Our Special Attractions
Guided Hands-On Assignment
Challenging Capstone Projects
1-1 Skype Live Doubt Clearance
Course completion certificate
Why Deep Learning ?
Deep learning algorithms perform much better, by providing godlike accuracy, when there are tons of data accessible for Image classification and Restoration, Object detection, and even recognizing Handwritten digits.
Super Fast Growing Industry
Highly Paid Job Roles
A Lot of Research Opportunities.
2X-3X Increment in Salary
Huge Demand in Market
To Resolve Your Doubts While Learning
1-1 Chat Support Everyday
Mentors will be available everyday between 3PM to 11:59PM (IST) to resolve all of your doubts and queries related to assignments, projects and interview questions
Complete
Course Fee
₹7999
₹
3999
-
Hands-on Practical Learning System
-
45+ Guided Assignments
-
1-1 Live Doubt Clearance Support
-
Course Completion Certificate
-
Lifetime Course Validity
Popular
Recent Success Stories of Our Learners
Let's Talk About Numbers
0
+
Total Learners
0
+
Social Media Followers
Meet The Course Designer
Hello, I'm Akash.
I'm A Data Scientist.
I have transitioned my career from Manual Tester to Data Scientist by upskilling myself on my own from various online resources and doing lots of Hands-on practice. For internal switch I sent around 150 mails to different project managers, interviewed in 20 and got selected in 10 projects.
When it came to changing company I put papers with NO offers in hand. And in the notice period I struggled to get a job. First 2 months were very difficult but in the last month things started changing miraculously.
I attended 40+ interviews in span of 3 months with the help of Naukri and LinkedIn profile Optimizations and got offer by 8 companies.
Now I want to use my experience to help people upskill themselves and land their dream Data Science jobs!
Complete Course Curriculum
( Every Details You Need Is On Your Fingertip )
Neural Network Module
Tensorflow Fundamentals
In this assignment, we have covered, use cases of tensorflow in real world, tensors definition, tensor types, tensor formats, tensorflow methods, mathematical operations in tensorflow, numpy compatibility, gradient and finally a small end to end linear classifier using the concepts of tensorflow we learnt from the beginning.
PyTorch Fundamentals
In this this assignments we have mentioned every fundamentals of Pytorch which one should know while working on this library.
Building Blocks of Neural Network
In this assignment we have mentioned all the concepts that one should know before getting into neural networks. Each and every concept that is required for working on neural nets has been mentioned in this assignment.
Activation Functions in Neural Network
In this assignment, we have covered definition of activation function, its uses in deep learning, types of activation function like linear, binary & non linear and their subtypes. We have also covered how to use it in coding.
Training Neural Network
In this assignment we have trained multiple models using mnist handwritten digit dataset and learnt how to choose number of layers and number of neurons for the model. We have also explained how changing the architecture of the neural network affects the model.
Loss Function
In this assignment, we will see what is a loss function, how to choose a loss function for a particular problem. We have covered multiple cases like regression, binary and multi-class classification where you will choose loss function accordingly.
Forward & Backward Propagation
In this assignment we will learn how we go from left to right in a neural network via forward propagation and then we do back propagation to update the weights in our neural network which eventually helps us in reducing our loss.
Optimizer & Types of Gradient
In this assignment, we have covered what is an optimizer, its different types like gradient descent, sgd, momentum, adagrad, adadelta, adam, mini batch, RMSprop, NAG, FTRL with a working example .
Regularization Techniques
In this assignment, you will learn different types of regularization techniques like dropout, L1, L2 and early stopping using the example of fashion mnist data. You will build multiple models to get deeper insights.
Neural Network Practice Set
In this assignment, you will implement all learnings from the neural network assignments on multiple datasets.
Python Oops
In this assignment we will learn OOPs concepts like classes, objects, member variables, methods, attribute, and many more.
Neural Network From Scratch
This assignment will teach you to code neural network completely from scratch. After this assignment one will be very much comfortable on creating any kind of neural nets from scratch and that's what the main agenda of this course.
Computer Vision Module
Introduction to CNN
In this assignment, we will cover basic concepts of CNN like what is CNN, padding, pooling, striding, fully connected layer using cifar dataset.
Image Data Pre-Processing
In this assignment, we will learn need of preprocessing, image augmentation, steps to use in the preprocessing, use of imagedatagenerator, and other steps using Tensorflow.
CNN With Fit Generators
In this assignment you will learn difference between fit and fit generators, what is flow from directory method, in the tensorflow by implementing it on dog cat classification dataset.
Transfer Learning
In this assignment we will learn what is transfer learning, why do we need it, what is resnet, resnet architecture, how to implement transfer learning using resnet etc.
