Why should you learn Machine Learning with Python?
Over the past few years, Big Data and its analysis have grown exponentially and changed the way businesses operate. Python has emerged as a strong contender for carrying out predictive analysis on Big Data, because of its syntax, clarity, and easy readability. Python for Machine Learning is a potent programming language that helps build algorithms for smart and intelligent machines that work without human intervention and continuously learn, evolve, and improve by taking in new data. Python based machine learning has found a wide variety of use cases in healthcare, insurance, banking, software, and several other industries. With the machine learning industry growing at an exponential rate, it is a trend that will sweep the world in the near future. Master ML with Python and become part of the technology revolution that will shape the future world.
How do you get started with ML with Python?
If you are new to ML and Python and wish to go from basic to advanced in order to start your career in this field, then this comprehensive course from Zeolearn is just right for you. You will learn all the concepts of Python and ML along with Supervised and unsupervised learning, understand how Statistical Modeling relates to Machine Learning, and learn to build algorithms with practical hands-on exercises. Enrol now and get started on a brilliant career in Machine Learning.
What you will learn
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 the basics of Machine Learning including an introduction to Supervised and Unsupervised 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
Learn about K-means Clustering and Hierarchical Clustering to understand Unsupervised Learning.
6. Ensemble Techniques
Learn to use multiple learning algorithms to obtain better predictive performance.
7. Neural Networks
Understand Neural Networks and apply them to classify image and perform sentiment analysis.
Prerequisites
- Elementary programming knowledge
- Familiarity with statistics
WHO SHOULD ATTEND
- Anyone interested in the field of machine learning and wanting to master essential machine learning algorithms for implementation as real life business solutions
- Software or Data Engineers interested in learning the fundamentals of quantitative analysis and machine learning
Zeolearn Experience
LEARN BY DOING
Immersive hands-on training with a combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.
LIVE & INTERACTIVE
Ask questions, get clarifications, and engage in discussions with instructors and other participants.
MENTORED BY INDUSTRY EXPERTS
Get mentored by Industry practitioners having more than 10 years of experience.
REASON BASED LEARNING
Don’t gain just theoretical or practical knowledge. Understand the WHAT, WHY, and HOW of a subject. Simplify the subject matter and get in-depth comprehension.
CODE REVIEW BY PROFESSIONALS
Get reviews and timely feedback on your assignments and projects from professional developers.
BUILD PROJECTS
We emphasize on learning the concepts through examples and help you in building a portfolio of projects through the course of training.
LIFETIME ENROLMENT
Free lifetime enrolment into any of the upcoming batches to help you refresh the concepts.
CURRICULUM DESIGNED BY EXPERTS
The curriculum goes through multiple levels of design and preparation by the experts to keep the topics/modules relevant to everyday changes in technology.
STUDY EVEN FROM REMOTE LOCATIONS
Learn to use collaborative mediums to share opinions and improve your coding skills with assistance from the instructors and other participants.
Curriculum
Learning Objectives:
Learn the basics of statistics like mean, median and mode, distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses, probability, Bayes’ theorem etc.
Topics:
- Statistical analysis concepts
- Descriptive statistics
- Introduction to probability and Bayes theorem
- Probability distributions
- Hypothesis testing & scores
Hands-on:
Learn to implement statistical operation in Excel
Learning Objectives:
Learn How to define variables, sets, and conditional statements. You will also learn the purpose of functions and how to operate on files to read and write data in Python along with a range of tools and techniques.
Topics:
- Python Overview
- Pandas for Pre-Processing and Exploratory Data Analysis
- Numpy for Statistical Analysis
- Matplotlib & 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 among other techniques.
Topics:
- Machine Learning Modelling Flow
- How to treat Data in ML
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting & Underfitting
Learning Objectives:
Get an understanding of various optimisation techniques such as Batch Gradient Descent, Stochastic Gradient Descent, ADAM, and RMSProp.
Topics:
- Maxima and Minima
- Cost Function
- Learning Rate
- Optimization Techniques
Learning Objectives:
Learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies. You will also learn hyper-parameters tuning like learning rate, epochs, momentum, and class-balance, concepts of Linear and Logistic Regression along with other techniques.
Topics:
- Linear Regression
- Case Study
- Logistic Regression
- Case Study
- K-NN 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 techniques like K-means Clustering and Hierarchical 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:
Learn the ensemble techniques which enable you to build machine learning models including Decision Trees for regression and classification problems, Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID along with other techniques.
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 kinds. With the ingredient composition known, we can build a model to predict 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 better than a single model.
- Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.
Learning Objectives:
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 $1billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build a Recommender System for a Retail Chain to recommend the right products to its users.
WHY CORPORATES COUNT ON ZEOLEARN TO SKILL UP THEIR TEAMS
Zeolearn is on a mission to help organizations transform their workforce and prepare for the future of work. We aim to enable our clients to build self-organizing and high-performing teams through world-class workshops, while building in-house leadership, talent, culture, and sustainable multiple practices and capabilities with the help of our Training, Coaching and Consulting engagements.
