Training will start on 2nd Feb 2019 and end on 17th Feb. Every Saturday and Sunday Morning classes for 3 hours with Live Instructor from Toronto, Canada

**No pre-recorded, every session will be interactive session with instructor**

Where: Virtual

When: 2 Feb 2019 - 17 Feb 2019

I

Instructor: Shailendra Pathak, Chandan Kumar

Content:

General Admission is ($349.00)

Tired of Cookie cutter AI/Machine learning course? We feel your pain where Instructor is worse than Wikipedia?

Don't get fooled by all cookie cutter courses where they only talk about ready to use Libraries and tools, our program is a ground up approach where we teach the Math behind each algorithm and then teach how to code it in Python. No programming experience needed as we will teach you Python as well.

Each module will have task and assignment ( home work) so that you could apply what you learned. All solved assignments are posted next week. We also provide Interview preparation at end of the session.

All students are eligible for life long support via our forums Q/A. https://www.becloudready.com/forum/questions

Who is the target audience?

• No programming experience is required.

• Downloading and installing IDE and tools are included in the course

• High-school Math and willingness to learn

• Setting up python at your machines
• introducing python IDE

• Basics about python and its advantages
• How python handles data
• Lists, arrays and dataframes
• types of data and variables - float, int
• Dictionaries
• Loops and inumeration variables

• Importing csv files
• Writing external files
• Functions
• Intro to libraries like scipy, numpy, pandas etc

• Summarising basic data -- Mean, Median , percentiles etc
• Creating new variables in a dataframe
• group by using SQL in python
• aggreagation using python functions
• Practical example and exercise

• Creating covariance matrix of variables
• correlation and multicollinearity
• Assumptions of OLS and its interpretation
• R-square and goodness of fit
• Practical Development of a OLS model and its performance evaluation

• Introduction to the concept of probability distributions
• Dummy variables
• Difference between OLS and a proababilistic model like logistic
• Hosmer-Lemeshov statistics and KS statistics and concordance
• Practical Development of a logistic model and its performance evaluation

• Difference from parametric modeling
• Classes of machine learning models - supervised learning and unsupervised learning
• What is the logic behind neural networks
• What are classification trees
• Random forests and gradient boosting - an introduction

• Practical development of a RF model using data
• Model parameter interpretation
• Model performance evaluation - AUC and ROC
• Model tuning

• Practical development of a ANN model using data
• Model parameter interpretation
• Model performance evaluation - AUC and ROC
• Model tuning

• Practical development of a GBM model using data
• Model parameter interpretation
• Model performance evaluation - AUC and ROC
• Model tuning

REFUND POLICY

• 100% refund only, if you can't attend it due to medical emergencies with proof.

• If the training has not been conducted for any reason, full refund.

• if you just change your mind then you will be assigned to next batch.

Contact us at training@becloudready.com for any questions. Group discounts are available. Corporate training is also available.

See our course catalog at - https://www.becloudready.com/training