AlexNet
The main purpose of this assignment is to get a complete idea of Alexnet and code it completely from Scratch.
VGG - 16
The main purpose of this assignment is to get a complete idea of VGG-16 and code it completely from Scratch.
OpenCV
In this assignment, we will learn functionalities of opencv. Opencv is a large open source library for computer vision, machine learning and image processing. We will learn how to process an image and video too.
You only look once(YOLO)
In this assignment, we will first teach you installation of yolo and its dependencies, getting the data, data annotation, how to create bounding boxes , how to train different yolo models, tensorboard implementation, and making predictions.
Detectron2
In this assignment, we will learn how to use detectron for object detection, image segmentation and panoptic segmentation.
Image Segmentation
In this assignment we will learn what is image segmentation, it's importance, types of image segmentation, for example region based segmentation, Edge Detection Segmentation.
DeOldify
In this assignment we will see how we can use DeOldify to colorize black & white images.
GANs
In this assignment we will see what is GANs, it's applications and how to implement it on the dataset.
NLP Module
Basics of NLP
In this assignment, you will learn about NLP, top 8 use cases in industry, concepts of tokenization, punkt, lemmatization, stemming , stemming vs lemmatization and stop words removal. All these concepts will help you in dealing with sentences and paragraphs later.
Converting Words into vectors (Basic)
This assignment covers concepts of countvectorizer, bag of words, TD-IDF, N-Grams, POS tagging which will teach you how you can convert words into vectors. It will help you dealing with text data. You will find all the necessary resources to do the assignment inside the file.
Converting Words into vectors (Advanced)
This assignment covers advanced concepts like Gensim, Word2Vec, continuous bag of words(CBOW), skip-Grams which will teach you more ways of converting words into vectors. It is extended version of assignment 2 converting words into vectors.
Sentiment Analysis
It is a project on predicting sentiment of words using logistic regression technique and all the learnings we had in previous 3 assignments. It covers countvectorizer, lemmatization, stemming, TF-IDF etc.
ANN
In this assignment, we will revise basics of artificial neural network with practical implementation using the dataset of credit card churn modelling.csv.
RNN
In this assignment you will build and train a model using RNN on the mnist dataset. You will learn use cases of RNN by practical implementation.
LSTM
In this assignment we will learn what Is LSTM, it's implementation using RNN using imdb dataset.
GRU & Bidirectional LSTM
In this assignment, we will learn about GRU, it's architecture, applications, Bidirectional LSTM RNN and the implementation of bidirectional LSTM using Tensorflow.
Encoder, Decoder & Transformer
In this assignment, we will learn about encoders, decoders, hidden state, attention model transformer, positional encoding, residuals, hugging face, and transformers pipeline.
Bert
In this assignment we will see implementation of bert model in two steps, pretraining and fine-tuning. We will do fine tuning for QA, sentiment analysis and Named Entity Recognition(NER).
Autocorrect
The Autocorrect with NLP project provides to way to get recommendations of words when you start typing the word. It also recommends correct spellings for wrongly spelled words based on text distance. It has an interface which is created in Google Colab.
Time Series Module
Introduction To Time Series
In this assignment, we will learn basics of time series i.e what is time series, its use cases with real world examples, time series components, and we will use a simple dataset to visualize time series data.
STATIONARITY
In this assignment, we will learn what is called stationarity series, how we check stationarity in the data using visualization, augmented dickey fuller test, KPSS test.
ACF & PACF
In this assignment, we will cover topics like correlation, autocorrelation, partial autocorrelation function and differencing using multiple datasets to explain each concepts.
ARIMA
In this assignment, we will learn about arima models, terms used in arima, AR & MA models, order of differencing, order of AR term, order of MA term, how to build arima model, and metrics used in it.
Auto ARIMA, SARIMA & SARIMAX
In this assignment, we will cover auto arima, interpreting residual plots in arima model, automatically building sarima model, and building sarimax with exogenous variable.
Model Selection
In the assignment, we will cover model selection, AIC, BIC, and it's implementation using drug sales data
VARMA
In this assignment, we will cover varma with its implementation.
Autoregressive ML Models
In this assignment, we will cover auto regressive concepts with example, model and implementation.
ANN for Time Series Forecasting
In this assignment, we will see the implementation of ANN for time series forecasting on stock prices dataset.
CNN For Time Series Forecasting
In this assignment we will cover the effect of CNN on numerical data and implement CNN for time series forecasting.
LSTM For Time Series Forecasting
In this assignment you will be learning about implementing time series forecasting using LSTM neural networks.
Get Amazed By Our most Appealing Data Science Mega Combo Course
Sample Of Course Complete Certificate
We are Acknowledged By Government of India As An Innovative Startup
Copyright © 2022 CloudyML. All Rights Reserved.