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Career Opportunities
Python Developer, ML Engineer, Data Scientist, Data Analyst, etc
Top skills you will learn?
Understanding Mathematical Models will help in capturing information from data.This course will help students in understanding fundamental concepts about supervised & unsupervised learning Algorithms.Developing Skills to use Python Libraries such as Numpy, Keras, Sklearn, Matplotlib & many such libraries.
Who can join the program?
Freshers, Students doing B.E. / BTech, BSc, MSc, MTech, BCA, MCA, BCom, Development Enthusiast, Working Professionals
Minimum Eligibility
Should know fundamentals of Computer Science
Course Curriculum
Best-in-class content by leading faculties & industry leaders in form of Live Classes, Projects, Industry Case studies & Assignments.
Milestone 1
Getting Started with language
10 Assessment | Credit : 4
Basics of Programming
What is a programming language?
Why do we need programming in Industry?
Practical examples (without code part)
Milestone 2
Language basics but essentials
10 Assessment | Credit : 7
Introduction to language
Data types
Type Conversion and typecasting
Keywords
Variables
Identifiers
Operators
Milestone 3
Program Flow and Design
10 Assessment | Credit : 8
Flow Control Structures
Iterative Control Structures (Loops)
Functions
Classes & Objects
Inheritance
Polymorphism
Abstraction
Encapsulation
Milestone 4
Object Oriented Concepts
10 Assessment | Credit : 5
Classes & Objects
Inheritance
Polymorphism
Abstraction
Encapsulation
Milestone 5
Production Level Essentials
10 Assessment | Credit : 3
Exception Handling
File Handling
Code Debugging
Milestone 6
Numpy
10 Assessment | Credit : 2
Numpy Introduction
Numerical operations using Numpy
Milestone 7
Pandas
10 Assessment | Credit : 2
Pandas Introduction
Handling datasets and processing data
Milestone 8
Stats for Machine Learning
10 Assessment | Credit : 27
Probability and Statistics
Population and Sample
Gaussian Normal distribution and CDF
Symmetric distribution and skewness
Standard normal variate and standardization
Kernel density estimation
Sampling distribution and Central limit theorem
Q-Q plot
Various distributions and their use
Chebyshev’s inequality
Discrete and Uniform distribution
Bernoulli and Binomial distribution
Log-Normal distribution
Power law distribution
Box-CoxTransform
Application of non-Gaussian distributions
Co-Variance
Pearson Correlation Coefficient
Spearman rank correlation coefficient
Correlation Vs Causation
Use of correlations
Introduction of confidence Interval
Computation of confidence interval
Hypothesis Testing
Resampling and Permutation Test
K-S test for similarity of two distributions
Proportional Sampling
Milestone 9
Python Essentials for Machine learning
10 Assessment | Credit : 5
Basics of Data Sets
Introduction to dimensionality reduction
Row and Column Vector
Representation of Data Set
Representing Data Set as a matrix
Milestone 10
Data Analysis
10 Assessment | Credit : 12
Factors affecting classification algorithms
Balanced Vs Imbalanced Datasets
Impact of outliers
Space and Run time complexity
K distance
Multiclass classification
Time and space complexity of K-Nearest neighbor
Feature Importance
Handling categorical and numerical features
Handling missing values
Curse of dimensionality
Bias-Variance tradeoff
Milestone 11
Data Preprocessing
10 Assessment | Credit : 8
Data Preprocessing
Mean of data matrix
Column standardization
Covariance of data matrix
MNIST Data set
PCA (Principle Component Analysis) for dimensionality reduction
Limitations of PCA
t-SNE for dimensionality reduction
Milestone 12
Preprocessing continued (Text Focused)
10 Assessment | Credit : 7
Preprocessing DataSet
Data Cleaning
Convert text to vector
Bag of words
Stemming
tf-IDF
Word2Vec
Milestone 13
Python Visualizations
10 Assessment | Credit : 2
MatPlotLib Introduction
Plotting various types of graphs such as scatter plot, line plot, histogram, etc
I2C, on-chip EEPROM
Watchdog timer, etc. Case Study of MC
Milestone 14
Visualization Practicals
10 Assessment | Credit : 11
Introduction to Iris dataset
2D scatter plots
3D scatter plots
Pair Plots
Histograms and Probability density function (PDF)
Univariate Analysis using PDF
Mean, Median, Variance and standard deviation
Cumulative distribution function (CDF)
Percentiles and Quantiles
Box Plots with whiskers
Violin Plots
Milestone 15
Supervised Machine Learning Algorithms
10 Assessment | Credit : 13
Naive Bayes algorithm for classification
Logistic Regression
Linear Regression
Gradient descent algorithm
Support Vector Machine
Decision tree algorithm for classification
Ensembles
Random Forest
Gradient boosting
XGboot and AdaBoost
Classification & Regression in Machine learning
K-Nearest Neighbour
Time and space complexity of K-Nearest neighbor
Milestone 16
Unsupervised Machine Learning Algorithm
10 Assessment | Credit : 4
Clustering algorithms
K means algorithm
Agglomerative clustering
Density-based clustering (DBSCAN)
Milestone 17
Model Performance Metrics
10 Assessment | Credit : 7
Accuracy measure of classification algorithm
Accuracy
Confusion matrix
ROC and AUC curve
Log-Loss
R-squared coefficient of determination
Median absolute deviation (MAD
Milestone 18
Working with different types of datasets
10 Assessment | Credit : 8
Feature Engineering
Moving window for time series
Fourier decomposition
Image histogram
Relational data
Graph data
Feature binning
Feature slicing
Milestone 19
Basic of Deep Learning
10 Assessment | Credit : 8
Neural network and Deep Learning
History of neural network and comparison with biological neuron
Multilayer perceptron
Training a single layer model
Training MLP model
Back Propagation
Activation function
Vanishing gradient problem
Milestone 20
Components of Deep Learning
10 Assessment | Credit : 12
Deep layer perceptron
Drop Outs
Regularization
RELU
OptimizerHill-descent 2D
OptimerHill-descent 3D
SGD
Adam optimizer algorithm
Softmax for multiclass classification
Tensor Flow and Keras
GPU vs CPU
Google collaboratory
Milestone 21
Deep Learning Algorithms
10 Assessment | Credit : 11
Convolutional Networks
Understanding Visual cortex
Edge detection in images
Padding and strides
Convolutional layer
Max Pooling
ImageNet data sets
AlexNet
VGGNet
Mini Project: Cats Vs Dogs
Given an image of an animal identify whether it is an image of Dog OR Cat OR None
Milestone 22
Advanced Deep Learning Algorithms
10 Assessment | Credit : 6
Recurrent Neural Networks
Training RNN model by backpropagation
Types of RNN
LSTM
Deep RNN
Bidirectional RNN
Milestone 23
MLOps
10 Assessment | Credit : 3
APIs
Docker Containers
Hosting
Industry Projects and Case Studies
Learn through real-life industry projects.
