Course Highlights

Personalized one-on-one guidance by experienced industry experts.

Test your subject matter comprehension with assignments and multiple-choice assessment questions.

Take advantage of the experiential learning and build your portfolio in a real-world simulation. 

Reinforce your learning with hands-on exercises and engaging learning material.

Learn concepts from scratch and advance your learning through step-by-step guidance.

Take advantage of the experiential learning and get your code reviewed by experienced programmers. 

The KnowledgeHut Edge

LEARN FROM PRACTITIONERS

The one-on-one guidance from your practitioner instructor, coupled with experiential hands-on projects, exercises, assignments and more provide an immersive learning experience.

RESOURCES TO ENSURE LEARNING NEVER STOPS

Access to extensive resources including the latest articles, ebooks and case studies prepared by industry experts and webinars by practitioners.

FREE ACCESS TO 100+ COURSES

Take advantage of our initiative to support online education during the global pandemic and grow your skills with 100 free e-learning courses.

Develop the skills to move fast and stay ahead.

Outcome-Driven-Learning

Outcome-driven Learning​

Achieve skill-based outcomes from our state-of-the-art immersive learning experience platform.​

Advanced-Analytics

Advanced Insights​

Get a quantified view of your team’s skills and put the right people on the right jobs.​​

Work-like-Experiences

“Work-like” Experiences​

Contextual learning crafted around real-world problem-solving in tech companies.​​

Blended-Learning

Blended Learning​

Get the best of both worlds with live instructor-led training and the flexibility of e-learning.​

Training-by-Practitioners

Training by Practitioners​

Learn from the real-world experience of globally renowned and accredited industry experts.​

Outcome-Driven-Learning

Outcome-driven Learning​

Achieve skill-based outcomes from our state-of-the-art immersive learning experience platform.​

Advanced-Analytics

Advanced Insights​

Get a quantified view of your team’s skills and put the right people on the right jobs.​​

Work-like-Experiences

“Work-like” Experiences​

Contextual learning crafted around real-world problem-solving in tech companies.​​

Blended-Learning

Blended Learning​

Get the best of both worlds with live instructor-led training and the flexibility of e-learning.​

Training-by-Practitioners

Training by Practitioners​

Learn from the real-world experience of globally renowned and accredited industry experts.​

prerequisites

Prerequisites

  • Sufficient knowledge of at least one coding language is required
  • Minimalistic and intuitive, Python is the perfect choice

Course Schedules

corporate-training

Enterprise

Empower your Data Science teams to drive results

Talk to a Learning Advisor to level up your team's Machine Learning skills.

SKILL UP YOUR TEAMS
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 that recommends the right products to its users. 

Frequently Asked Questions

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.

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.

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.

What Learners are saying

  • 5/5

Apr, 2020

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.

Astrid Corduas

Senior Web Administrator

verified-learner
Verified Learner
  • 5/5

Apr, 2020

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Steffen Grigoletto

Senior Database Administrator

verified-learner
Verified Learner
  • 5/5

May, 2020

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.

Marta Fitts

Network Engineer

verified-learner
Verified Learner
  • 5/5

Mar, 2020

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

Ellsworth Bock

Senior System Architect

verified-learner
Verified Learner
  • 5/5

May, 2020

The teaching methods followed by Knowledgehut is really unique. The best thing is that I missed a few of the topics, and even then the trainer took the pain of taking me through those topics in the next session. I really look forward to joining KnowledgeHut soon for another training session.

Archibold Corduas

Senior Web Administrator

verified-learner
Verified Learner
  • 5/5

Jan, 2020

The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

Alexandr Waldroop

Data Architect.

verified-learner
Verified Learner
  • 5/5

Apr, 2020

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

Rafaello Heiland

Prinicipal Consultant

verified-learner
Verified Learner
  • 5/5

Mar, 2020

Knowledgehut is among the best training providers in the market with highly qualified and experienced trainers. The course covered all the topics with live examples. Overall the training session was a great experience.

Garek Bavaro

Information Systems Manager

verified-learner
Verified Learner

Machine Learning with Python Course in Newark, NJ

Machine Learning With Python Training in Newark

Newark is a vital city in New Jersey. It is home to well-known technological institutions and popular companies, both local and global. You can give your career a boost and enhance your professional skills with KnowledgeHut's data analysis using Python course in Newark. Python is one of the most used programming languages among top developers and programmers. The flexibility and simplicity offered by Python make it very easy to use for developing both large and small applications. As one of the fastest-growing programming languages, Python is finding prevalence not only in Newark but worldwide too. Its growing significance in data analysis makes the data analysis training using Python in Newark, offered by KnowledgeHut a sought-after training. The online classes of this course can be taken conveniently from the comforts of your home anywhere in Newark.

A New Alternative

The data analysis using Python course in Newark gives you a deep understanding of why Python is an efficient programming language and its increasing use for machine learning and data analysis purposes. You will then go on to learn the finer aspects of different Python packages, such as Scipy, Pandas, stats model, Numpy, and more. The online training offered by KnowledgeHut will help you get an insight into the various design methodologies that are useful to find data analysis solutions. Some of the design methodologies included in the machine learning using Python course in Newark include clustering, parallel processing, reducing data dimensions, data classification, etc. Some aspects of Python packages included in the online lessons of the machine learning using Python course in Newark include plot configuration in Matplotlib, filtering and aggregating data frames, and signal to process in Numpy and Scipy. Industry experts deliver this training to introduce you to the IPython toolkit and give you a thorough understanding of how this toolkit is used for customization, parallel computing, interactive work, and configuration.

Keeping Ahead of the Curve

The machine learning training using Python is a course that can benefit professionals who are interested in having a thorough understanding of the use of Python in machine learning and data analysis. At the end of this course, you will receive a course completion certificate. IT entrepreneurs, webmasters, scientists, software developers, and analysts can take have an edge over their untrained peers in their organization with this certification.

KnowledgeHut Empowers You

This instructor-led training offered by KnowledgeHut includes project work and hands-on exercises to make you well-versed in the concepts covered in the course. After completing the training successfully, you can apply these concepts to fulfill your organizational needs.

Read More

Other Training Programs