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Machine Learning with Python Training in Mumbai, India

Know how Statistical Modeling relates to Machine Learning

  • 48 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques

Description

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

1950:
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

1952:
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

1990:
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

PREREQUISITES

For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Understand how to use Pandas, a must have package for anyone attempting data analysis in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

Learn Machine Learning

Learn Machine Learning in Mumbai, India

Machine Learning uses the concepts of Artificial Intelligence to help the system in learning, performing, and improving a given set of tasks. In this, programs and systems are developed for accessing and analyzing the data without any human help.

Machine Learning involves observing the available data to derive information. The analysis of data is used to decipher patterns and relationships in the data, enhancing business scalability and improving business operations. All the major Machine Learning algorithms can be divided into the following two categories:

  • Supervised Machine Learning Algorithms:

This includes using labeled examples to use the past data to get information and then apply it to the new data to predict future events. First, a dataset is loaded to the system which then trains using the data. Next, algorithms are used to infer a function and make predictions. 

  • Unsupervised Machine Learning Algorithms

These types of algorithms are used when the available data is unlabelled and unclassified. The systems use this unlabeled data for deciphering the hidden structure in the data and inferring a function. These algorithms can only draw inferences from the data.

As data is becoming an integral part of our society, so is machine learning. Machines are faster than humans. So, machine learning can help you in resolving, evaluating and working out different options that will help you get the best possible result. Now more than ever, businesses are deploying machine learning to get the work done effectively and efficiently. It is used in different applications for different domains like health care, transport, customer service, nursing, finance, banking, customer service, etc. 

Mumbai may not match Bangalore when it comes to the startup scene. However, that doesn’t change the success stories of various startups in 'The City Of Dreams', such as Quikr, Purple Squirrel, Grabhouse, Doormint, Pepperfry, etc. There are more than 100 Artificial Intelligence startups in Mumbai, including Eightfold, Webaroo, CreditVidya, etc. Data has become an important part of these companies. 

According to a study done by Analytics India Magazine, the AI industry has grown by close to 30 per cent in the last one year to USD 230 million. Yet, over 4,000 positions in India remain vacant due to a shortage of qualified talent at the mid and senior level. 

Here are some other top reasons to learn Machine Learning:

  • It reels in better job opportunities: 

According to a report published by Tractica, by the year 2025, the services driven by AI will reach about $19.9 billion. More and more corporations are now incorporating Machine Learning in their business. This allows machine learning engineers to have better career opportunities.

  • Machine Learning engineers earn a pretty penny: 

According to Analytics India Magazine, Mumbai is the highest paymaster in AI, at almost Rs 15.6 lakh per annum, followed by Bengaluru at Rs 14.5 lakh, and Chennai, the lowest paymaster, at Rs 10.4 lakh. 

  • Demand for Machine Learning skills is only increasing: 

Since the demand for machine learning is so high, there is just not enough talent. And as more and more companies are shifting towards the field, this demand is only going to rise. Currently, there are 90 Artificial Intelligence jobs available in Mumbai.

To learn Machine Learning, you need to follow the below-mentioned steps:

  • Structural Plan: Create a plan containing all the topics that you need to learn. This must be detailed explaining all the resources that you must be using. 
  • Prerequisite: Select a programming language that you will be using for machine learning. Also, brush up your mathematical and statistical skills.
  • Learning: Start learning according to the plan created in the first step. You can use books or online resources to understand the concepts of machine learning. You can also look for offline courses in Mumbai. There are various training centres in Mumbai offering courses in Machine Learning.
  • Implementation: Implement what you are learning. Create a project using the algorithms that you have learnt. There are several real-world datasets that you can use for practice. You can also try participating in online competitions like Kaggle.

If you are an absolute beginner, here is how you can get started in Machine Learning-

  • Adjust your mindset: You need to figure out what motivates you to learn machine learning. Just remember that you need to keep practicing as it will help you learn the concepts of machine learning better.
  • Pick a process that suits you best: The next step is to work through problems and find a solution by picking up a systematic and structured process.
  • Pick a tool: In this step, you need to select the tool through which you will be implementing the concepts of Machine Learning. Here are a few tools that can help you map the tool onto your processes:
    • Weka Workbench – For beginners
    • Python Ecosystem – For Intermediate level learners
    • R Platform – For experts
  • Practice on Datasets: Start working on datasets to implement your machine learning skills. These datasets must be small, real-world and installed on your system.
  • Build your own portfolio: Create projects to not only work on your skills but also build your portfolio.

