Machine Learning with Python Training in New Jersey, NJ, United States

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. 

Real-World Focus

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.

Curriculum Designed by the Best

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. 

What learners are saying

V
Vito Dapice Data Quality Manager
5

The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good experience.

Attended PMP® Certification workshop in April 2020

E
Elyssa Taber IT Manager.
3

I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.

Attended Agile and Scrum workshop in June 2020

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Astrid Corduas Senior Web Administrator
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The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development. Thank you KnowledgeHut.

Attended PMP® Certification workshop in April 2020

I
Issy Basseri Database Administrator
5

Knowledgehut is the best training institution. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask for feedback every time and clear all the doubts.

Attended PMP® Certification workshop in January 2020

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Yancey Rosenkrantz Senior Network System Administrator
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The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

M
Marta Fitts Network Engineer
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The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

Attended PMP® Certification workshop in May 2020

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Kayne Stewart slavsky Project Manager
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The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

Attended PMP® Certification workshop in June 2020

A
Anabel Bavaro Senior Engineer
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The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

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Learn Machine Learning

Learn Machine Learning in New Jersey, USA

Machine Learning involves applying systems using the concepts of Artificial Intelligence for making the systems capable of learning, improving and performing tasks automatically without the need of human intervention or any reprogramming. The Machine Learning concept focuses mainly on developing computer systems and programs that can access and analyse data on their own and then learn from it.

The whole process begins with using observations of data. Then, the systems and programs look for patterns within the data, which are then extrapolated for making better future decisions based on the datasets and examples available to the program or computer system.

Machine Learning methods can be categorized into:

  • Supervised ML algorithms: These algorithms apply learnings from previous data to the new data. Labelled examples are used for predicting future events.
    • The system learns from and trains on a known dataset that is fed into the system
    • A learning algorithm is produced in the form of functions used for making predictions
    • These algorithms provide results for new inputs after undergoing adequate training and learning.
  • Unsupervised ML algorithms: These algorithms are used when information needed for system training is not classified or labelled.
    • Unsupervised learning systems infer functions describing hidden structure from labelled data
    • These systems can’t provide accurate results but can draw inferences from exploring the data available. 

New Jersey has 16 of the Fastest Growing Tech Companies, according to Deloitte. All these companies are looking for expert ML engineers to help them predict customer behaviors, purchasing patterns, and customized offers. 

The Machine Learning concept involves systems and computers taking in huge data and analysing it and training on the data to solve a problem or perform a task in the best possible way. It allows humans to solve problems without the need of understanding the problem or why the problem needs to be approached in a certain manner.

  • Easy and effective: Machines can work faster than human brains do and hence, can solve problems faster than we ever can. For example, if there are a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.
  • Vast applications: There are many real-life applications of Machine Learning. It is the long-awaited solution to various problems. Machine learning drives businesses by helping them save time and money as well as effort. It is allowing people to get more things done in a more efficient, effective and appropriate manner. Many industries in New Jersey, starting from health care, transport, nursing, customer service financial and government institutions are utilizing Machine Learning.

All organizations, from start-ups to big brand names in New Jersey, are looking to gather the immense data generated nowadays and put it to use for key decisions. Big and small data is reshaping technology and business and it will continue to do so.

The predominance of Machine Learning in day to day life and tech world

Machine Learning is a new field of research. Tech experts have been increasingly making use of Machine Learning over the years. Surge pricing at Uber, Google Maps, Social media feeds on Facebook and Instagram, etc – all of these make use of Machine Learning algorithms. Knowingly or unknowingly, every individual is making use of a Machine Learning product. In such a scenario, learning about Machine Learning is something that all professionals, particularly those involved in the field of IT and Data Science, must do in order to stay relevant.

Machine Learning benefits include:

  1. Improved job opportunities: With every industry in the world looking at expansion in the domain of Machine Learning and AI, a knowledge of the same is bound to attract improved career opportunities.
  2. High pay for Machine Learning engineers: The average Machine Learning Engineer salary in New Jersey is $100k.
  3. Increase in demand for skills in Machine Learning easing: There is a huge gap between the availability and the demand of Machine Learning engineers, even in some of the biggest corporations around the world. Hence, the demand as well as the income for professionals with Machine Learning skills will surely increase in the future. New Jersey is a great place for machine learning engineers to work in. Some of the companies that employ machine learning professionals in New Jersey include BNY Mellon, McKinsey & Company, Jet.com, JP Morgan Chase, Jet & the Walmart eCommerce Family of Brands, Google, Deloitte, etc.
  4. Most industries are using Machine Learning: Most industries around the world are dealing with huge data. By gaining insights from this data, companies in New Jersey are looking to be more efficient and competent in their work and gain an advantage over their competitors.

