*#1 Most Popular Online Course & Internship in Data Science* You can enroll today & get certified from EasyShiksha & HawksCode.
Welcome to the ultimate Data Science , Machine Learning course for 2025 – your complete guide to mastering Data Science , Machine Learning from the ground up with real-world examples and hands-on projects.
This course is designed for beginners and intermediate learners who want to dive deep into the fields of Data Science , Machine Learning. Whether you’re starting from zero or brushing up your skills, this course will walk you through all the essential concepts, tools, and techniques used in Data Science , Machine Learning today.
You’ll begin by understanding the core principles of Data Science , Machine Learning, then move into Python programming, data preprocessing, model training, evaluation, and deployment. With step-by-step explanations and practical exercises, you’ll gain real-world experience in solving problems using Data Science , Machine Learning.
By the end of the course, you’ll be fully equipped to handle real projects and pursue career opportunities in Data Science , Machine Learning confidently.
Introduction to Data Science , Machine Learning:
Understand the principles and concepts of data science and machine learning.
Explore real-world applications and use cases of data science across various industries.
Python Fundamentals for Data Science:
Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.
Master data manipulation, analysis, and visualization techniques using Python.
Data Preprocessing and Cleaning:
Understand the importance of data preprocessing and cleaning in the data science workflow.
Learn techniques for handling missing data, outliers, and inconsistencies in datasets.
Exploratory Data Analysis (EDA):
Perform exploratory data analysis to gain insights into the underlying patterns and relationships in the data.
Visualize data distributions, correlations, and trends using statistical methods and visualization tools.
Feature Engineering and Selection:
Engineer new features and transform existing ones to improve model performance.
Select relevant features using techniques such as feature importance ranking and dimensionality reduction.
Model Building and Evaluation:
Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and gradient boosting.
Evaluate model performance using appropriate metrics and techniques, including cross-validation and hyperparameter tuning.
Advanced Machine Learning Techniques:
Dive into advanced machine learning techniques such as support vector machines (SVM), neural networks, and ensemble methods.
Model Deployment and Productionization:
Deploy trained machine learning models into production environments using containerization and cloud services.
Monitor model performance, scalability, and reliability in production and make necessary adjustments.
Introduction To Course
Python Complete Course Introduction
Python Class 1 : Introduction To Python
Python Class 2 : Setting Python Environment
Python Class 3 : Introduction To Variables
Python Class 4 : Introduction To Keywords
Python Class 5 : Introduction To Datatypes
Python Class 6 : ID Function
Python Class 7 : Arithmetic Operator
Python Class 8 : Logical Operator
Python Class 9 : Comparison Operator
Python Class 10 : Bitwise Operator
Python Class 11 : Membership Operator
Python Class 12 : Identity Operator
Python Class 13 : Conditional Statements
Python Class 14 : For Loop and Range Function
Python Class 15 : While Loops
Python Class 16 : Break and Continue
Python Class 17 : Function
Python Class 18 : Try Except Finally Blocks
Python Class 19 : String and Functions
Python Class 20 : List and Functions
Python Class 21 : Tuple and Functions
Python Class 22 : Dictionary and Functions
Python Class 23 : Class and Object
Python Class 24 : Class Methods
Python Class 25 : Inheritance and its types
Python Class 26 : Polymorphism and its types
Python Class 27 : Encapsulation and Access Modifiers
Python Class 28 : Abstraction
Python Class 29 : Mini Project
Complete Data Science Course
Numpy Complete Course
Numpy Class 1 : Import and Install
Numpy Class 2 : Array and its Types
Numpy Class 3 : Datatypes
Numpy Class 4 : NDIM Function
Numpy Class 5 : ARANGE Function
Numpy Class 6 : CONCATENATE Function
Numpy Class 7 : NDMIN Function
Numpy Class 8 : NDITER Function
Numpy Class 9 : All Functions
Pandas Class 1 : Import Dataset
Pandas Class 2 : Head & Tail Function
Pandas Class 3 : Info Function
Pandas Class 4 : Drop na Function
Pandas Class 5 : Fill na Function
Pandas Class 6 : Drop Duplicates Function
Pandas Class 7 : Replace Values Function
Matplotlib Class 1 : Import Dataset
Matplotlib Class 2 : Show Function
Matplotlib Class 3 : Marker Function
Matplotlib Class 4 : Xlabel Ylabel Function
Matplotlib Class 5 : Title Function
Matplotlib Class 6 : Linestyle Linewidth Function
Matplotlib Class 7 : Barplot
Complete Machine Learning Introduction
Machine Learning Class 1 : Linear Regression
Machine Learning Class 2 : Logistics Regression
Machine Learning Class 3 : Support Vector Machine
Machine Learning Class 4 : KNN
Machine Learning Class 5 : K Means Clustering
Machine Learning Class 6 : Naive Bayes
Machine Learning Class 7 : Decision Tree Classifier
Machine Learning Class 8 : Random Forest
The following course is fully online, and hence there is no need for any physical classroom session. The lectures and assignments can be accessed anytime and anywhere through a smart web or mobile device.
Anyone can choose a preferred course and start immediately without any delay.
As this is a purely online course program, you can choose to learn at any time of the day and for as much time as you want. Though we follow a well-established structure and schedule, we recommend a routine for you as well. But it finally depends on you, as you have to learn.
If you have completed the course, you would be able to have lifetime access to it for future reference too.
Yes, you can access and download the content of the course for the duration. And even have lifetime access to it for any further reference.
All the software/tools that you need for the course would be shared with you during the training as and when you need them.
No, only a soft copy of the certificate will be awarded, which can be downloaded and printed, if required.
You can try to make the payment through a different card or account (maybe a friend or family). If the problem persists, email us at info@easyshiksha.com
Due to some technical faults, this can happen. In such a case the amount deducted will be transferred to the bank account in the next 7-10 working days. Normally the bank takes this much time to credit the amount back into your account.
At times, there may be a slight delay in your payment reflecting on your EasyShiksha dashboard. However, if the problem is taking longer than 30 minutes, please let us know by writing to us at info@easyshiksha.com from your registered email id, and attach the screenshot of the payment receipt or transaction history. Soon after verification from the backend, we will update the payment status.
If you have enrolled, and are facing any technical problem then you can request a refund. But once the certificate has been generated, we shall not refund that.
Yes! You surely can. To begin this, just click the course of your interest and fill in the details to enrol. You are ready to learn, once the payment is made. For the same, you earn a certificate too.
Please contact us at: info@easyshiksha.com
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