- Global presence in well over 60 countries
- Consultants, trainers, coaches with experience across multiple domains: BFSI, Telecom, Retail, Automobiles, E-Commerce, more
- One-Stop Learning Partner offering a wide range of 250+ courses
- Accelerating digital talent transformation and enabling future ready tech talent
- Multilingual trainers at nearest shore local base locations for ease of reach and coordination
- Complete learning eco-system with pre and post-course preparation and support materials, e-learning, video archives, question banks, and many other learning aids
- Multimodal delivery system including Classroom, Live Virtual Classroom (LVC), E-Learning, Customized Blended Learning, In-House LMS, and more
- Consulting, Coaching and Staffing services going beyond training and certification to support complete transformation
- 500+ Clients
-
![Bosch logo]()
-
![Cognizant logo]()
-
![Capgemini logo]()
-
![Deloitte logo]()
-
![HP logo]()
-
![Honeywell logo]()
FAQs
The Course
After completing our course, you will know:
- About Python, its basic data structures, objects, and operations
- Optimization techniques, supervised and unsupervised learning
- Basic statistics and machine learning fundamentals
- Recommendation systems
- Common classifier algorithms such as Decision Trees, random Forests etc.
ML is hot! Machine learning is used on data with the intention of arriving at useful insights and supporting business decision making. ML has changed the way organizations operate and is applicable in a variety of fields from retail and healthcare to entertainment and hospitality. Python on the other hand is a multi-domain, high-level, programming language highly preferred for its easy readability, vast array of libraries and reliability. Python has emerged as the most convenient language for data analysis and machine learning. If you want to pursue a career in ML, then learning Python is a must as ML with Python has several advantages and is what organizations will use going forward.
- Elementary programming knowledge
- Familiarity with statistics
This course is for anyone wanting to apply ML algorithms to real life business problems. Also, Software or Data Engineers interested in learning quantitative analysis and ML will benefit from this course.
Machine Learning with Python workshop experience
The workshops at Zeolearn are always interactive, immersive and intensive hands-on programs. There are 3 modes of Delivery and you can select based on the requirements -
- Online Classroom training: Learn from anywhere through the most preferred virtual live instructor led training with the help of hands-on training and interactive sessions
- One-to-One Training: You can enrol for one-to-one Machine Learning with python classroom training session with our expert trainer at a preferred time. With this mode, you can customize your curriculum to suit your learning needs.
- Team/Corporate Training: In this type of training, an organization can nominate their entire team for online or classroom training. You can customize your curriculum to suit your learning needs and also get post-training expert’s support to implement Machine Learning with Python concepts in the project.
We follow the below mentioned procedure for all the training programs by dividing the complete workshop experience into 3 stages i.e Pre, Workshop and Post. This is a tried and tested approach using which we have been able to upskill thousands of engineers.
Pre-training
Before the start of training program, we make sure that you are ready to understand the concepts from Day 1. Hence, as a process of preparation for the intensive workshop, we provide the following -
- Reference articles/ Videos and e-books
- 2-4 hrs of training on pre-requisites - to make you workshop ready
- Pre-Workshop Assessments - to assess and benchmark
- Environment set-up docs
During Training
The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team -
- PPT and Code Snippets used in the class
- Learners Guide or E-book
- Projects / Case Studies
- Assessments / Lab exercises
- Quizzes and Polls
- Study Plans - To structure your learning.
Post Training
We don’t just impart skills but also make sure that you implement them in the project. And for that to happen, we are always in touch with you either through newsletters or webinars or next version trainings. Some of the post-training deliverables lined-up for you are -
- Project assistance with mentor
- Course Recordings
- Access to Alumni Network
- Additional workshops on advanced level concepts
- Regular emails/newsletters on Blogs/Tutorials and other informational content
Yes, Zeolearn has well-equipped labs with the latest version of hardware and software. We provide Cloudlabs to explore every feature of Machine Learning with Python through hands-on exercises. Cloudlabs provides an environment that lets you build real-world scenarios and practice from anywhere across the globe. You will have live hands-on coding sessions and will be given practice assignments to work on after the class.
At Zeolearn, we have Cloudlabs for all the major categories like Web development, Cloud Computing, and Data Science.
As a Machine Learning Professional, you will be required to use your skills in different situations to solve different problems. The best way to showcase your skills is to work on a variety of projects like the ones below and sharpen your problem-solving skills. These projects are samples of the kind of projects you will work on during your training.
PROJECT-1
- TITLE - Predict Property Pricing using Linear Regression
- DESCRIPTION - 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.
PROJECT- 2
- TITLE - Classify good and bad customers for banks to decide on granting loans.
- DESCRIPTION - 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.
PROJECT-3
- TITLE - Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.
- DESCRIPTION - 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 or non-biodegradable.
PROJECT-4
- TITLE - Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.