The best way to learn Machine Learning is through a course. In New Jersey, there are several institutions that offer courses in Machine Learning including:

  1. New Jersey Institute of Technology
  2. ONLC Training Centers
  3. NobleProg
  4. DeZyre
  5. Rutgers School of Arts and Science

Machine learning is a diverse and huge field. Staying motivated is the key to effective self- learning of ML. You should also keep the following points in mind:

  • Hands-on learning allows learning practical skills faster and learning to implement them
  • Working on projects is the best way to attract employers and to test our skills

Following steps need to be followed:

  1. Structured Plan: Create a plan on the topics that you need to learn
  2. Programming and Statistics: Choose a programming language you feel comfortable with and start brushing up your statistical and mathematical skills
  3. Learn: Stick to the plan you have created. Learn from books or from different sources online or from books. Understand the workflow of ML algorithms
  4. Implement: Learning is impossible without implementation of skills. Try working on projects using the algorithms you learned. Start solving datasets and problems available on the internet, and take part in online competitions like Kaggle.

If you are a beginner in Machine learning, networking with other professionals will help you get a clear understanding of Machine learning concepts and the current trends. Here are a few meetups organized in New Jersey for Machine Learning professionals:

  1.    New Jersey Data Science Meetup
  2.    InRhythmU
  3.    Central NJ Data Science Meetup
  4.    Hadoop Big Data Analytics of NJ Meetup
  5.   NJ Data Science

The five-step process to get started on Machine Learning for an absolute beginner in New Jersey includes:

  • Adjusting the mindset:
    • Realize what might be holding you back from achieving your objectives
    • You need to believe that Machine Learning is not as complicated as it might seem
    • Machine Learning should be thought of as a concept that you have to practice to get a grasp of
    • Try connecting with people who can help you with your learning process of Machine Learning.
  • Choosing a suitable process: Choose a systematic and structured process that suits you.
  • Picking a tool: Pick a tool that you are comfortable with and utilize it for the process.
    • Weka Workbench is recommended for beginners
    • The Python Ecosystem is recommended for intermediate learners
    • The R Platform is recommended for advanced level learners
  • Practicing on Datasets: Practice data manipulation and collection by choosing one of numerous datasets available to work on
    • Practice your own Machine Learning skills with relatively small, installed in memory datasets
    • Gain an understanding of real world problems connected to the world of Machine Learning
  • Building a portfolio: A portfolio can help you demonstrate your skills and the knowledge you have gained.

New Jersey is the hub for several tech companies that are willing to pay handsomely to skilled Machine Learning professionals. These organizations include Dun and Bradstreet, Two Sigma Investments, LLC., WeWork, Spotify, Butterfly Network, Express Scripts, SoftVision, IPsoft, Dow Jones, Etsy, WorkFusion, The Trevor Project, Amazon, DIA Associates, etc. Below are some key technical skill sets required to learn Machine Learning (ML):

  • Programming: An important prerequisite is to be capable of comfortably programming with languages like Java, Python, Scala etc. An ML learner must be able to at least make basic use of such programming languages.
  • Database skills: A previous experience and knowledge of MySQL and relational databases is another prerequisite to understand ML concepts. Programmers must be capable of reading data from different sources, and then convert the obtained data in a readable and compatible format.
  • Visualization tools: There are several tools for visualizing the data. A basic understanding and knowledge of these tools will be helpful while applying ML concepts in real life.
  • Knowledge of ML frameworks: Several mathematical and statistical algorithms, are made use of in order to design a Machine Learning model to learn from the input data and make a prediction for a given data set. Knowledge of frameworks like ScalaNLP, Apache Spark ML, TensorFlow R, etc. is highly recommended.
  • Mathematical understanding: Mathematics is at the core of Machine Learning. This is because a Machine Learning model is formed through these mathematical concepts and algorithms. A Machine learning student must know the following mathematical concepts:
    • Optimization
    • Calculus of variations
    • Linear algebra
    • Probability theory
    • Probability Distributions
    • Bayesian Modeling
    • Fitting of a distribution
    • Mathematical statistics
    • Regression and Time Series
    • Differential equations
    • Statistics and Probability
    • Graph theory

Given below are steps to successfully execute an ML project:

  1. Data Gathering: The most crucial step is to get the right data for your Machine Learning project. The performance of your model is highly dependent on the quantity and quality of data.
  2. Data Cleaning and Preparation: The gathered data is raw data and cannot be injected directly into the model. This step involves careful cleaning of data which involves correction of the missing data and preparing the data. The raw data needs to be converted to the right data for the model and then the data needs to be divided into two parts: testing data and training data.
  3. Data Visualization: This can also be the final step of the project to show the prepared data and finding the correlation between the variables. Visualization helps to understand the nature of data and accordingly select a model.
  4. Picking the correct model: The next step is to harvest this data and to identify which model or algorithm is best suited to do so. The performance of the algorithm is significantly determined by the model you choose.
  5. Training and testing: The data is prepared for being injected into our chosen model. In the previous step, the data has been into training and testing data. Now, the model is trained with the training data and after it is trained, its accuracy is tested with the test data in which it wasn’t trained.
  6. Adjusting parameters: The parameters can be adjusted after determining the accuracy of the model. An example is to change the number of neurons in a neural network.

Every ML learner should know and understand the concepts of algorithms in ML as it forms a crucial part of the study. You can do it by:

  1. Enlisting different ML algorithms: Every algorithm has its own uniqueness and importance. However, you need to decide which Machine Learning algorithms you wish to begin with. List down these algorithms in a doc or text file.
  2. Applying the listed ML algorithms: There is a reason why Machine Learning algorithms exist. No matter how much time you spend learning the theory, the best way to learn is to practically apply and implement Machine Learning algorithms on data sets.
  3.  Description of algorithms: The logical step is to explore what has been understood already about the algorithms. A thorough understanding and analysis of ML algorithms will help describe these algorithms. Keep adding information to these descriptions as you find more information during the course of your study of ML algorithms.
  4. Implementation of algorithms: The most concrete way to learn the workings of an algorithm is to implement it. That way, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts. It will also provide an idea about the workings of an algorithm along with the mathematical descriptions and extensions of the algorithm.
  5. Experimentation on Algorithms: After implementation and understanding of ML algorithms, you can experiment with an algorithm. Standardized data sets, control variables can be used as well as study the functioning of algorithms in the form of a complex system in itself

Machine Learning Algorithms

The K Nearest Neighbours algorithm is an uncomplicated and simplistic Machine Learning algorithm. It can be used when a totally multiclass dataset is to be worked on, for prediction of the class of a given data point.

  • The main requirement for the classification of nearest neighbour is a pre-defined number, which is stored as the value of ‘k’. This number, k, defines the number of training samples that are closest in distance to a new data point that is to be classified. 
  • The label that will be assigned to this new data point, will then, be one that has already been assigned to and defined by these neighbours.
  • There is a fixed user-defined constant for K-nearest neighbour classifiers defining the number of neighbours that has to be determined.
  • These algorithms work on the concept of radius based classification. The concept is that all the samples are identified and classified inside and under a fixed radius depending on the density of the neighbouring data points.
  • All these methods based on the classification of the neighbours are also known as the non-generalizing Machine Learning methods.
  • A majority vote is conducted among the nearest neighbours of an unknown sample and then classification is performed.

The K Nearest Neighbour algorithm is the simplest of all machine learning algorithms. Yet, the algorithm is proven to be very useful for solving numerous of regression and classification problems, with character recognition and image analysis being the examples.

Your intention while learning Machine Learning determines whether you need to learn ML algorithms or not. If you simply want to make use of existing Machine Learning algorithms, knowledge of classic algorithms may not be required. There are several courses on the internet providing knowledge on Machine Learning, without having algorithms as a requirement. You can also join boot camps in New Jersey if you are not comfortable taking online classes. 

If you want to be innovative with Machine Learning, a critical prerequisite is to have some knowledge of how algorithms work and what are its uses. Since you will basically be involved in the adaptation or design of a new algorithm, you need the knowledge and tools required for adapting, designing and innovating. You need to be familiar with concepts like correctness of an algorithm, its complexity, time taken by an algorithm, costs involved etc.

Machine Learning Algorithms can be classified basically into the following 3 types - 

  • Supervised Learning: The use of historical data that is categorized for learning the mapping function from the input variables (X) to the output variable (Y). Examples are:
    • Linear Regression   
    • Logistic Regression
    • Classification and Regression Trees (CART)
    • Naïve Bayes
    • K-Nearest Neighbours  
  • Unsupervised Learning: With such problems, output variables are not given and only the input variables are given. Thus, possible clusters and associations are revealed through analysis of the underlying structure of the given data sets. Examples are -
    • Apriori
    • K-Means
    • Principal Component Analysis (PCA)
  • Ensemble Learning:  Such algorithms work on the premise that ensembles or groups of learners are likely to have better performance than singular learners. The results of each learner are combined and then analysed as a whole for getting a relatively accurate representation of the outcome.  

