$200!! 2 Day Data Science: Machine/Deep Learning Artificial Intelligence to Big Data ML pipe line Training at Erudition Inc.

Erudition Inc. is offering Data Science Primer: Machine/Deep Learning AI to Big Data Training on November 10-11 2018.

Our mission: Erudition Inc.'s mission is to provide hands-on education in emerging technologies to masses at no cost or very affordable rate. What is life's objective at the end of the day? Life is fleeting, and permanence in this world is something we all strive for. The best way to achieve permanence is through sharing knowledge.

Doesn't matter if you are aligned to left brain or right brain you can join Erudition in your Emerging Technologies training!! We will also provide interview help and placement services. This course is 80% hands on!!

You will get USD300.00 worth of books for Free!! That are written by Bhairav Mehta and other authors.

Time Date : November 10-11 2018 8AM to 6PM

Location: Biltmore Hotel, 2151 Laurelwood Rd, Santa Clara, CA 95054

Instructor: Bhairav Mehta

Bhairav Mehta is Data Science Manager at Apple Inc. He has 15 years experience in Analytics and Data Science space at various fortune 100 companies. Bhairav Mehta is academician and tenured faculty at various Bay area Universities. Bhairav Mehta has taught 1000s of students in AI, ML and Big Data technologies over last 5 years. He also gives talks at Association of Computing Machinery (ACM), IEEE Computer Science society, Global Big Data and AI conferences, Open Data science conference and other forums. Bhairav Mehta has 5 graduate degrees from top institutes: MS Computer Science (GeorgiaTech), MBA (Cornell University), MS Statistics (Cornell University) etc.

Linkedin Profile: https://www.linkedin.com/in/mehtabhairav/­

Erudition Website: https://bit.ly/2N72mhQ

Bhairav Mehta talk videos and other conference proceedings: https://bit.ly/2MrMbGV­

What is this course about?

Data Science is most lucrative careers with unlimited opportunity today!! Data Science career requires key Data Engineering and Machine Learning, Artificial Intelligence and Deep Learning skills to be successful. This training will teach you all skills needed to be a successful Data Scientist. Its not only algorithms or tools but an end to end skills needed with latest advancements in the industry today. Project and Learning by examples is at core of this training. What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Schedule:

Day 1

1. Overview and Introduction

2. Data Science Introduction

3. Data Infrastructure Introduction

4. Distributed and In-memory computing

5. Hadoop Distributed File System and Spark In-Memory computing. RDDs, HDFS, Commodity Cloud Infrastructure

6. Hadoop and Spark Labs on Cloud

7. Rest APIs and Data pipelines

8. Data Formats JSON, BSON, XML data structures, Data pipelines and parsing routines in Python

9. Machine Learning Introduction

10. Unsupervised and Supervised algorithms introduction

11. Unsupervised algorithms: Clustering, Discriminant Analysis, Dimensionaility Reduction techniques like PCA/ LDA/FA/ DA

12. Supervised algorithms: Classifiers, Regression, Ensemble algorithms like Random Forest

13. Diagnostic tools for evaluating models: ROC Curves, Lift Curve, Bias Variance Tradeoffs, F1 scores etc.

14. Machine Learning Labs with Python on GPU/TPU Cloud @ Google Colab and/or Kaggle Kernels

15. Building Machine Learning algorithm deployment at Scale using state of the art Cloud based tools.

16. AI ML Hardware Acceleration using GPU, TPU and FPGA

17. Data Science Pipelines with Big data and Cloud based tools for storage and compute

18. Neural Network Introduction

19. Gradient Descent, Error function

20. Training Neural Network

21. Tensorflow, Keras, Theano, Lasagne, Torch, Caffe introduction

22. Tensorflow Labs

Day 2

1. Tensorflow and Keras labs for simple classification and clustering. Supervised and unsupervised.

2. Regularization Intro

3. Neural Network Architecture and Hyper Parameter tuning

4. Convolution Neural Network

5. Labs with Tensorflow and CNN, CNN with Regularization

6. CNN in Tensorflow

7. Weight Initialization

8. Auto Encoders

9. Transfer Learning

10. ImageNet, LeNet, Alexnet, VGGNet, Inception, ResNet

11. Object Detection

12. Auto Encoder and Transfer learning labs

13. Image Segmentation

14. Face Detection

15. Image Classification

16. Labs with Keras and TensorFlow

17. Advanced Object Detection methods: R-CNN, F R-CNN, YOLO, Mask R-CNN, Labs

18. Labs for Image Classification

19. Labs for Image Segmentation and Face detection

20. Recurrent Neural Network Intro (RNN)

21. Long Short term Memory (LSTM)

22. Motivation for learning RNN and LSTM

23. Simple RNN and LSTM labs for Time Series

24. Cloud based tools for doing object detection, image classification and applications of CNN

25. RNN-LSTM Labs continued

26. Natural Language Processing (NLP)

27. Work2Vec, Word Embedding, PCA and T-SNE for Word Embedding

28. NLP Labs

29. Sequence to Sequence LSTM Chatbots and LSTM based Text Generation

30. Review and Introduction to advanced concepts in Neural Networks e.g. Reinforcement Learning, Generative Adversarial Networks, Autonomous Driving car etc.

Intended Audience

Programmers, analysts, managers, investors, enthusiast pretty much anyone technically curious about deploying Machine Learning.

TextBooks and Slides:

Slides (To be delivered separately)

Following material will be provided by Bhairav Mehta (It is worth USD300.00)

12 workshop guides will be provided

• Machine Learning Algorithms
• Deep Learning and Python CNN
• Deep Learning and Natural Langugage Processing
• LSTM and RNN
• Machine Learning with Python
• Big Data tools Distributed and In Memory computing

Additional Books are on specific topics in AI / ML and Big Data. Each topic includes codes and explanation step-by-step

Thanks

Bhairav Mehta

URL: https://bit.ly/2N72mhQ

Phone# 4086608118

Email: eruditionbayarea@gmail.com