If you want to become a Machine Learning Engineer, you need to learn the following key technical skill sets:

  1. Programming languages: Programming is one of the most basic and important skills that you need to work on a Machine Learning project. Languages like Python, Java, Scala, etc.  are the most commonly used programming languages. Apart from this, you must be familiar with data processing and data formats. 
  2. Database skills: While working on a machine learning project, you must have knowledge of databases and SQL. You will be required to read the data and transform into a format that the machine learning framework can work with. 
  3. Machine Learning visualization tools: You also need to have knowledge of libraries that are used for data visualization. It is very important as visualizing data will help you get a better understanding of it and decipher patterns and relationships among data.
  4. Knowledge of Machine learning frameworks: Statistical and mathematical skills are required to create the Machine Learning model. You must have experience in working with frameworks like Scala, NLP, TensorFlow, R, Apache Spark, etc. 
  5. Mathematical skills: For processing, analyzing, and creating the Machine Learning model, you need mathematical skills. You must be familiar with concepts like probability, statistics, Bayesian modeling, calculus, optimization, graph theory, hypothesis testing, linear algebra, regression, etc. to become an expert in Machine Learning. 

Successful compilation of the project includes the following steps:

  1. Gathering Data: The first step is to gather accurate data on which you are likely to apply your Machine Learning skills. 
  2. Preparing the developed models: The second step is to work with the raw data and prepare it as raw data cannot readily be injected into our model. 
  3. Data visualization: This is the last step and helps understand the correlation between data and the variables. It also allows to make a good selection of model based on the kind of data that is available.

Algorithms are the most integral and essential part of the Machine Learning. Here is how you can learn the top essential Machine Learning algorithms:

  1. List the various Machine Learning algorithms: Create a list of algorithms that you want to learn. Categorize them according to their types and classes. This will help you in building familiarity with the different types of algorithms.
  2. Apply the Machine Learning algorithms that you listed down: Next step is to implement machine learning algorithms on real-world datasets. Make sure that you focus more on topics like Support Vector Machines, Decision trees, etc.
  3. Describe these Machine Learning algorithms: These descriptions will help you get a better understanding of the algorithm. In the end, you will have a mini-encyclopedia of all the machine learning algorithms.
  4. Implement Machine Learning Algorithms: Start implementing machine learning algorithms on a project. Not only will it improve your programming and machine learning skills, but also help build your portfolio.
  5. Experiment on Machine Learning Algorithms: Experimenting with the Algorithms follows logically after you have learnt and implemented the Algorithms. You can look at various standardized data sets and variables to be able to develop a complex system. Additionally, as you go on experimenting with the algorithms, you will be able to customize the parameters to suit your needs.

Machine Learning Algorithms

K-Nearest Neighbor algorithm is the most basic and essential Machine Learning algorithms for beginners. It is used for predicting the data points’ class from a multiclass dataset. Here is how it works:

  • The first step is defining a number that is stored as ‘k’. This is the number of training samples that are near to the new data point.
  • Next comes the task of assigning a label to the new data point. This is defined by assigned to the neighbors.
  • The user-defined and fixed constant for the number of neighbors is determined by the K-nearest neighbor classifiers.
  • The algorithm follows the radius based classification method that identifies and classifies the samples under a fixed radius by the density of the neighboring data points. 
  • It is also known as the non-generalizing machine learning method where the training data is remembered.

Knowing algorithms to learn Machine Learning is not necessary. You don’t need to know any classic algorithms if you are planning to just use the ML algorithms. 

However, basic knowledge of ML algorithms is required if you want to experiment with concepts of machine learning or create a new algorithm. You need to have the knowledge of the algorithm’s accuracy, its complexity, the costs involved, time taken, etc.