The simplest of machine learning algorithms solve the simplest of ML problems. The criteria for selection of such algorithm are:

  • Easy to implement and understand the underlying principles.
  • Takes less time and resources for training and testing the data as compared to high-level algorithms.

The k-nearest neighbour algorithm is best suited for beginners of Machine Learning. It is a classification algorithm that can be used for regression as well. Some practical and real-life examples where KNN is used are:

  • Used to detect patterns in credit card usage.
  • When searching in documents containing similar topics.
  • Vehicular number plate recognition.

Given the popularity of ML, there are numerous models, algorithms and tools available to choose from. However, there are a few things you have to keep in mind while choosing the algorithm which will be the core of your project. These include:

  • Understand data: The data upon which you will apply your algorithm is the first thing you need to consider and accordingly find the right algorithm
    • Visualize your data by plotting graphs.
    • Your data is not always perfect, there can be missing data or bad data as well which can be sensitive to your model. Deal with this and clean your data.
    • Try to find correlations among the data which indicate strong relationships.
    • Prepare your data by feature engineering to make your data ready to be injected into your model.
  • Be intuitive: A lot of times, we don’t understand the underlying objective of the task, and that’s why ML is needed to solve the problem in the first place. After understanding that, you need to see which kind of learning will help your model complete the task at hand. There are 4 types of learning in general:
    • Supervised learning
    • Semi-supervised learning
    • Reinforcement learning
    • Unsupervised learning
  • Know the constraints: Without any constraints while planning, you will end up choosing the best algorithms and tools, which isn’t the right approach. Constraints can be both software and hardware.
    • Data storage capacity limits the amount of data that we can stored for training and testing phases.
    • Depending upon time constraints, you can determine the duration to allow for testing and training phase.
    • Hardware constraints allow us to choose algorithms which run according to the hardware available to us.
  • Find suitable algorithms: After completion of the above three steps, it is possible to find algorithms that suit your requirements, and constraints.

You become more efficient and faster with implementation of ML algorithms as you continue to implement different algorithms. The process for implementation can be

  1. Choosing a programming language: The decision of selecting the programming language will determine the standard libraries and APIs that can be used for implementation.
  2. Choosing the algorithm for implementation: While choosing an algorithm, consider the specifics of the algorithm decisively and precisely. Thus, you need to decide on the type of algorithm, the classes and also the specific description and implementation.
  3. Choosing the problem: Select a canonical problem set that will be used for testing and validating the correctness and efficiency implementation.
  4. Researching the algorithm that you wish to implement: Go through books, libraries, research papers, blogs and websites containing descriptions of algorithm, its implementation, conceptual understanding etc.
  5. Unit testing: Develop unit tests and run it for all functions of the algorithm.

There are many institutes and training centers in New Jersey offering basic courses on Machine Learning, such as ONLC Training Centers, New Jersey Institute of Technology, etc. 

Apart from the basic Machine Learning concepts, some important topics that all learners of ML should know include:

  • Decision Trees: It is a type of a supervised learning algorithm used for classification problems.
  • Support Vector Machines: These are a type of classification methodologies that provide a higher degree of accuracy in classification problems.
  • Naive Bayes: This algorithm is a classification technique based on the Bayes’ theorem.
  • Random Forest algorithm: It is a supervised learning algorithm

Machine Learning Engineer Salary in New Jersey, NJ

The median salary of a Machine Learning Engineer in New Jersey is $1,37,146/yr. The range differs from $100K to as high as $167K.

The average salary of a machine learning engineer in New Jersey compared with Portland is $1,16,000/yr whereas, in Portland, it’s $1,09,000/yr.

If you’re to follow the most trusted career social network, LinkedIn, there are more than 1800 Machine learning engineering jobs available. The numbers have had astonishing uplift in the last 3 years and the sector has grown more than 344% making ML Engineering the fastest growing job sector. These factors very well prove how much the industry values Machine learning engineers.

New Jersey is home to many technology start-ups and companies. A recent report has revealed that while data scientist is still the most popular, the rise of machine learning careers is high as it is growing more than 9 times of what it was 5 years ago. These numbers are validations of the promise that this job holds and the endless possibilities it offers. And, obviously not to forget an impressive average base salary of $146,085.

It is not just that the high salary that Machine learning Engineering offers that has created this massive demand but it is also how much the business and technology sectors need skilled professionals to unlock the full potential of Machine learning. Following are the perks of being a machine learning engineer apart from the high payout -

  • Opportunities - The sector is growing at a rate of 344% which has also made this job the dream job for most of the engineering graduates in 2018. Moreover, it is the opportunity to grow that machine learning offers which attracts these skilled professionals to try and use their abilities for the best.
  • Network - When you are working towards a bigger goal, you learn how to build networks in order to ease your work and utilize the opportunities to their maximum extent.