Get Hands-on coding practice
Devlop projects and applications
Get mentored by industry experts
Email Slicer
The email slicer is a handy program to get the username and domain name from an email address. You can customize and send a message to the user with this information.
Desktop Notifier App in Python
A desktop notifier app runs on your system and it will be used to send you notifications after every specific interval of time.
Convert Text to Speech in Python
Convert your text into voice with Python and Google APIs. Text to speech project takes words as input on digital devices and converts them into audio or speech with a button click or finger touch.
YouTube Video downloader
Another interesting project is to make a nice interface through which you can download youtube videos in different formats and video quality.
Language translator in Python
Instantly translate texts, words, paragraphs from one language to another. The objective of this project is to translate text content from one language to any other language in real-time with a button click.
Taxi Demand In Your City
The end user of this application is a Taxi driver. Taxi driver will be informed about the expected number of pickups from a given region in the next 10 minutes.
Cancer Diagnosis
Classify the given genetic mutations/variations on the evidence of text based clinical literature.
Predict Price Of House In A Given City
Using Housing Data Set this project will predict what will be the prize of a house depending on Location & Other factors within the City.
Emotion Detection By Using Image Of Human Face
The Project will capture images of human faces from the camera and it will predict emotions like Happy, Sad, Angry and so on.This Project is very important for security systems.
Self Driving Car
Software emulator for self-driving cars.Understanding the challenges in building self driving cars and improving efficiency by reducing risks and also improving throughput (such as saving fuel)
Course Completion Certificate
You will be awarded a Course Completion Certificate only if you pass with a
minimum grade of 60% and a Certificate of Excellence if you secure 90% and above.
Join Internship program with companies to gain complete insights into Python - the perfect programming language. Get guidance from industry experts, and top graders on live projects and case studies.
Community Access
Community that connects you with the best Pythoniasts across the globe! All your doubts will be cleared live with industry experts. Aim to grow your knowledge and skills with the Python Community now!
Placement Support
Dedicated mentorship and intensive career support for your career growth. Prepare for interviews & interact with industry experts at career events. Help you find the perfect career opportunity!
Download lessons and learn anytime, anywhere with the Ekeeda
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Success Stories
Our users have achieved so much in just a little time.
Honestly speaking when I started this data scientist course, I wasn't well-equipped with a statistical background or any form of analytical knowledge. However, after taking up this course with Ekeeda in just couple of month's time, I gained industry-level data analytics and statistical knowledge. This immediately landed me a job with good pay package as a start. It is not only our hardwork but the guidance and willingness to clear your doubts by experts at Ekeeda that makes this data scientist course a must in your career program. I can say it was the best investment I've done for my flourishing career. I am now working as a senior data scientist for one of the best fintech companies in India. Thank You Ekeeda for your support and mentorship
Shubham Jadhav
Computer IT Engineering
Data Scientist
I had absolutely zero coding background before this course. Initially I was little skeptical but later it took me to surprise when I saw Ekeeda delivered what they have promised. I learnt two coding languages Python and R right from scratch. Faculty is very friendly and passionate to help you, students can easily reach them with questions and get solutions on it. The curriculum is designed with industry needs and latest trends. It takes you to the basic coding and then helps you understand and build a good base on the advanced topics like Machine learning, Visualization. Lastly, the career team did a fantastic job by guiding me through the job search process and opportunities to seize. Today, I work as a data scientist in one of the top software companies of India with good salary and great job satisfaction. Thank you Ekeeda
Kalpak Awaghade
Computer IT Engineering
Data Scientist
The increasing amount of data requires proper data management, manipulation, analysis and value creation. Since I was passionate to make my career into this field, I googled for a fast-paced certification on data science. That's when I came across Ekeeda Data Science program. To my surprise, the course provides exactly what a data scientist has to have, and offers the unique opportunity to learn & make use of other languages, tools and enhance your technical skills. After taking up this online course I started improving my programming, machine learning and data science skills. I would like to express my gratitude to all the faculty members at Ekeeda. I would say once you complete this course, you can introduce yourself as a data scientist confidently and make a great career start. Career guidance them will help you with 100% job placements. A must for program for all the aspirants who want love data management.
Akash Tomar
Mechanical Engineering
Data Scientist
500+ hours of learning
23 Milestone
100+ Assignments & Projects
100+ Job Opportunities
Get Hands-on coding practice
Devlop projects and applications
Get mentored by industry experts
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