the business world. It is allowing machines to work quickly and is helping companies in doing things as quickly as possible. It acts as a virtual assistant most of the time. Chatbots are one such example. ML helps a machine in learning human behavior and acts accordingly with dependency on a live agent. ML can be applied to the Internet of Things to achieve a maximum level of efficiency. Machine Learning, Deep Learning, Neural Networks, Python are all interlinked and together they are an integral part of AI.

What is the brief history of Machine Learning?

Machine Learning is a branch of Artificial Intelligence. It uses neural networks to help computer decode and code various algorithms to learn from the given data and information itself. The neural networks model was created in the year 1949 by Donald Hebb in the book called The Organization Behaviour.
In the year 1950, Alan Turing came up with the Turing Test to find whether a computer actually has any real intelligence. In order to pass this test, the computer had to act in a way that the human on the otherwise gets convinced that it is a human too. In 1952, Arthur Samuel came up with the first computer learning algorithm/program.
In 1957, Frank Rosenblatt came up with the design of the first-ever neural network for deep learning for computers. In 1967, the first algorithm was fabricated; “nearest neighbor”.

What is Machine Learning workflow & what are the 7 phases in ML Workflow?

.The following are the steps you need to understand to build the Machine Learning Project from the very start. Firstly, you will have to download and install the Python libraries. The workflow of Machine Learning can be segregated into three dimensions: Gathering relevant data and information
Data pre-processing
Researching the model that will be the best fit for this particular kind of data

The 7 phases in a ML workflow that create a foundation for Machine Learning and help in learning how it works in various industries are:

  • Gathering Data
  • Preparing the Gathered Data
  • .Choosing The Right Model
  • Training
  • Evaluation
  • Hyper Parameter Tuning
  • Prediction
How can I download and install SciPy scientific Library for Python to learn Machine Learning (ML)?

Here you will learn how to download and install SciPy Scientific Library for Python to learn Machine Learning using Anaconda. Below-mentioned is a step-by-step guide that you need to follow.

  • Download Anaconda (suitable according to your system)
  • Install Anaconda
  • Start Anaconda Navigator
  • Run and update Anaconda
  • Install Scikit – learn Python library
  • Update Scikit – learn library by applying the conda command
  • Download and Install Deep Learning Libraries
  • For downloading the Theano DL library: input; ‘conda install theano’ command
  • For downloading the TensorFlow DL library: input; ‘conda install -c conda-forge tensorflow’ command
  • For downloading the Keras DL library: input ‘pip install keras’ command
How to make sure your Python environment is installed successfully on windows/mac operating system?

Follow these steps to ensure your Python environment is installed successfully on Windows/Mac OS.
Steps For iOS:

  • Install Xcode
  • Open terminal
  • Install Homebrew
  • Install Python
  • Install Pip Install Packages (PIP)
  • Install Virtualenv
  • Install Git and make a Github account

Steps For Windows:
Firstly, download the Python interpreter; visit python.org.
At python.org find the download page for Windows
Click on Python Releases for Windows & download the latest Python setup
Scroll down and click on either one these mentioned below:
Windows x86-64 executable installer for 64-bit
Windows x86 executable installer for 32-bit
Run the installer by double-clicking on the downloaded file
Click on install now button

What are the best Python tools used for Machine Learning in 2020?

Python is a subset of Machine Learning. It helps in the proper functioning of ML by creating algorithms that any machine can use time and again. The best thing about Python is the extensive list of tools it has. These tools help the developers to solve complex problems. They do not have to work upon the codes from the beginning. Here is a list of the Python tools you can explore to gain a better insight into this industry.

Tensorflow
Theano
Keras
Scikit-Learn
PyTorch
NumPy
Pandas
Seaborn
SciPyELI5
FastAi
Caffe
Gluon
Apache MXNet
NLTK
Gensim
Matpoltlib
Statsmodels
XGBoost
LightGBM
CatBoost
Spacy
Bokeh
Plotly
Flask

How long is the Machine Learning Course?

This Machine Learning Course will take between 3-4 months to complete. The instructor-led sessions are of 50+ hrs. You will also have to work on assignments and case studies.

What job opportunities will I get after completing the Machine Learning Course?