The Machine Learning algorithm can be categorized into the following 3 categories:

  • Supervised Learning: In the supervised learning method, classified past data is used to map the functions of the input variables to the output variables. Here are some of the algorithms that follow the supervised learning method:
    • Linear Regression
    • Logistic Regression
    • Classification and Regression Trees (CART)
    • Naïve Bayes
    • K-Nearest Neighbor 
  • Unsupervised Learning: In Unsupervised Machine Learning Algorithms, systems are not provided with labelled information. The analysis of the dataset’s underlying structure is performed to decipher associations and clusters. Here are some examples of such type of algorithms:
    • Apriori
    • K-Means
    • Principal Component Analysis (PCA)
  • Ensemble Learning: This learning method involves using the results from every learner and combining them to get outcome’s accurate representation. The following algorithms follow ensemble learning method:
    • Bagging
    • Boosting

For beginners, the simplest Machine Learning algorithm is k-nearest neighbor algorithm. It can be used for classification and regression problems. It uses the measure of similarity to perform classification and labeled data for the training phase. 

Selecting the right Machine Learning algorithm for a particular statement is very important as it affects the performance of the ML model. Here is how you can do it:

  • Understanding your data: Before you select the algorithm, you need to understand your data. To do this, you need to follow these steps:
    • Visualize data using graphs and charts
    • Find patterns and relationships among the data
    • Remove the irrelevant data and find the missing one
    • Make the data ready for the model through feature engineering
  • Get the intuition about the task: Next, you need to understand what your problem statement is. This will help you know what learning method to use for your task. There are 4 types of learning methods:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: When it comes to selecting the tools and projects for your ML projects, you can’t always go for the best one because the most efficient algorithms will need high data storage and manipulation resources that can only come from high-end machines. There are 3 constraints that you need to look for before selecting the algorithm:
    • Data storage – for storing the testing and training data
    • Hardware constraints – for computational power
    • Time constraints – For determining the length of the training phase
    • Optimizing the above mentioned factors, you will finally be able to identify the best algorithm that suits your needs.

Here is how you can practically design & implement a Machine Learning algorithm using Python:

  • Select the algorithm that you wish to implement: The first step is to select the algorithm that you want to implement using Python. Apart from this, you also need to figure out the type, classes, description, and special implementation of the algorithm.
  • Select the problem you wish to work upon: You also need to select the problem on which you will be implementing your algorithm. You will be testing your algorithm and validating its efficiency.
  • Research the algorithm that you wish to implement: Use books and online resources to research your algorithm. Go through implementation, outlooks, and description of the algorithm to understand the different methodologies that you can apply on the algorithm. 
  • Undertake unit testing: This last step involves developing and running unit tests for algorithm’s functions. This can be considered as the test-driven development aspect of the algorithm.

To be able to work on a Machine Learning project, you must be familiar with the following essential concepts of Machine Learning:

  • Decision Trees: Decision trees are the supervised learning algorithm that is used for solving classification problems. It will help you in determining the features that must be selected and used for splitting and ending iteration. It has the following advantages:
    • It is simple to understand, visualize and interpret
    • Data preparation can be done with minimal efforts
    • Feature selection and variable screening can be performed
    • It can handle multiple outputs and numerical/categorical data
    • Non-linear relationship between the parameters does not affect the algorithm
  • Support Vector Machines:  These algorithms are used for solving classification and regression problems with high accuracy. It offers the following benefits:
    • Provide optimal solutions
    • Can be used for linearly and nonlinearly separable data
    • ‘Kernel Trick’ helps in performing feature mapping easily.
  • Naive Bayes: Based on Bayes theorem, this algorithm assumes that all the predictors are independent of each other. Here are the following benefits of using Bayes theorem:
    • Requires less training data
    • Quickly converges
    • Is highly scalable
    • Involves doing a bunch of counts
  • Random Forest algorithm: This supervised learning algorithm involves creating forest of decision trees and randomizing the input. This is done so that the system doesn’t identify a pattern on the basis of the order. Bagging method is used to train the model. Here are the advantages of using this algorithm:
    • Can be used for classification and regression
    • Produces good prediction result
    • Easy to use

Machine Learning Engineer Salary in Mumbai, India

The median salary of Machine Learning Engineer in Mumbai is ₹7,50,000/yr. The Range differs from ₹3,76,000 to as high as ₹11,60,000

The average salary of a Machine Learning engineer in Mumbai compared with Bangalore is ₹7,50,000/yr whereas, in Bangalore, it’s ₹8,00,000/yr.

Mumbai is located in Maharashtra and the average salary of machine learning engineers sums up to around ₹6,50,000/yr whereas in Pune it is ₹414,229/yr.