Although there are quite many companies offering jobs to Machine Learning Engineers in New Jersey, following are the prominent companies

  • Apple
  • Siemens Healthineers
  • Spotify
  • Dia&Co
  • Microsoft
  • J.P. Morgan
  • Intel Corporation
  • Bloomberg L.P.
  • Futurewei Technologies

Machine Learning Conference in New Jersey, NJ

S.NoConference NameDateVenue
1.6th Annual NJBDA Symposium: The Future of Big Data: Artificial Intelligence and Machine LearningApril 5th, 2019New Jersey City University, NJ, USA
2.Machine Learning LiveMarch 20th, 2019

Hyatt House Jersey City, 1 Exchange Place, Jersey City, NJ 07302, United States

3.

Disruptive Innovation in Bio-Pharma Ecosystem through AI & Machine learning

October 10th, 2019

Bristol-Myers Squibb 1 Squibb Dr. New Brunswick, NJ 08903 United States

4.Angelbeat Technology Seminar on Cloud/Security/AI/Data

October 22nd, 2019

Princeton, NJ. USA

5.

MRS 2019 — International Symposium on Multi-Robot and Multi-Agent Systems

August 22nd-23rd, 2019

Rutgers University, New Brunswick, NJ, USA

  1.  6th Annual NJBDA Symposium: The Future of Big Data: Artificial Intelligence and Machine Learning, New Jersey
    1. About the Conference: The conference brings together the best professionals, exceptional students, academic researchers, government participants to discuss the future and innovations in Big Data.
    2. Event Date: 5th April, 2019
    3. Venue: 2039 Kennedy Blvd Jersey City, NJ 07305
    4. Days of Program: 1
    5. Timing: 8.30 a.m. - 3.30 p.m.
    6. Speakers & Profiles
      • J.D. Jayaraman, Ph.D, Associate Professor New Jersey City University
      • Peggy Brennan-Tonetta, Ph.D, Associate Vice President for Economic Development, Rutgers University
      • Dr. Bernard McSherry, Dean, School of Business, NJCU Steven Fulop, Mayor, Jersey City Chris Rein, Chief Technology Officer, New Jersey Office of Information Technology
      • Dr. Manish Parashar, Director, Office of Advanced Cyberinfrastructure, National Science Foundation (NSF) and Distinguished Professor of Computer Science, Rutgers University
      • Dr. Hieu Duc Nguyen, Rowan University
      • Shen-Shyang Ho, Scott Zockoll, Mathew Marchiano, Hieu Nguyen, Rowan University
      • Yunzhe Xue, NJIT; Fadi G. Farhat, NJIT
      • Olga Boukrina, Kessler Foundation,
      • Anna M. Barrett, Kessler Foundation;
      • Jeffrey R. Binder, Medical College of Wisconsin; Usman W.Roshan, NJIT;
      • William W. Graves, Rutgers University
      • Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Rowan University
      • Dr. Forough Ghahramani, Rutgers University
      • Scott Fisher, New Jersey City University
      • Ruoyuan Gao, Souvick Ghosh, Matthew Mitsui, Chirag Shah, Rutgers University
      • Abdullah Albizri, Katherine Ashley, Marina Johnson, Montclair State University
      • Jim Samuel, William Paterson University
      • Deniz Appelbaum, Abdullah Albizri Montclair State University
      • Pradeep Subedi, Philip Davis, Manish Parashar, Rutgers University
      • Jason G. Cooper, Managing Director, JCG, LLC
      • Dr. Sue Henderson, President, NJCU
      • Dr. Chris White, Lab Leader, Algorithms, Analytics and Augmented Intelligence, Nokia Bell Labs
      • Mark McKoy, VP & General Manager, UPS
      • Mohammed Chaara, Enterprise Director of Advanced Analytics, UPS
      • Karen Reif, Vice President, Renewables and Energy Solutions, PSE&G
      • Dennis Belanger, Director, Operational Certainty, Emerson Automation Solutions
      • Alex Cunningham, Data Scientist, Church and Dwight Co, Inc.
      • Stanislav Mamonov, Montclair University
      • Christie Nelson, Rutgers University
      • Jessica Paolini, Rutgers University
    7. Who were the major sponsors:
      • NJBDA
      • Rutgers University
  1. Machine Learning Live, New Jersey
    1. About the Conference: Discussing ways of accelerating machine learning applications from edge-to-cloud.
    2. Event Date: March 20th, 2019
    3. Venue: Hyatt House Jersey City, 1 Exchange Place, Jersey City, NJ 07302, United States
    4. Days of Program: 1
    5. Timing: 8.00a.m. - 5p.m.
    6. Purpose: Showcase the leveraging of Xilinx to understand network latency and having first hand experiences of the workshops conducted by in-house technical experts.
    7. Who are the major sponsors: Xilinx
  1. Disruptive Innovation in Bio-Pharma Ecosystem through AI & Machine learning, New Jersey
    1. About the Conference: Professional Meeting of innovators in AI and Machine Learning to discuss their use in Bio-Pharma Ecosystem
    2. Event Date: October 10th 2019
    3. Venue: Bristol-Myers Squibb,1 Squibb Dr. New Brunswick, NJ 08903, United States
    4. Days of Program: 1
    5. Timing: 5.00p.m. - 9.00p.m.
    6. Registration fee: $20-$500
    7. Purpose: Discuss the foundational aspect of Artificial Intelligence and Machine Learning and how it is the right time for Artificial Intelligence.
    8. Speakers
      • Nataraj Dasgupta, Vice President, RxDataScience Inc.
      • Vijay Mohan, Senior Principal Applied Scientist, Amazon.com
  1. Angelbeat Technology Seminar on Cloud/Security/AI/Data, New Jersey
    1. About the Conference: The conference includes presentations by leading technology providers without sales pitch for professionals from Security, Storage, Infrastructure, AI/ML, DevOps, Applications/Programming, Data Governance/Analytics, Databases and Digital Transformation.
    2. Event Date: October 22nd, 2019
    3. Venue: Princeton, NJ 08540
    4. Days of Program: 1
    5. Timing: 8.00a.m.-3.00p.m.
    6. Registration: $200
    7. Purpose
      • Discussing traditional areas like:
        • Security/Compliance, Ethical Hacking/Penetration Testing
          • Storage/Backup and Data Center Infrastructure
          • Systems Management & Automation/Orchestration
          • Network/Application Performance
          • DevOps and Accelerated/Automated Application Updates
          • Mobility, Wireless & Collaboration
      • Discussing changing topography of technology landscape that are Cloud-centric and Application/Data Oriented like:
        • Private/Public/Hybrid Cloud Strategies
          • Multi-Cloud Provider Environments
          • Cloud Native Storage to Avoid Vendor Lock-in
          • Artificial Intelligence (AI) & Machine Learning (ML)
          • Containers, Dockers & Kubernetes Application Architecture
          • Data Analytics, Internet-of-Things (IoT), Business Intelligence (BI)
          • Chatbots, Cloud-Based & AI-Powered Unified Communications/Conversations
          • Microservices
          • Blockchain
    8. Who are the major sponsors: Angelbeat
  1. MRS 2019 — International Symposium on Multi-Robot and Multi-Agent Systems, New Jersey

