Machine Learning with Python Training in Mumbai, India

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 250,000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Machine Learning skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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Highlights

  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Machine Learning?

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Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand machine learning and Python skills and meet that need.  

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Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

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Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

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Get trained by leading practitioners who share best practices from their experience across industries.

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Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

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Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

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Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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What you will learn in the Machine Learning with Python course

1

Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.

2

Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

3

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.

4

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.

5

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.

6

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .

7

Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis.

Skill you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

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Machine Learning with Python Training Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  

Topics

  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  

Hands-on

  • Learning to implement statistical operations in Excel

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of 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 with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 

Topics

  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling 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 and Underfitting  

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp

Topics

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

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp

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

  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use 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 based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  

Topics

  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study

Hands-on

  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 

Topics

  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study

Hands-on

  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 

Topics 

  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study

Hands-on

  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

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

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

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

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

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

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

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

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

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

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

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

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

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

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

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

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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

If you want to network with other Machine Learning Engineers, you can try one of the following professional groups:

  • Mumbai Women in Machine Learning and Data Science
  • Mumbai Machine Learning Group
  • Machine Learning India - Mumbai
  • IBM AI Mumbai

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.

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.

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