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Machine Learning Course with Python

Know how Statistical Modeling relates to Machine Learning

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

Description

Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world? KnowledgeHut’s comprehensive course will help you go from basic to advanced concepts in Machine Learning using Python, the most popular language in the Data Science space. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each using Python libraries. You will work on real life case studies to get hands-on experience and learn Machine Learning techniques to build predictive models.
 
A career in Machine Learning is much sought after because it defines and shapes the future. Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey.

What You Will Learn

PREREQUISITES
  • Familiarity with statistics
  • Elementary programming knowledge

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

Who Should Attend?

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

Knowledgehut Experience

Instructor-led Experience

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

Curriculum Designed by Experts

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

Learn through Doing

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

Mentored by Industry Leaders

Our support team will guide and assist you whenever you require help.

Advance from the Basics

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

Code Reviews by Professionals

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

Curriculum

Learning Objectives:
  1. Visit basics like mean (expected value), median and mode
  2. Distribution of data in terms of variance, standard deviation and interquartile range
  3. Basic summaries about the data and the measures. Together with simple graphics analysis
  4. Basics of probability with daily life examples
  5. Marginal probability and its importance with respective to datascience
  6. Learn baye's theorem and conditional probability
  7. Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value
Topics covered:
  • 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:
Get a taste of how to start work with data in Python. 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. Learn how to use pandas, a must have package for anyone attempting data analysis in Python. Learn to visualization data using Python libraries like matplotlib, seaborn and ggplot

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

Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models like Classification, Regression, Clustering. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning

Topics covered:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
Learning Objectives:
Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques
Learning Objectives:
  1. Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
  2. Real Life Case Study on Linear Regression
  3. Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
  4. Real Life Case Study on Logistic Regression
  5. Learn how KNN can be used for a classification problem
  6. Real Life Case Study on KNN Classification
  7. Real Life Case Study on Naive Bayesian Classifiers
  8. Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization
  9. Real Life Case Study on SVM
Topics covered:
  • 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 workshop description:
  • 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 covered:
  • 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: 
  1. Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID
  2. Real Life Case Study on Decision Tree
  3. Cover basic ensemble techniques like averaging, weighted averaging & max-voting
  4. Learn about bootstrap sampling and its advantages followed by bagging.
  5. Boost model performance with Boosting
  6. Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees
  7. Real Life Case Study on Random Forests
  8. Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
  9. Real Life Case Study on PCA/Factor Analysis
Topics covered:
  • 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:
  1. Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems. Learn what is IBCF and how is it used in Recommender Engines
  2. Real Life Case Study on building a Recommendation Engine
Topics covered:
  • 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 is willing to buy. And Netflix is a big example. Netflix successfully used recommender system to recommend movies to its viewers. And Netflix 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 Recommender System for a Retail Chain to recommend the right products to its users 

Projects

Project related to this course

Covers projects using Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, K-Nearest Neighbor, Support Vector Machine, Neural Networks

Testimonial

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

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Richard Dsouza

Business Analyst at Valtech from Bangalore, India

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia

The training was very interactive and engaging with the attendees.

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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Faqs

The Course

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data.

Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

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

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

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

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

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

Finance Related

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

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

The Remote Experience

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

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

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