    1. About the Conference: The conference brings together the best researchers to cross-fertilize ideas for the development in Multi-Robot and Multi-Agent Systems. The importance of Machine Learning and how it takes forward the researches in Artificial Intelligence.
    2. Event Date: August 22nd-23rd, 2019
    3. Venue: Rutgers University, New Brunswick, NJ, USA
    4. Timing: 8.00a.m. - 6.30p.m.
    5. Days of Program: 2
    6. Registration: $200 - $450
    7. Purpose: To bring to attention the various developments and research that has taken place in the field, and exchange ideas about the next step for Multi-Robot and Multi-Agent Systems
    8. Sponsors
      • IEEE RAS,
      • Rutgers University,
      • Amazon Robotics
    9. Speakers
      • Katia Sycara, Research Professor, School of Computer Science, Carnegie Mellon University
      • Dan Halperin, Professor, School of Computer Science, Tel Aviv University
      • Bryan Kian Tsian Low, Assistant Professor, Department of Computer Science, National University of Singapore
      • Peter Stone, Founder and Director, Learning Agents Research Group at the Artificial Intelligence Laboratory, Department of Computer Science, University of Texas at Austin
      • Gaurav Sukhatme, Professor of Computer Science and Electrical Engineering Systems, USC Viterbi School of Engineering
S.NoConference NameDateVenue
1.ACM 2018- International Conference on Pattern Recognition and Artificial IntelligenceAugust 15-17th, 2018Kean University, Union, NJ, USA
2.5th Annual NJBDA Symposium and 1st Annual Career Fair @TCNJ ‘Big Data: Transforming tomorrow’s workplace’April 30th, 2018College of New Jersey
3.4th Annual NJBDA Symposium @NJIT ‘Big Data Connects’March 16th, 2017New Jersey Institute of Technology
  1.  ACM 2018- International Conference on Pattern Recognition and Artificial Intelligence, New Jersey
    1. About the conference: It was an annual conference to take forward the new developments in Pattern Recognition, Artificial Intelligence and Machine Learning. The best government officials, researchers and professors came together to discuss the future of the discipline.
    2. Event date: August 15-17th, 2018
    3. Venue: Kean University, Union, NJ, USA
    4. Days of Program: 3
    5. Registration: $60-$450
    6. Purpose: Discussions were held on various subjects related to Pattern Recognition and Machine Learning which included:
      • Statistical, syntactic and structural pattern recognition
      • Machine learning and data mining
      • Artificial neural networks
      • Dimensionality reduction and manifold learning
      • Classification and clustering
      • Graphical Models for Pattern Recognition
      • Representation and analysis in pixel/voxel images
      • Support vector machines and kernel methods
      • Symbolic learning
      • Active and ensemble learning
      • Deep learning
      • Pattern recognition for big data
      • Transfer learning
      • Semi-supervised learning and spectral methods
      • Model selection
      • Reinforcement learning and temporal models
      • Performance Evaluation
    7. Speakers
      • Prof. Chingsong Wei from City University of New York, USA, 
      • Prof. Mehmet Celenk from Ohio University, USA
  1. 5th Annual NJBDA Symposium and 1st Annual Career Fair @TCNJ, New Jersey
    1. About the conference: The conference had a discussion on the impact and contribution of Machine Learning and Big Data in business operations.
    2. Event Date: August 30th, 2018
    3. Venue: College of New Jersey, 2000 Pennington Rd., Ewing, NJ, USA
    4. Days of Program: 1
    5. Purpose: Finding methods of incorporating computing technologies and big data analysis to improve business and have competitive advantage. Sponsors also organized a career fair where they recruited top researchers and students of Big Data and Machine Learning among the hundreds of qualified experts who attended the conference.
    6. Who were the major sponsors:
      • Daiichi-Sankyo
      • Neotech
      • pka-Tech
      • RICOH
  1. 4th Annual NJBDA Symposium @NJIT ‘Big Data Connects’, New Jersey

