Machine-Learning with Python using scikit-learn & tensorflow Training

Machine-Learning with Python using scikit-learn & tensorflow Course:
Machine Learning is one of those technology which promises to change the world & it's not too late to announce that it has already started. This machine learning course conducted in Pune & many other places is designed in such a way that you no longer will be a viewer of this change, rather be a part of it. This machine learning training program starts from fundamental aspects of machine learning & goes till deployment. Right from creating machine learning business problem to data wrangling to pre-processing followed by model training all the aspects will be covered. Projects are integral part of this machine learning training program & regular hackathons are conducted in Pune. For other places, online hackathons are conducted. We have also conducted corporate machine learning training with assessment at many of our client office location as a part of our continues engagement. Focus is given on the stats, maths as well as making developers production ready. Some of the projects we do as a part of machine learning course includes - credit risk, sales prediction, data mining, healthcare projects, insurance predictions, cloth design pattern, text analysis for named entity, image classification, sentiment analytics, time series and many more. Graded assignments are integral part of the machine learning course. After completion of this machine learning training program a certification provision is also there. These certification is accredited by different companies & candidates who have cleared this are absorbed after a few screening rounds by the companies. This machine learning course is updated every alternative month as per input from different corporate. Our services are present all over the India majorly covering notable cities like Bangalore, Hyderabad, Kolkata, Kochi, Chennai, Delhi, Mumbai, Gurgaon, Noida and also in most of the non-major cities. Write to us if trainings are needed info@zekelabs.com Some of our clients are - GE - Pune, IBM - Bangalore, Pune, Kolkata, Cerner - Bangalore, Epsilon - Bangalore, TCS - Pune, Hyderabad, CISCO - Pune, Bangalore.

Machine-Learning with Python using scikit-learn & tensorflow Course Curriculum

1. The Fundamentals of Machine Learning

Chapter 1The Machine Learning Landscape

What Is Machine Learning?

Why Use Machine Learning?

Types of Machine Learning Systems

Main Challenges of Machine Learning

Testing and Validating

Exercises

2. End-to-End Machine Learning Project

Working with Real Data

Look at the Big Picture

Get the Data

Discover and Visualize the Data to Gain Insights

Prepare the Data for Machine Learning Algorithms

Select and Train a Model

Fine-Tune Your Model

Launch, Monitor, and Maintain Your System

Try It Out!

Exercises

3. Classification

MNIST

Training a Binary Classifier

Performance Measures

Multiclass Classification

Error Analysis

Multilabel Classification

Multioutput Classification

Exercises

4. Training Models

Linear Regression

Gradient Descent

Polynomial Regression

Learning Curves

Regularized Linear Models

Logistic Regression

Exercises

5. Support Vector Machines

Linear SVM Classification

Nonlinear SVM Classification

SVM Regression

Under the Hood

Exercises

6. Decision Trees

Training and Visualizing a Decision Tree

Making Predictions

Estimating Class Probabilities

The CART Training Algorithm

Computational Complexity

Gini Impurity or Entropy?