According to  LinkedIn, there are currently 1,829 open Machine Learning Engineering positions on the website. Mumbai is considered among the most developed cities of India. As most companies are integrating ML and AI into their top data initiatives, ML Engineers are in good demand.

Apart from the handsome salary package, other benefits of being a Machine Learning Engineer includes - 

  • Being ahead in the technology sector in terms of learning and evolving as the coding methods and techniques used here are very different from the usual sectors. The Big Data allows the machine to learn itself and therefore helps the Engineers learn from it and predict.
  • Machine learning also opens the door to many new possibilities, such as software engineer, research assistant, technical assistant, data scientist, System engineer, etc.
  • According to a recent report by Research and Markets, the global machine learning market is anticipated to grow from $1.4B in 2017 to $8.8B by 2022, which means there will be high stability in this career.
  • Unorthodox methods of coding which requires one to be really passionate about the work. Engineers can get ahead in their career.
  • Machine learning today is used for lead prediction, NLP and voice search, consumer-driven chatbots, image identification and classification at scale, and hence a great quantity of employers are looking to hire Machine learning engineers.

Although there are many companies offering jobs to Machine Learning Engineers in Mumbai, following are the prominent companies that hire them

  • Quantiphi
  • Cere Labs
  • DemandMatrix
  • Difference-engine.ai
  • Depasser Infotech

Machine Learning Conference in Mumbai, India

    S.No

Conference nameDateVenue
1.INBA General Counsel Summit 201919th July 2019Taj Lands End, Bandra, Mumbai
2.Analytics In Markets (AIM)20th July 2019Renaissance Hotel, Powai, Mumbai
3.International conference on Artificial Intelligence And Robotics (ICAIR-2019), Mumbai
19th – 20th August 2019
Mumbai, ICAIR-19, Mumbai, India
  1. INBA General Counsel Summit 2019, Mumbai
    1. About the conference: The conference aimed to discuss the  role of a general counsel in a process and company where trillions of bytes of data is being constantly generated.
    2. Event Date: 19th July, 2019
    3. Venue: Hotel Taj lands end, Mumbai
    4. Days of Program: 1
    5. Timings: 7:30 – 17:30 
    6. How many speakers: 30
    7. Speakers & Profile:
      • Sanjay Sahay (IPS)- Addl. Director General of Police, Police Computer Wing, Govt. of Karnataka 
      • Bipin Kumar Tibrewala- Group CFO Airbus India
      • K Ramachandran- Sr. Advisor, Banking technology Indian Banks Association 
      • Mohit Shukla- Managing Director, Legal Head Barclays India 
      • Ayan Roy Chowdhury- Director Legal, Sony Pictures Entertainment 
      • Rajiv Choubey- Director Legal ACC Limited
      • Oindrilla Maitra- Director Legal and Business affairs JioSaavn 
      • Pathik Arora- Director Legal and general counsel india Senvion Wind technology 
      • Neha Mahyavamnshi- Director, Field compliance officer (South Asia) SAP India 
      • Anurag Shukla- Sr. vice president and head legal Kotak Mahindra Life insurance
      • Rajesh Kumar- Group manager, Privacy and data protection Infosys 
      • Sudha Hooda- General counsel, India and Board member Nvidia Graphics
      • Preeti Balwani- General counsel the kraft heinz company
    8. With whom can you Network in this Conference:
      • General Counsel, In-House Counsel, Heads of Legal, Governance Heads
      • Lawyers Serving Law Firms, Individual Practitioners
      • Data Protection & Security Heads
      • Fraud, Forensics and Cyber Crime Analysts
      • Privacy Experts, IT and TMT Specialists
      • IP, Litigation, Conciliation, Mediationand Arbitration Experts
      • Law Academicians
      • Digital Heads including AI, Machine Learning Professionals
      • Marketing and Business Heads of Legal Products & Services Companies
    9. Registration cost:
      • Law firms and other organisations: Rs. 6,500
      • In-House Counsel & INBA members: Rs. 5,500
    10. Who are the major sponsors:
      • TFCI events
      • Witness 
      • Maharashtra nlu
  2. Analytics In Markets (AIM), Mumbai
    1. About the conference: The conference aims to discuss Financial analytics with relation to new Age artificial intelligence and conventional statistical tools.
    2. Event Date: 20th July, 2019
    3. Venue: Renaissance Hotel, Powai, Mumbai
    4. Days of Program: 1
    5. Timings: 9:30 – 13:00
    6. Purpose: The goal of the conference is to bridge the gap between industry demands and subject experts
    7. How many speakers: 6
    8. Speakers & Profile:
      • Dr. Diganta Mukherjee: Professor Indian Statistical Institute
      • Dr. H. K. Pradhan: Professor of Finance & Economics XLRI Jamshedpur/ Additional Director  SBI DFHI Limited
      • Dr. N. ChattopadhyayL Director Indian Meteorological Department
      • Dr. Prasenjit Majumder: Associate Professor Dhirubhai Ambani University (DAIICT)
      • Mr. Sankarson Banerjee: Chief Information Officer  RBL Bank
      • Ms. Ujjyaini Mitra: Analytics & Data Science Leader ZEE
    9. With whom can you Network in this Conference:
      • Chief Technology Officer
      • Chief Financial Officer
      •  Chief Executive Officer
      •  Data Scientists
      •  Machine Learners
      •  Analyst Developers
      •  AI Experts
      •  Technical Analysts
    10. Registration cost: 8000
    11. Who are the major sponsors
      • Tickerplant
      • Indian statistical institute
  3. International conference on Artificial Intelligence And Robotics (ICAIR-2019), Mumbai
    1. About: The conference will have a discussion on artificial intelligence and robotics.
    2. Event Date: 19th – 20th August, 2019
    3. Venue: Mumbai, ICAIR-19, Mumbai, India
    4. Days of Program: 2
    5. Timings: 9 AM onwards
    6. Purpose: To provide knowledge of the new and emerging technologies in the field and understand the impact of technology driven process. 