    1. About: A healthy discussion between professionals and students took place to discuss the potential use of Machine Learning in different fields of healthcare, business and national security.
    2. Date: March 16th, 2017
    3. Venue: New Jersey Institute of Technology, Newark, NJ 
    4. Days of Program: 1
    5. Purpose: The conference discussed the practical application of Big Data and Machine Learning in various social security and economic fields.
    6. Speakers
      • Jason Cooper, VP and Chief Analytics Officer, Horizon Bluecross,
      • Malcolm Kahn, Mormon Water
      • Govi Rao, CEO, Noveda Technologies

Machine Learning Engineer Jobs in New Jersey, NJ

Here are the responsibilities of a Machine Learning Engineer:

  • To create programs that will allow machines to take actions without being directed to perform those tasks
  • Implementing and analyzing the Machine Learning Algorithms
  • Operation of tests and experiments
  • Design and development of deep learning and machine learning systems

New Jersey is home to several big names in the corporate world including Cognizant, Merck, Honeywell, Conduent, ADP, Newell Brands, Toys ‘R’ Us, Bed Bath & Beyond, etc. It also has several startups that are actively looking for machine learning engineers to join their team and help them understand business objectives and develop models.

Some of the companies hiring in New Jersey are:

  • JP Morgan Chase
  • Bank of America
  • Dun & Bradstreet
  • BNY Mellon
  • Deloitte

The top professional groups for Machine Learning Engineer in New Jersey:

  • New Jersey Data Science Meetup
  • Bridgewater Machine Intelligence Meetup
  • AI Alliance

Here are some of the jobs in the field of Machine Learning:

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

Referrals have become the most common source of interviews in the IT industry. For this, you need a strong professional network. Here is how you can network with other Machine Learning Engineers in New Jersey:

  • Machine Learning conferences
  • Online platforms like LinkedIn
  • Social gatherings like meetups

Machine Learning with Python New Jersey, NJ

Steps for getting started with Python for Machine Learning are given below:

  1. Have the right mindset to apply Machine Learning Concepts.
  2. Download the Python SciPy Kit for Machine learning and install it along with other useful packages.
  3. Explore the tool in order to get an idea of all the available functionalities and their uses.
  4. Load a dataset and use data visualization and statistical summaries for understanding its workings and structure.
  5. Practice with the commonly used datasets so as to better understand the concepts.
  6. Begin with small projects then move to more complicated and bigger projects.
  7. With all the knowledge gathered, you will eventually have the confidence to apply Python for Machine Learning Projects.

