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Advanced Deep Learning using Python in Mumbai

Gain an in-depth understanding about deep learning algorithms and their development using Python.

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies Python

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

Automating machines to process information and replicate knowledge-levels like humans is the primary focus of deep learning and neural networks. Deep learning simplifies the process of gathering, assessing, and interpreting data sets for engineers, and offers a proactive solution to foreseeing events by studying patterns. Engineers can develop and study versatile deep learning codes using Python - making it a fundamental skill in the job industry. 

This course is focused on enriching engineers with theoretical and practical knowledge on the following: 

  • Feed forward neural networks
  • Activation functions
  • Deep learning algorithms
  • Convolutional neural networks
  • Recurrent neural networks
  • Natural language processing

You will also get to complete projects requiring extensive hands-on work with Python according to modern trends in the industry.

Course Syllabus in Mumbai

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 1 - Artificial Neural Network (Feed Forward Neural Network)

This week will cover

  • Neural networks 
  • Different architectures of Neural Networks
  • Importance of Neural Networks
  • Hyperparameters in Neural Networks
  • Different types of Gradient descent methods

Week 2 - Activation Functions in Neural Networks

This week will cover

  • Conic sections
  • Hyperbolic trigonometric functions
  • Sigmoid activation function
  • Tanhx activation function
  • Relu activation function
  • Softmax activation function

Week 3 - Deep Learning

This week will cover

  • Deep learning terminologies
  • Nomenclature
  • Order of vectorized forms
  • Forward propagation derivation with 1 layer
  • Back propagation derivation with 1 layer
  • Batch size, iteration and epoch

Week 4 - Evaluation of Models

This week will cover

  • Underfitting
  • Overfitting
  • Lasso regularization
  • Ridge regularization
  • Elastic Net regularization

Week 5 - Improvising the Model

This week will cover

  • Ensemble methods
  • Sparse and convex functions
  • Bagging to avoid overfitting
  • Boosting to avoid underfitting
  • Stacking to avoid underfitting

Week 6 - Optimizers

This week will cover

  • Frobenius norm regularization
  • Data augmentation
  • Early stopping
  • Adam optimizer
  • Tensorflow 2.0

Week 7 - Convolutional Neural Network (CNN) - Part 1

This week will cover

  • Basics of CNN
  • Edge detection
  • Padding
  • Stride
  • Simple CNN
  • Difference between CNN & ANN

Week 8 - Convolutional Neural Network (CNN)- Part 2

This week will cover

  • Pooling layers
  • Transfer learning
  • Examples of CNN architecture
  • Combination of different Neural network architecture
  • CNN in Python

Week 9 - Recurrent Neural Network (RNN) - Part 1

This week will cover

  • RNN Model
  • Different types of RNN
  • Gradients in RNN
  • Back propagation
  • Difference between RNN & ANN

Week 10 - Recurrent Neural Network (RNN) - Part 2

This week will cover

  • Gated Recurrent Unit (RNN)
  • Long short term memory (LSTM)
  • Bidirectional RNN
  • RNN Implementation in Python

Week 11 - Basics of Natural Language Processing (NLP)

This week will cover

  • Stop words
  • Stemming
  • Lemmatization
  • Word2vec
  • Implementation of word2vec in Python

Week 12 - End-to-End ML Project Steps

This week will cover

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects in Mumbai

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Logistic Regression and Gradient Descent

In this project, you will perform logistic regression and gradient descent on a given dataset using Python. 

ANN, CNN and RNN

In this project, you will have to work on prediction of machine failure for a given dataset using ANN (Hyperparameters is completely dependent on individuals to come up with the best model).

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Become Skill-full With the Advanced Deep Learning Using Python Course in Mumbai

The Skill-Lync's 'Advanced Deep learning Using Python' is a three-month deep learning certification for Mumbai students. This course concentrates on all prominent deep learning, ML & AI practical concepts, real-world applications, and current industry trends while leveraging Python as a tool.

It is an industry-experts-led advanced deep learning course designed to help learners comprehend the fundamental-to-advanced level deep learning concepts, and approaches. It teaches how deep learning algorithms are deployed to solve real-world problems. Thus, it would help you secure high-demand job roles in the emerging AI industry.

The curriculum consists of a twelve-week industry-oriented study plan covering crucial deep learning & ML concepts, including Artificial Neural networks, evaluation and improvisation of deep learning models. Convolutional Neural networks (CNN), Recurrent Neural networks, Natural Language Processing, and much more are also a part of the curriculum. It also comprises two comprehensive projects on Logistic Regression & Gradient Descent and ANN, CNN, & RNN to help deliver exhaustive, hands-on deep learning training in Mumbai.

FAQs

1. Why should you go for the Skill-Lync Advanced Deep Learning using Python Course in Mumbai?

As the world is moving towards digitisation the there is a scope of engineers with advanced skillsets. With the current boom in the new-age tech space, the best deep learning courses will help you to set you on the right career trajectory for in-demand job roles in AI & ML-affiliated domains.

2. What are the prerequisites for taking the Skill-Lync Advanced Deep Learning using Python Course in Mumbai?

This deep learning course in Mumbai is open to all students, professionals, and anyone interested in pursuing a career in the cutting-edge AI and ML tech industry.

3. What is the machine learning course fee in Mumbai?

This deep learning online course is available in three different payment options: the basic, the pro and the premium plan. You can choose two months of access with the Basic plan (at INR 7000 per month for 3 months), 4 months of access with the Pro plan (at INR 10,000 per month for 3 months), and a lifetime of access with the Premium plan (at INR 15,000 per month for 3 months).

4. What benefits are pursuing the Skill-Lync Advanced Deep Learning using Python Course in Mumbai?

This deep learning training in Mumbai will provide you with hands-on training on advanced ML concepts, complex deep learning algorithms, and real-world applications. Moreover, you will get practical experience in implementing Tensorflow in Python.

5. What are the career prospects after completing the Skill-Lync Advanced Deep Learning using Python Course in Mumbai?

After successful completion of this deep learning course in Mumbai, you get upskilled to apply to several in-demand job positions in Mumbai, like-

6. What is the expected salary range after completing the Skill-Lync Advanced Deep Learning using Python Course in Mumbai?

Deep learning engineer's estimated pay ranges between ₹ 3.5- ₹ 20.0 Lakhs, with an average annual salary of ₹ 7.5 Lakhs.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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1 industry expert

Our instructors are industry experts along with a passion to teach.

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8 years in the experience range

Instructors with 8 years extensive industry experience.

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Areas of expertise

  • Machine Learning
  • Deep Learning

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