Machine Learning with Python Rated 4.5/5 based on 11856 customer reviews

Machine Learning Using Python Training in San Diego-CA, United States

A comprehensive Machine learning with Python training course from Zeolearn.

  • 50 hours of Instructor led Training
  • Comprehensive Hands-on with Python programming
  • Learn about Supervised and Unsupervised learning algorithms
  • Master Ensemble Machine Learning

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

Prerequisites
  • Elementary programming knowledge
  • Familiarity with statistics

Who should Attend

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:

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 operation 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

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 

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

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
  • 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 technique - K-Means Clustering and Hierarchical Clustering. Cement the concepts learnt with a 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 kinds. 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 better 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 $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.

Projects related to this course

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build

Read More

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.

Read More

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.

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

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model

Read More
Note:

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.

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.

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.


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.

Have More Questions?

Machine Learning with Python Course in San Diego-CA

Machine learning with Python

The Python machine learning Course in San Diego teaches its registered students on Python packages to use them in Data Analytics applications and Machine Learning. Enrol now to this 40 hours of online training offered by ZeoLearn lecturers who are Python masters. This course is available all through the week and students can register for a convenient batch. This course is a unique blend of theory and practice sessions and ends in students delivering a project done in Python packages reviewed by live industry experts. Missed sessions are coped up from available class recordings or by attending the next live batch session.

One has to be familiar with Python programming background before you register for the Python machine learning training in San Diego. This machine learning with Python online classes in San Diego is apt for Data Analysts.

Course Agenda

There is a total of seven modules in the Python machine learning training curriculum. The Python machine learning training in San Diego has a well-planned curriculum from ZeoLearn for its registered students. It includes python setup, installation, basic operation, functions, numerical computing, Scipy toolkit introduction, numpy, vector matrix, pandas, datasets and data frames in pandas, machine learning basics, model persistence with sci-kit learn, conventions, data visualisations using matplotlip and seaborn.

After submitting the course end project in Python program and getting reviewed, ZeoLearn offers the python machine learning certification in San Diego to its students.

Boon of the ZeoLearn course

The Python machine learning Course in San Diego is a popular one among many other Python courses offered by ZeoLearn. The students learn the importance of data analysis, able to predict future outcomes, able to give good business decisions, apply a predictive algorithm to data, learn how to work with Hadoop distributed file ecosystem, PIG and HIVE.

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