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Module 1- Statistical Learning
Learning Objectives: Visit basics like mean (expected value), median and mode
Distribution of data in terms of variance, standard deviation and interquartile range
Basic summaries about the data and the measures. Together with simple graphics analysis
Basics of probability with daily life examples
Marginal probability and its importance with respective to datascience
Learn baye's theorem and conditional probability
Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value
Module 2- Python for Machine Learning
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
Hands-on: No hands-on
Module 3- Introduction to Machine Learning
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
Hands-on: No hands-on
Module 4- Optimization
Learning Objectives: Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp
Hands-on: No hands-on
Module 5- Supervised Learning
Learning Objectives: Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
Real Life Case Study on Linear Regression
Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
Real Life Case Study on Logistic Regression
Learn how KNN can be used for a classification problem
Real Life Case Study on KNN Classification
Real Life Case Study on Naive Bayesian Classifiers
Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization
Real Life Case Study on SVM
Hands-on workshop description
Module 6- Unsupervised Learning
Learning Objectives:Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering
Real Life Case Study on K-means Clustering
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.
Module 7- Ensemble Techniques
Learning Objectives: Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain,
Standard Deviation reduction, Gini Index,CHAID
Real Life Case Study on Decision Tree
Cover basic ensemble techniques like averaging, weighted averaging & max-voting
Learn about bootstrap sampling and its advantages followed by bagging.
Boost model performance with Boosting
Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees
Real Life Case Study on Random Forests
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
Real Life Case Study on PCA/Factor Analysis
Module 7- Recommendation Systems
Learning Objectives: 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
Real Life Case Study on building a Recommendation Engine
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.
Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.Attended workshop in April 2018
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.Attended workshop in July 2018
Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.Attended workshop in May 2018
The training was very interactive and engaging with the attendees.Attended workshop in June 2018
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!
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.
Yes, KnowledgeHut offers this training online.
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: 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.
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