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

A comprehensive Machine Learning with Python training course

  • 48 hours of Instructor-Led Training
  • Comprehensive Hands-on with Python programming
  • Learn about Supervised and Unsupervised learning algorithms
  • Master Ensemble Machine Learning
View Schedules GET FREE DEMO

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. 

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.

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

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
Talk to scrum expert

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.

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.

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: https://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?

Mail Us support@zeolearn.com

Machine Learning with Python Course in San Francisco-CA

Machine learning with Python

Organizations, these days depend a lot on the data generated to get demand forecasts, consumer behaviour analysis, which helps them to be better prepared with their inventory, production cycles etc. The study of this big data generated has changed the way organisations operate and Python helps to achieve that.

Python is the most popular programming language for predictive analysis of big data. The ease in use makes Python as one of the most preferred programming machine languages in the world. In the past few years, Python has progressed immensely and has created analytics packages such as Matplotlib, Scipy, Pandas, Numpy which has made analysing data with Python very easy. Zeolearn academy offers Python Machine Learning Training in San Francisco.

Benefits of the course:

Python Machine Learning Course in San Francisco is devised in a very comprehensive way, and the participants become skilled in numerical computing using Python and also get introduced to Panda, Scipy and basics of machine learning. The lectures for python machine learning training in San Francisco are given online by certified world class instructors. The participants accomplish the following at the end of the python machine learning course in San Francisco:

 

  • Application of predictive algorithms to data
  • Advanced data structures and algorithms available in Scipy, Numpy and Pandas
  • Understanding of Python concerning Hadoop, PIG and Hive
  • Using analytics libraries with Hadoop Map Reduce, PIG and HIVE
  • Develop analytical apps with Python advanced concepts

Mode of delivery:

The course is delivered by certified trainers, and any query is satisfactorily explained.

 

  • Machine Learning with Python Online Classes in San Francisco is for 40 hours conducted on weekdays and weekends.
  • The online class has a proper mix of demos, workshops, practice sessions and live projects which helps the participants to understand the basics of machine learning better.
  • Exclusive one to one and corporate or team training are also available.
  • The institute gives lifetime online access to study material, from the time the participants
  • The fee for an online classroom for is USD 2199

 

 

Certification:

The academy gives Python Machine Learning Certification in San Francisco on successful completion of the course.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Read More

Other Training