Machine Learning Engineer Jobs in Mumbai, India

As a Machine Learning Engineer, you will be responsible for designing and developing machine learning systems, executing the machine learning algorithms, conducting experiments and tests, working on customizing the algorithms according to your needs, etc.

Mumbai is home to leading banks like ICICI, DCB, IndusInd, etc., hundreds of legacy organisations, family businesses, and over 5000 startups. It is known as India’s fintech capital with over 400 fintech startups. Industries like IT, finance, healthcare and startups are looking to transition into AI, over the next few years, so there is a huge opportunity for machine learning professionals in Mumbai. Currently, there are 831 Machine Learning Jobs in Mumbai. 

The companies with Machine Learning open positions in Mumbai are:

  • DeepAffects
  • JioSaavn
  • Quantiphi
  • Yagerbomb Media Pvt. Ltd
  • Nimap Infotech

The following ML job roles are in demand in 2019:

  • Machine Learning Engineer
  • Cyber Security Analysts
  • Cloud Architects
  • Data Architect
  • Data Scientist
  • Data Mining Specialists

To network with other machine learning engineers in Mumbai, you can try one of the following:

  • Meetups
  • Conferences and tech talks
  • Online platforms like LinkedIn

Machine Learning with Python Mumbai, India

If you want to master Machine Learning using Python, you need to follow these steps:

  1. The first step is to get motivated to learn Machine Learning.
  2. Next, you will have to download and install the programming language, Python SciPy Kit for Machine Learning along with its packages.
  3. Get an understanding of the built-in functions of Python and their usage.
  4. Practice statistical summaries as well as data visualization on a loaded dataset.
  5. You can start by commonly used datasets to develop and improve your concepts.
  6. These steps are likely to help you start working on Machine Learning Projects and applying Python.

To implement machine learning, you need to have the knowledge of the following essential Python libraries:

  • Numpy: This provides high performance for N-dimensional arrays.
  • Scikit-Learn: This library is used for data analysis and data mining.
  • Pandas: This library is particularly used for high-level data structures and data extraction.
  • Matplotlib: This is used for data representation in 2D.
  • TensorFLow: This library is used for Deep Learning purposes in projects. The multi-layer nodes that it uses is particularly effective for training and setting up the neural networks.