Python has an open source community, because of which it has many useful libraries, including:

  • Pandas: Useful for high-level data structures and incredibly useful for data extraction and preparation.
  • Scikit-learn: Primarily used for data mining and also in data science and data analysis.
  • Matplotlib: Almost all ML problems require plotting of a graph for data representation, matplotlib allows 2D graph plotting.
  • Numpy: Useful due to its high performance when dealing with N-dimensional arrays.
  • TensorFlow: This library is created by Google and is the library that should be used for deep learning projects. This is because it makes use of multi-layered nodes, allowing quick training, setting up and deployment of artificial neural networks.

Given below are steps to successfully execute an ML project:

  1. Data Gathering: Choosing the right data is the most important part of your Machine Learning project. The performance of your model is highly dependent on the quantity and quality of data.
  2. Data Cleaning and Preparation: This step includes cleaning of data, such as correcting missing data and preparing the data. 
  3. Data Visualization: Visualization helps to understand the nature of data and accordingly select a model. 
  4. Picking the correct model: The performance of the algorithm is significantly determined by the model you choose.
  5. Training and testing: The model is trained with the training data and after it is trained, its accuracy is tested with the test data in which it wasn’t trained. 
  6. Adjusting parameters: The parameters can be adjusted after determining the accuracy of the model.  

Here are some tips for beginners to learn Python Programming:

  1. Be consistent: Code on a regular basis. It is important to be consistent when learning a new programming language. Be committed to coding. It can seem daunting but you shouldn’t give up. Increase the time you dedicate to coding as you progress.
  2. Take notes: Writing a few things down with your hands, can help you retain concepts better. This tip is particularly beneficial for programmers who are learning Python to become full time developers. Another advantage of writing stuff down is that it helps you plan out your code before you actually implementing it on computer.
  3. Go interactive: The interactive Python shell serves one of the best learning tools, regardless of whether you are programming for the first time, learning about Python data structures like dictionaries, strings, list, etc or debugging an application. For initializing the Python shell, open your terminal and enter Python or Python3 into the command line and hit Enter.
  4. Practice debugging: Running into bugs is inevitable. The best way to learn basic Python programming skills is to solve the bugs on your own. Take up the challenge of debugging as it allows learning Python in the best possible way
  5. Facilitate learning by being around the right people: Being collaborative can actually bring out the best result while coding, even though it might not seem at first. You should look to surround yourself with people who are also learning Python since this will also help your learning. You can even get helpful tips and tricks from others.
  6. Try Pair programming: The technique of pair programming involves two developers working together on a single code. One programmer serves as the Driver, while the other serves as a Navigator. The driver is the one who actually writes the code, while the Navigator is the one guiding the entire process, giving feedback and reviews and checking the correctness of the code. Pair Programming facilitates mutual learning and provides a fresh perspective on problem solving, debugging, or even writing the code.

There is a huge range of libraries available for you to explore, thanks to the vast open-source community of Python. Following are the best Python libraries for machine learning:

  • SciPy: Contains packages for engineering, Mathematics, and science (manipulation).
  • Scikit-learn: Used primarily for data mining, data science and data analysis.
  • Pandas: Significantly helpful while during data extraction and preparation. Provides high-level data structures.
  • Numpy: Provides efficiency and a lot more with free and fast matrix and vector operations.
  • TensorFlow: It utilizes multi-layered nodes which allow quick training, setting up and deployment of neural networks
  • Keras: is the go-to library to be used for Neural network.
  • Matplotlib: It allows 2D graphing of plot, thus helping data visualization.
  • Pytorch: Pytorch is used if NLP is the objective.

Machine Learning with Python Course in New Jersey, NJ

A view at a map of the United States will tell you that New Jersey is one of the smallest states. But did you know that it is the most thickly populated state in the union? A state that was the site of several decisive battles during the American Revolutionary War, New Jersey has come a long way. Today is one of the most progressive, well defined places in terms of high-tech and banking headquarters. A vibrant place, New Jersey is surrounded on the southeast and south by the Atlantic Ocean, it borders on the north and east by New York State, on the west by Pennsylvania, and on the southwest by Delaware. Interestingly, the first organized baseball game was played in Hoboken, NJ in 1846. It has the highest number of horses per square mile than any other state. This amazing city is full of opportunities for those armed with the right credentials. KnowledgeHut helps you with this by offering a range of courses to choose from including-- PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, Big Data Analysis, Apache Hadoop, and many more.

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