Regularization Hyperparameters

Regression

Instability

Exercises

7. Ensemble Learning and Random Forests

Voting Classifiers

Bagging and Pasting

Random Patches and Random Subspaces

Random Forests

Boosting

Stacking

Exercises

8. Dimensionality Reduction

The Curse of Dimensionality

Main Approaches for Dimensionality Reduction

PCA

Kernel PCA

LLE

Other Dimensionality Reduction Techniques

Exercises

Neural Networks and Deep Learning

9. Up and Running with TensorFlow

Installation

Creating Your First Graph and Running It in a Session

Managing Graphs

Lifecycle of a Node Value

Linear Regression with TensorFlow

Implementing Gradient Descent

Feeding Data to the Training Algorithm

Saving and Restoring Models

Visualizing the Graph and Training Curves Using TensorBoard

Name Scopes

Modularity

Sharing Variables

Exercises

10. Introduction to Artificial Neural Networks

From Biological to Artificial Neurons

Training an MLP with TensorFlow’s High-Level API

Training a DNN Using Plain TensorFlow

Fine-Tuning Neural Network Hyperparameters

Exercises

11. Training Deep Neural Nets

Vanishing/Exploding Gradients Problems

Reusing Pretrained Layers

Faster Optimizers

Avoiding Overfitting Through Regularization

Practical Guidelines

Exercises

12. Distributing TensorFlow Across Devices and Servers

Multiple Devices on a Single Machine

Multiple Devices Across Multiple Servers

Parallelizing Neural Networks on a TensorFlow Cluster

Exercises

13. Convolutional Neural Networks

The Architecture of the Visual Cortex

Convolutional Layer

Pooling Layer

CNN Architectures

Exercises

14. Recurrent Neural Networks

Recurrent Neurons

Basic RNNs in TensorFlow

Training RNNs

Deep RNNs

LSTM Cell

GRU Cell

Natural Language Processing

Exercises

15. Autoencoders

Efficient Data Representations

Performing PCA with an Undercomplete Linear Autoencoder

Stacked Autoencoders

Unsupervised Pretraining Using Stacked Autoencoders

Denoising Autoencoders

Sparse Autoencoders

Variational Autoencoders

Other Autoencoders

Exercises

16. Reinforcement Learning

Learning to Optimize Rewards

Policy Search

Introduction to OpenAI Gym

Neural Network Policies

Evaluating Actions: The Credit Assignment Problem

Policy Gradients

Markov Decision Processes

Temporal Difference Learning and Q-Learning

Learning to Play Ms. Pac-Man Using Deep Q-Learning

Exercise

Frequently Asked Questions

What are the modes of training for "Machine-Learning with Python using scikit-learn & tensorflow" course?

This "Machine-Learning with Python using scikit-learn & tensorflow" course is an instructor-led training (ILT). The trainer travels to your office location and delivers the training within your office premises. If you need training space for the training we can provide a fully-equipped lab with all the required facilities. The online instructor-led training is also available if required. Online training is live and the instructor's screen will be visible and voice will be audible. Participants screen will also be visible and participants can ask queries during the live session.

Will I be provided with any study material during the "Machine-Learning with Python using scikit-learn & tensorflow" training?

Participants will be provided "Machine-Learning with Python using scikit-learn & tensorflow"-specific study material. Participants will have lifetime access to all the code and resources needed for this "Machine-Learning with Python using scikit-learn & tensorflow". Our public GitHub repository and the study material will also be shared with the participants.

What is the pedagogy of zekeLabs?

All the courses from zekeLabs are hands-on courses. The code/document used in the class will be provided to the participants. Cloud-lab and Virtual Machines are provided to every participant during the "Machine-Learning with Python using scikit-learn & tensorflow" training.

What is the duration of this course?

The "Machine-Learning with Python using scikit-learn & tensorflow" training varies several factors. Including the prior knowledge of the team on the subject, the objective of the team learning from the program, customization in the course is needed among others. Contact us to know more about "Machine-Learning with Python using scikit-learn & tensorflow" course duration.

What would be the venue for the "Machine-Learning with Python using scikit-learn & tensorflow" training?

The "Machine-Learning with Python using scikit-learn & tensorflow" training is organised at the client's premises. We have delivered and continue to deliver "Machine-Learning with Python using scikit-learn & tensorflow" training in India, USA, Singapore, Hong Kong, and Indonesia. We also have state-of-art training facilities based on client requirement.

Who is the trainer for "Machine-Learning with Python using scikit-learn & tensorflow" training?

Our Subject matter experts (SMEs) have more than ten years of industry experience. This ensures that the learning program is a 360-degree holistic knowledge and learning experience. The course program has been designed in close collaboration with the experts working in esteemed organizations such as Google, Microsoft, Amazon, and similar others.

Can we customize this course based on our requirements?

Yes, absolutely. For every training, we conduct a technical call with our Subject Matter Expert (SME) and the technical lead of the team that undergoes training. The course is tailored based on the current expertise of the participants, objectives of the team undergoing the training program and short term and long term objectives of the organisation.

How can I reach out to you if I have any other queries regarding the "Machine-Learning with Python using scikit-learn & tensorflow" course?

Drop a mail to us at info@zekelabs.com or call us at +91 8041690175 and we will get back to you at the earliest for your queries on "Machine-Learning with Python using scikit-learn & tensorflow" course.

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Machine-Learning with Python using scikit-learn & tensorflow Training

Machine-Learning with Python using scikit-learn & tensorflow Course:

Machine-Learning with Python using scikit-learn & tensorflow Course Curriculum

Frequently Asked Questions

Recommended Courses

Big Data Processing with PySpark

Artificial Intelligence

Data Science & Machine Learning Foundation

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