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

Know how Statistical Modeling relates to Machine Learning

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

Description

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

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

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

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

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

Why Learn Machine Learning from Knowledgehut?

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

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

What is Machine Learning?

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

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

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

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

2) Nvidia defines Machine Learning as:

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

3) McKinsey & Co. defines Machine Learning as:

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

4) The University of Washington defines Machine Learning as:

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

5) Carnegie Mellon University defines Machine Learning as:

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

Origin of Machine Learning through the years

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How does Machine Learning work?

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

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

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

Advantages of Machine Learning

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

Industries using Machine Learning

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

  1. Financial services:

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

  1. Government:

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

  1. Health Care:

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

  1. Oil and Gas:

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

  1. Retail:

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

What is the future of Machine Learning?

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

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

What You Will Learn

PREREQUISITES

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

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

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

Who Should Attend?

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

Knowledgehut Experience

Instructor-led Live Classroom

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

Curriculum Designed by Experts

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

Learn through Doing

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

Mentored by Industry Leaders

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

Advance from the Basics

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

Code Reviews by Professionals

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

Curriculum

Learning Objectives:

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

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

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

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

Hands-on: No hands-on

Learning Objectives :

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

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

Hands-on: No hands-on

Learning Objectives:

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

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

Hands-on: No hands-on

Learning Objectives:

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

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

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

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

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

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

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

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

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

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

View Profile

Projects

Predict Property Pricing using Linear Regression

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

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

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

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

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

Read More

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

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

Read More

Predict quality of Wine

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

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

Learn Machine Learning

Learn Machine Learning in 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

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.

reviews on our popular courses

Review image

KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.

Merralee Heiland

Software Developer.
Attended PMP® Certification workshop in May 2018
Review image

I liked the way KnowledgeHut framed the course structure. The trainer was really helpful and completed the syllabus on time and also provided live examples.  KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Jules Furno

Cloud Software and Network Engineer
Attended Certified ScrumMaster®(CSM) workshop in May 2018
Review image

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and supported throughout the process.

Astrid Corduas

Telecommunications Specialist
Attended Agile and Scrum workshop in May 2018
Review image

I was totally surprised by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. Training session changed my way of life.

Matteo Vanderlaan

System Architect
Attended Agile and Scrum workshop in May 2018
Review image

My special thanks to the trainer for his dedication, learned many things from him. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training.

Prisca Bock

Cloud Consultant
Attended Certified ScrumMaster®(CSM) workshop in May 2018
Review image

It is always great to talk about Knowledgehut. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and liked the way of teaching. My special thanks to the trainer for his dedication, learned many things from him.

Ellsworth Bock

Senior System Architect
Attended Certified ScrumMaster®(CSM) workshop in May 2018
Review image

I am really happy with the trainer because the training session went beyond expectation. Trainer has got in-depth knowledge and excellent communication skills. This training actually made me prepared for my future projects.

Rafaello Heiland

Prinicipal Consultant
Attended Agile and Scrum workshop in May 2018
Review image

The trainer took a practical session which is supporting me in my daily work. 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.

Marta Fitts

Network Engineer
Attended PMP® Certification workshop in May 2018

Faqs

The Course

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

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

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

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

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

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

Finance Related

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

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

The Remote Experience

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

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

Have More Questions?

Machine Learning with Python Course in 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.