- DESCRIPTION - 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.
PROJECT-5
- TITLE - Predict quality of Wine
- DESCRIPTION - 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).
This course is delivered by industry-recognized experts who would be having more than 10 years of real-time experience in Machine Learning with Python.
Not only will they impart knowledge of the fundamentals and advanced concepts, they will provide end-to-end mentorship and hands-on training to help you work on real-world projects with regards to Machine Learning with Python.
Once you register for the course you will be provided with system requirements and lab setup document which contains detailed information to prepare the environment for the course.
To attend Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below -
Hardware requirements:
- Minimum 4 GB ram
- 500 GB hard disk
Software Requirements:
- Ananconda 3.5 version
- Windows/Mac OS
- Visual studio editor
Machine Learning with Python Online Training Experience
All our training programs are quite interactive and fun to learn with plenty of time spent on lot of hands-on practical training, use case discussions and quizzes. Our instructors also use an extensive set of collaboration tools and techniques which improves your online training experience.
This will be live interactive training led by an instructor in a virtual classroom.
You will receive a registration link to your e-mail id from our training delivery team. You will have to log in from your PC or other devices.
Yes, for all the online public workshops there would be participants logging in from different locations.
In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: http://www.zeolearn.com/contact-us, or send an email to hello@zeolearn.com.
We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, we will facilitate a one-on-one discussion session with our trainers.
If you miss a class, you can access the class recordings anytime from our LMS. At the beginning of every session, there will be a 10-12 minute recapitulation of the previous class. You can watch the online recording and clarify your doubts at that time. You may need to login 15 minutes before the main lecture begins to avail this facility.
We also have a Free Lifetime enrollment for most of our courses. In case you miss out a class, you can also enroll for another complete workshop or only for a particular session.
Finance related
Typically, Zeolearn’s training are exhaustive and the mentors help you out in understanding the in-depth concepts.
However, if you find it difficult to cope, you may discontinue within the first 4 hours of training and avail a 100% refund. Learn more about our refund policy here.
Zeolearn offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.
Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary upto 50% of the course fees.
To avail scholarships, please get in touch with us at hello@zeolearn.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, the panel of experts take a decision on the Grant. The entire process could take around 7 to 15 days.
Yes, we do have instalment options available for the course fees. To avail instalments, please get in touch with us at hello@zeolearn.com. The team shall explain how the instalments work and would provide the timelines for your case.
Usually we allow payment in 2 to 3 instalments but have all to be paid before you complete the course.
Projects related to this course
Predict Property Pricing using Linear Regression
With attributes describing various aspects of residential homes, you are required to build
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.
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.
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
Predict quality of Wine
Wine comes in various types. With the ingredient composition known, we can build a model
Covers projects using Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, K-Nearest Neighbor, Support Vector Machine, Neural Networks, CNN, RNN, Adaboost, GBM, Random Forest etc.
Have More Questions?
Machine Learning with Python Course in Chicago-IL
Machine learning with Python
ZeoLearn, a registered institute which has numerous certified courses worldwide, offers Python Machine Learning Course in Chicago, the United States 40 hours of advanced online training offered by lecturers who have substantial experience in Python. Python Machine Learning training in Chicago throughout the week and students can enrol for any convenient class type based on their availability and facilities. Students who register for the Python Machine Learning online training in Chicago are expected to have known basic programming knowledge in Python. Students can easily enrol for this online course and must spend at least 10 to 12 hours of weekly timings for self-study, online classes and project completion. If any particular session is missed in this online training, students can refer to the class recordings or take up another live batch session of python machine learning course in Chicago.
What you Learn!
The Machine learning with python online classes in Chicago has a well-framed curriculum by Python experts. The modules of this workshop teach its students about Python setup, numerical computing using Python, Introduction to scipy, introduction to pandas, basics of machine learning, machine learning with sci-kit learn and data visualisation.
Students have to submit a project at the end of the python machine learning training in Chicago as a means to get familiar with all the concepts learnt in this online course. It is a live project working with Python packages to perform data analysis. Instructors and fields experts review it. Once approved, ZeoLearn offers python machine learning certification in Chicago.
Key Benefits of the Course and Academy
The python machine learning certification in Chicago is recognised in the field of data science and helps the students get a glittering career as a data analyst. ZeoLearn's mentor-driven courses not only teach them theory but also offers practice sessions, demos and real exposure. The key feature about ZeoLearn is that, unlike other academics, it provides the facility of staying in constant touch with the experts and follow up on the updating features and technologies in Python and data science.
Other Training
- Master Sas classroom training in Perth
- Facebook Video Tab
- Data Science With Python training in Bangalore
- Node Js classroom training in Ahmedabad
- Asp Dot Net training in Hyderabad
- Python classes in San Diego
- Master Turbogears training in Perth
- Perl Deep Dive classes in Austin
- Javascript course
- Master R Programming classroom training in Toronto
- Facebook Video Tab