After successful completion of the Machine Learning Course, you will get opportunities of being a Business Analyst, Product Analyst, Machine Learning Engineer or a Data Scientist.

Why Learn Machine Learning?

Machine learning is the application of artificial intelligence which allows the software applications to develop accurate results. Google often says the future of Machine Learning is going to be very promising. It is very important to learn Machine Learning because it will help in increasing your business efficiency. A large number of companies are hiring proficient engineers because Machine Learning is the brain behind business intelligence
Some of the top reasons to learn machine learning are:

  • With the help of Machine Learning, you can easily recommend products to your customer that they have previously viewed, purchased or added to the cart.
  • Machine Learning is very good at strengthening businesses fraud detection systems.
  • In today’s time, there are a lot of opportunities in Machine Learning jobs.
How do I get started in Machine Learning as an absolute beginner?

Machine Learning is one of the subsets of AI. If you are just a beginner then here is a simple way to start in Machine Learning.
The very first step you need to do is select the programming language.
· In the second step, you need to have basic maths knowledge about statistics, algebra, probability as well as calculus.
· Now you need to study about python libraries
· Learn about the Scikit-learn library.
· Do read about the Python Machine Learning
· Know about the cheat sheet properly.
Finally, you should go for an exam and get yourself certified

What are the key technical skill sets required to learn Machine Learning (ML) & become a Machine Learning Engineer?

A Machine Learning Engineer is mainly a programmer who can develop a system based on artificial intelligence. The system thus will be able to learn new things without any specific command input.
Artificial Intelligence is the goal of any machine learning engineer. There are some key technical skill sets required to learn machine learning and become a machine learning engineer. Some of the skills are:

  • Programming languages such as Python, C++, Java, and R
  • Statistics
  • Signal Processing Techniques
  • Applied Mathematics
  • Neural Network Architectures
  • Audio, Video, and Language Processing
  • Artificial Intelligence Training
  • Industry Knowledge
  • Effective Communication
  • Rapid Prototyping

Apart from this, it is important to stay updated with the new technological advancements in machine learning.

How to learn the top essential Machine Learning Algorithms?

Here are some of the best ways to learn essential Machine Learning Algorithms:

Linear Regression: It is very helpful in estimating real values that are based on a continuous variable.

Logistic Regression: This is mainly used for estimating the discrete values that are based on a given set of independent variables or variables.

Decision Tree: This algorithm is one of the supervised learning algorithms that are mainly utilized for classification problems.

Support Vector Machine: This is a classification method in which you can plot each data item as a point in n-dimensional space.

How to learn Machine Learning through self-study? What are your recommendations for self-study?

If you wish to learn Machine Learning through self-study then you can follow the below steps:
Step 1 – Prerequisites
For this, you need to build a foundation of statistics, programming as well as a bit of math.
Step 2 – Sponge Mode
After the first step is done, indulge yourself in the essential theory behind Machine Learning and its development over the years.
Step 3 – Targeted Practice
You need to use the Machine Learning packages in order to practice the nine essential topics.
Step 4 – Machine Learning Projects
It is very important for you to jump into exciting domains with small projects to begin with. Projects are helpful to get an actual insight into your practical knowledge of Machine Learning.

What is considered the best programming language for Machine Learning?

Listed below are a few programming languages that are considered as best for Machine Learning.

Python: It is highly recommended for Machine Learning because the syntaxes of this programming language are very clear and easy to learn.

R: R is considered as the workhouse for statistical analysis as well as the extension of machine learning.

Prolog: This programming language contains tree-based data structuring, efficient pattern matching and also auto backtracking that helps in contributing towards the flexible programming framework.

Java: It is one of the best choices when it comes to Machine Learning. Java helps in providing amazing benefits such as simple usage, package services and simplified work with vast projects.

Do I need to be a Python programmer to learn Machine Learning?

Yes, you need to be a Python programmer to learn Machine Learning. Machine Learning is all about playing with different models, data, validations, optimization of hyper-parameters, vectoring variables, and also visualizing the happenings that are taking place in algorithms. And Python Programming deals with data, models, variables, and other similar elements. This makes Python programming important to learn machine learning.
Python now has become one of the most preferred languages for learning Machine Learning. In comparison to other languages such as Java or C++, Python programming is much simpler. It may be a bit slow in comparison to other languages but has a great capacity in data handling.

