Deep Learning

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Deep Learning Details

Prerequisites : Anyone can learn Web Designing and Development.

Level : Beginner (By the end of your course become a Professional)

Duration : __ Months

Batches : Weekend and Weekday Batches Available

Mode : Online and Offline

Certificate : Available after completing the course and projects.

Placement : Available

Module 1 Introduction to Machine Learning (ML)
What is Machine Learning?, Use Cases of Machine Learning, types of Machine Learning- Supervised to Unsupervised methods, machine learning workflow
Module 2Statistics for Data Science
Common charts used, Inferential statistics, probability, central limit theorem, normal distribution and hypothesis testing.
Module 3Data Visualization
Plotting basic statistical charts in Python, Data visualization with Matplotlib, statistical data visualization with Seaborn, Interactive data visualization with Bokeh.
Module 4Exploratory Data Analysis
Introduction to Exploratory Data Analysis (EDA) steps, Plots to explore relationship between two variables, Histograms, Box plots to explore a single variable, heat maps, pair plots to explore correlations.
Module 5Data Preprocessing
Preprocessing techniques like missing value imputation, encoding categorical variables, scaling , too many nulls, same values/ skew, data types, missing value imputation, when column doesn't have missing values, categorical attributes, related attributes.
Module 6Deep Learning (Neural Networks)
Introduction to Neural networks, what are perceptrons and types of perceptrons, Artificial Neural Networks (ANN), Convolution Neutral Networks (CNN), Recurrent Neutral Networks (RNN), Transfer Flow, Scikit - Learn, YOLO - Real time object detection with OpenCV, workflow of a Neural network and analogy with biological neurons.

Facts

199+

Number of students enrolled in this Course

165+

Students who have completed the course

95%

Number of students who received a Job opportunity on completion of Course