Deep learning with Keras & Tensorflow
Your Instructor
Overall Summary
- 9+ years – Data Scientist / 5+ years – Corporate Training
- Conducted 5000+ hours training on Data Science and related tools
- Author of the book “Practical Business Analytics using SAS”
- Rich industry experience as applied Data Analyst and Data Scientist
- Experience in credit risk model building, market response model building,social media analytics, revenue forecasting and machine learning
- Post Graduate in Applied Statistics and Informatics from IIT Bombay
Course Curriculum
Machine Learning Basics
Available in
days
days
after you enroll
-
Start0. introduction to machine learning recap (0:50)
-
Start1. Regression (5:59)
-
Start2. Regression LAB (7:06)
-
Start3. Logistic regression (7:19)
-
Start4. logit function (3:56)
-
Start5. Building a logistic Regression Line (7:02)
-
Start6. Multiple logistic regression (5:30)
-
Start7. Validation Matrices - Classification Matrix (4:29)
-
Start8. Sensitivity and Specificity (4:43)
-
Start9. Sensitivity vs Specificity (8:39)
-
Start10. Sensitivity Specificity LAB (6:13)
-
Start11. ROC and AUC (5:35)
-
Start12. ROC and AUC LAB (2:49)
-
Start13. The training error (5:42)
-
Start14. Over Fitting and Under Fitting (8:01)
-
Start15. Bias Variance Tradeoff (6:28)
-
Start16. Holdout data validation (1:58)
-
Start17. Hold Out data validation LAB (6:10)
-
StartSession-1. Machine Learning Basics-Case Study
Artificial Neural Networks
Available in
days
days
after you enroll
-
Start1. Introduction to ANN (1:46)
-
Start2. Logistic Regression Recap LAB (4:49)
-
Start3. Decision Boundry - Logistic Regression (4:51)
-
Start4. Decision Boundry - LAB (2:15)
-
Start5. New Representation for Logistic Regression (4:48)
-
Start6. Non Linear Decision Boundry - Problem (3:15)
-
Start7. Non Linear Decision Boundry - Solution (6:57)
-
Start8. Intermediate Output LAB (6:15)
-
Start9. Neural Network Intution (7:35)
-
Start10. Neural Network Algorithm (6:47)
-
Start11. Demo Neural Network Algorithm (6:16)
-
Start12. Neural Network LAB (11:29)
-
Start13. Local Minima and Number of Hidden Layers (5:09)
-
Start14. Digit Recogniser Lab (13:50)
-
Start15. Conclusion (5:36)
-
StartSession-2. Introduction to ANN-Case Study
TensorFlow and Keras
Available in
days
days
after you enroll
-
Start3.1 Introduction to Deep Learning Frameworks (4:33)
-
Start3.2 Key Terms of Tensorflow (9:03)
-
Start3.3 Coding basics in Tensorflow (7:23)
-
Start3.4 Model building intution (1:03)
-
Start3.5 LAB Building Linear and Logistic regression models with Tensorflow (6:38)
-
Start3.6 LAB MNIST model using tensorflow (3:34)
-
Start3.7 Tensorflow shortcomings and Intro to Keras (2:56)
-
Start3.8 LAB MNIST model using Keras (2:38)
-
Start3.9 Tensorflow vs Keras and conclusion (1:14)
-
StartSession-3. TensorFLow and Keras-Case Study
FAQs
When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.