Do I need to know algorithms to learn Machine Learning? Why is it hard to choose the right Machine Learning Algorithms?

Machine learning is a combination of science and art. So, you will find that Machine Learning does not work on a fixed approach. There are a number of factors such as understanding the data, categorizing the problem, and others that determines the algorithm of Machine Learning.
There are some of the CS algorithms that are important for the purpose of machine learning. But again, it depends on how much the algorithms will be in use and how much you wish to use it in the process. Hence, it is always an added advantage to have knowledge of algorithms for machine learning.

What are the top different types of Machine Learning Algorithms? Describe a few Machine Learning Algorithm Examples?

Linear regression, Logistic regression, Decision tree, Naive Bayes, SVM, KNN, K-Means and Random forest are the top machine learning algorithms.

Linear Regression: This is mainly used for estimating the real values that are based on the continuous variable. In this, you will be able to establish the perfect relationship between dependent and independent variables.

Logistic Regression: This is utilized for estimating the discrete values that are based on a given set of independent variable or variables.

Decision Tree: This is a type of learning algorithm that is mainly used for classifying the problems. It can easily work with continuous and categorical dependent variables.

Which is the simplest Machine Learning algorithm for beginners?

From the past few years, it has been seen that interest in learning machine learning has increased to a great extent. But being a beginner, it can be a little difficult for you to start with this. Here are some of the simple Machine Learning Algorithms for beginners.
· Logistic Regression
· Linear Regression
· Naïve byes algorithm
· Classification and regression trees
· K-nearest neighbours’ algorithm
· Apriori algorithm
· K-means
· Principal Component Analysis
· Bagging with Random Forests
· Boosting with AdaBoost
There are 5 complete supervised learning techniques, 3 unsupervised learning techniques as well as 2 ensemble techniques. You can study and practice these once you have fully understood the basics of machine learning algorithms.

How does a Random Forest Algorithm operate in Machine Learning?

Random forest is mainly a supervised learning algorithm which is used in classification, regression as well as other tasks. These tasks are functioned by building a host of decision trees at training time as well as outputting the class which is considered as the mode of classes or mean prediction. It finally chooses the best & right solution by means of voting.
In order to perform the forecast by using the trained random forest algorithm, it is important to pass through test features through the rules of each arbitrarily built tree. Once you do the above, you will be well versed with the operation of Random forest algorithm in machine learning.

Why Study Python Machine Learning Course from DigitalVidya?

Machine learning engineers are said to be among the top artificial intelligence job profiles across the world. It is estimated that as many as 2.3 million jobs will be available in the field of machine learning this year; hence, it is important to get a certification from the right place.
When you get enrolled in DigitalVidya with the Python Machine Learning Course, you will be able to get a number of facilities such as in-depth modules, hands-on projects, and assignments. This is supported well by one to one career mentoring, and more than 39 hours of live classes. Apart from the professional training procedure, they have more than 50 placement partners to offer a great recruitment session for the candidates.

What is the average salary for Machine Learning Engineers?

A good number of candidates are looking forward to having a career in machine learning not just for their interest but also for the salary package that is offered by the companies. Machine Learning Engineer is categorized as one of the most sophisticated profiles due to the work profile as well as the salary packages that the candidates draw from the companies.
On average, a machine learning engineer can have a salary package ranging from $73K to $166k. Now the figures can differ from one person to another depending upon a number of factors such as the location of the person, the experience, skillset etc.

What will I learn after this Machine Learning Course with Python?

With this course, you will get to learn about the basics of machine learning with the programming language Python.
Initially, an overview of the purpose of machine learning is provided along with the places where it is applied in the real world. After getting a basic knowledge of machine learning, the next stage is to learn about different topics such as:

  • Types of machine learning
  • Machine learning algorithms
  • Model evaluation, and others.

The theoretical knowledge supported by practical sessions and assignments will help you in adding up a new skill in the resume and getting certified in machine learning to approach towards a new career.

How to get started using Python for mastering Machine Learning 2019?