Here are the steps you need to follow to create a successful Machine Learning project with Python:

  1. Gathering data: The first step is to gather data on which you are likely to apply your Machine Learning skills. Your Model’s efficiency will depend on the quality and quantity of the data and vice versa.
  2. Cleaning and preparing data: Now, most of the data that you have collected will be in an unstructured form. To make this data ready to be injected into the ML model, you need to prepare this data. This involves removing irrelevant data and find the missing one. Feature engineering is done to convert the data acceptable by our format. After this, data is divided into – training and testing data.
  3. Visualize the data: Visualizing data is very important as it will not only help you get a clear understanding of the data and find the relationships among the data, but also help the non-technical members of the team understand the data.
  4. Choosing the correct model: This step involves selecting the algorithm and model on the basis of data. It is very important as it will determine your projects’ performance and accuracy.
  5. Train and test: This step involves training the ML model with the training data and then testing the accuracy of the model with the testing data.
  6. Adjust parameters: You can improve the accuracy of the ML model by adjusting the parameters of the model.

If you are a beginner in programming and want to learn Python, you need to follow these 6 tips:

  • Consistency is Key: This is crucial in order to develop the muscle memory. Practice as much as you can. Coding daily will help you get a clear understanding of the language.
  • Write it out: Write everything that you are learning. This will help you retain concepts for a longer time. 
  • Go interactive!: You can try going interactive by using the Python shell. Just fire up the terminal, type ‘Python’ in the command line and hit enter. This will help you get thorough knowledge of strings, lists, dictionaries, etc.
  • Assume the role of a Bug Bounty Hunter: Bugs are inevitable. As you will start coding, you will get a lot of bugs. Take the challenge and solve each bug. This will help you improve your programming skills.
  • Surround yourself with other people who are learning: Socialize with fellow Python developers. As a coder, you will find immense help from the community. Join meetups or conferences in Mumbai to connect with students, researchers, experts in Machine Learning.
  • Opt for Pair programming: Pair programming is a coding technique where 2 people work on the same code. The first one is driver who writes the code and the second one is the navigator who directs the process and provides feedback. This is a great way to work in a team, share responsibilities, and at the same time helps develop skills.

If you want to learn Machine Learning, you must have a complete knowledge of the following Python libraries-

  • Scikit-learn: This library is used in projects of the field of data mining, data analysis, and data science.
  • SciPy: It helps in manipulating mathematics and engineering concepts.
  • Numpy: This library is used for performing vector and matrix operations faster and efficiently.
  • Keras: This library is used for implementing neural network.
  • TensorFlow: It is used to train, setup and deploy of artificial neural networks through multi-layered nodes.
  • Pandas: It is used for extracting and preparing data with the help of high-level data structures. 
  • Matplotlib: This library helps to plot 2D graphs for visualization.
  • Pytorch: It is used for implementing Natural Language Processing.

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Faqs

The Course

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Machine Learning experts who have years of industry experience.

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

Have More Questions?

Machine Learning with Python Course in Mumbai

Machine Learning with Python Training in Mumbai

Mumbai, tagged as the City of Dreams is the capital city of Maharashtra. Throbbing with huge numbers of billionaires and millionaires, this city is also known as the economic and financial capital of India. Developing on the IT front as well, this growth has prompted the need for data analysis professionals to make their presence felt in churning out projects, publishing and writing papers by a process which inspects, transforms, cleans and remodels data. Primarily meant to find out useful information, support decision making and conclusions, this is teamed with machine learning using Python by KnowledgeHut as part of a comprehensive course plan.

What is the course all about?

Nowadays, one can learn both data analysis and machine learning by attending workshops related to python course. Data Analysis using python course in Mumbai is being taught by KnowledgeHut, an online academy. This course helps one learn programming techniques using python. Apart from that, one can also learn about scientific computing libraries and modules. Data analysis training using python in Mumbai can give a big boost to one's career as most of the companies work on data analysis. KnowledgeHut through a demo offers student-friendly procedures for them to clear their doubts whenever possible, supported by exhaustive training material for ready reference. Benefits of the course Python has its own libraries using which a student can learn machine learning very properly. A student can attend various lectures and practice sessions of machine learning using python course in Mumbai which can later be incorporated into machine learning.

The KnowledgeHut Way

The coaching of machine learning includes various modules like linear regression, parallel computing, decision tree and clustering, deep learning and unsupervised learning. The cost charged by machine learning training using python facilitated by KnowledgeHut is very less. Therefore, one should register and enroll in a purse-friendly and knowledge-centric course.