Artificial Intelligence and Machine Learning are everywhere nowadays. To understand these, one should know the very basic foundations of python because python forms an important aspect of both ML and AI. To get started using Python for mastering Machine Learning, follow the below steps

Step 1: Mastering Python Basics
Step 2: Understanding the python scientific computing environment
Step 3: Classification
Step 4: Regression
Step 5: Clustering
Step 6: More classification
Step 7: Ensemble methods

Once studied and practiced well enough, these 7 steps can majorly help you in clearing your basics and establishing strong fundamentals for mastering Machine Learning 2019.

What are the best free online educational e-books to learn Python Machine Learning the easy-way?

Python is a language and coding platform that can be used for both programming and development purposes. Unlike any other language, Python is used by various industries. That is the reason why this is the most demanded skill today. Here is a list of free e-books on Python that can help you upgrade your skills.

  • Picking a Python Version: A Manifesto
  • A Whirlwind Tour of Python
  • 3 in 1 Bundle: Python For Beginners, Java
  • Programming and Html & CSS For Beginners
  • 20 Python Libraries You Aren’t Using (But Should)
  • How to Make Mistakes in Python
  • Python in Education
  • Think Python
  • Learn Python The Hard Way
  • PYTHON: 3 Manuscripts — Python Programming, Hacking Using Python and Linux, and Data Analytics
What are the steps required for executing a successful Machine Learning project with Python (ML Project)?

You want to start doing ML projects using Python but do not know where to begin from? With this step-by-step guide, you will be able to complete your first ever Machine Learning Project.

Step 1- Download and install SciPy. Fetch the Machine Learning package you find the most useful.

Step 2-Download a dataset and comprehend its structure.

Step 3-Look at the dataset and summarize the information accordingly; Dimension of the dataset, statistical summary of each and every attribute, differentiating the data using the class variable.

Step 4-Create whisker plots of individual variables. To understand the idea of data distribution, use histogram representation.

Step 5-Test harness and different algorithms; LR, LDA, KNN, CART, NB, SVM

Step 6-Make and evaluate the best predictions

Step 7-Choose the best model.

How to load a dataset and understand its structure using statistical summaries and data visualization?

Data Visualisation helps in a better understanding of the text, numerics, and stats. The statistical representation of data in the form of patterns can help understand trends and correlations which are otherwise difficult to understand.

For a graphical representation of data, use:

  • Line Chart
  • Histogram
  • Bar Chart

In Matplotlib, Line Chart can be made using the Plot Method. A Histogram can be made using the Hist Method and a Bar Chart, by using the Bar Method.
In Pandas Visualization, Line Chart can be made using .plot.line(). A Histogram, by using plot.hist. And a Bar Chart, by using plot.bar().
In Seaborn, Line Chart can be made using sns.lineplot or sns.kdeplot for the round edges. A Histogram, by using sns.distplot. And a Bar Chart, by using sns.countplot.

What are the best Python libraries essential for Machine Learning in 2019?

R, Julia, C++, Java, Python etc., are all computer languages. However, in the recent past, Python has gained popularity and is now being used in every industry by data scientists and ML experts. In a world where technology is the communication channel, mastering Python for Machine Learning is one of the greatest opportunities available for aspiring data scientists. One is the subset of another. Here is the list of the best Python libraries essential for Machine Learning.

NumPy
Pandas
Seaborn
SciPy
Matpoltlib
Statsmodels
Tensorflow
Theano
Keras
Scikit-Learn
FastAi
Caffe
Gluon
Apache MXNet
PyTorch
XGBoost
LightGBM
CatBoost
ELI5
NLTK
Gensim
Spacy
Bokeh
Plotly
Flask

What are the steps to gain mastery over Machine Learning with Python?

There are 7 steps to Master Machine Learning with Python.

Learning Basic Python Skills: If you want to start your career as a data scientist then having the basic knowledge about Python is important.

Mastering The Basics of Machine Learning : Apart from knowing Python language, it is necessary to understand the basics of Machine Learning..

Knowing The Python Libraries: While mastering ML, all Python libraries should be downloaded on your system.

Generating Hands-On ML Experience: Get extensive hands-on experience in SciPy (Scikit-learn) by testing your knowledge on sample projects

Exploring ML Algorithms: Once you have studied Python well, you are all set to climb higher ladders in Machine Learning.

Exploring Topics: Once you have learned all about Python, start exploring advanced topics related to ML.

Deep Learning in Python: DL and ML go together. Python helps to build the neural networks between them for AI to work.

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

Machine Learning and Deep Learning are both branches of computer science. Today, these are being applied extensively in almost every sector. From data computation to making the machinery smarter, everything is being made human independent. This means, by using a particular type of algorithm, machines are given the power of understanding the command and learning from there without giving any further instructions or without programming time and again.

Deep Learning is a sub-branch of Machine learning. Deep Learning uses neural networks to execute the command or functions of Machine Learning. These are similar to human neural nods. A DL neural network has three main components: an inner layer, a hidden layer, and output layer.

What are the most fundamental steps required to complete a Machine Learning project?

In Machine Learning Projects, Data Understanding, Data Preparation, Business Understanding, Deployment, Modeling, and Evaluation play an important role. Before applying anything to the project, understand the following questions thoroughly.

  • What is our ultimate target? Why is it important for our business?
  • How to examine the inputs and the outputs of the project?
  • How can solutions be implemented?
  • How can this project be a huge success?
  • Is the input data available? If not then how difficult is it to avail it? Are we in a position to use it?
  • Are we having a limited budget and time? Will these hinder our performance?
  • Who will help build an adequate solution? Do we have expert help available?
How can I install Python programming environment on Mac?

Python works on various operating systems. From Windows to iOS, it can be easily downloaded and installed within a few minutes. If you own a Macbook then here is a step-by-step guide for making your Mac device ready for Python Programming.

Step 1 – Install Xcode
Step 2 – Open terminal
Step 3 – Install Homebrew
Step 4 – Install Python
Step 5 – Install Pip Install Packages (PIP)
Step 6 – Install Virtualenv
Step 7 – Install Git and make a Github account

Once all the above 7 steps are executed properly without any mistake, you have successfully installed Python programming environment on your Mac device.

How can I download & install Python step-by-step on Windows?

In order to download and install Python setup environment step-by-step on Windows, you will need access to the Python interpreter. You can visit python.org to access the interpreter.
1. At python.org find the download page for Windows
2. Click on Python Releases for Windows and download the latest Python setup
3. Scroll down and click on either one these mentioned below:
4. Windows x86-64 executable installer for 64-bit
5. Windows x86 executable installer for 32-bit
6. Run the installer by double-clicking on the downloaded file
7. Click on install now button

To avoid any installation at all on computer systems, there are various websites that provide you access to a Python interpreter online.

How to build a set up on Python Environment for Machine Learning within 10 Minutes?

Here is how you need to build a set up of Python Environment for Machine Learning within 10 minutes. Follow the below-mentioned steps to get it right in one go.

Download Anaconda (suitable according to your system)
Install Anaconda
Start Anaconda Navigator
Run and update Anaconda
Install Scikit – learn Python library
Update Scikit – learn library by applying the conda command
Download and Install Deep Learning Libraries
For downloading the Theano DL library: input; ‘conda install theano’ command
For downloading the TensorFlow DL library: input; ‘conda install -c conda-forge tensorflow’ command
For downloading the Keras DL library: input ‘pip install keras’ command.

How & why to use anaconda for Machine Learning?

For scientific computing, Anaconda is an open-source and free distribution of computer languages such as Python and R. Its main objective is to simplify complex package management and deployment of data. It has more than 1500 packages along with conda package and virtual environment manager. It provides tools required for gathering data from files, databases, and data lakes, managing environment with Conda, sharing, working together, and recreating projects.

Follow these steps to use Anaconda for Machine Learning:
1. Download Anaconda (suitable according to your system)
2. Install Anaconda
3. Start Anaconda Navigator
4. Run and update Anaconda
5. Install Scikit – learn Python library
6. Update Scikit – learn library by applying the conda command
7. Download and Install Deep Learning Libraries
8. For downloading the Theano DL library: input; ‘conda install theano’ command, TensorFlow DL library: input; ‘conda install -c conda-forge tensorflow’ command, and the Keras DL library: input ‘pip install keras’ command

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