Online Machine Learning Course with Certificate

About Us

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Course Overview

In the rapidly evolving landscape of technology, data science and machine learning have emerged as indispensable fields, driving innovation and insights across industries. Digicrome's Advanced Certification online program in Data Science and Machine Learning is a comprehensive course designed to equip professionals and enthusiasts with the skills and knowledge needed to excel in this dynamic domain.

Why Opt for an Advanced Certification Online in Data Science & Machine Learning?

The proliferation of data has transformed how businesses operate, making data science and machine learning essential skills. This advanced certification program provides a structured learning path, covering key concepts and tools required to analyze and derive meaningful insights from vast datasets.

Digicrome's Advanced Certification Program in Data Science & Machine Learning:

Digicrome's program stands out as a beacon for those seeking an in-depth understanding of these transformative technologies. Crafted to cater to both professionals seeking career advancement and individuals aspiring to enter the field, this program offers the following
key highlights:

Comprehensive Curriculum:

The program covers a broad spectrum of topics, including Python, exploratory data analysis, machine learning, deep learning, and more. The curriculum ensures a natural progression, gradually introducing learners to interconnected facets of data science.

Expert Faculty:

Delivered by industry leaders and subject matter experts, the program's immersive lectures utilize advanced technological tools for a seamless learning experience. Dedicated program managers provide support beyond the curriculum.

Hands-On Learning:

Assignments and projects are integral to the program, allowing participants to test their skills and apply newly acquired knowledge constructively. Instructors provide detailed feedback, fostering active participation and skill development.

Networking Opportunities:

The program facilitates networking among learners, connecting them with peers, mentors, and industry professionals. Communication channels, including email, voice calls, and video calls, enhance the online learning experience.

Flexible Support:

One-on-one doubt-clearing sessions, coupled with email, voice call, and video call support, ensure participants navigate the course smoothly. The dedicated support system covers both study material-related doubts and managerial queries.

Embark on a transformative journey in the world of data science and machine learning with Digicrome's Advanced Certification Program. Gain a competitive edge in these high-demand fields, enrich your career prospects, and become an expert in harnessing data for valuable insights and innovation. Join the program to stay ahead in technology and make a meaningful impact in the data-driven landscape. Elevate your skills, advance your career, and be a leader in the dynamic field of data science and machine learning.

Course Includes :

Price
60,000 + GST
US Price
$690.92
Dubai Price
2536.99AED
Certifications
Yes
Language
English (US)

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Our Course Curriculum

100% Trusted And Golden Opportunities With Key Features That will Help You To Transform Your Career

1 Introduction to Python - print(), input(), Comments, Variables, Built-in Data Types

2 Basics of Python - Strings and its methods, Python Booleans, Operators (Arithmetic, Logical, Comparison, Assignment,

Identity, Membership, Bitwise), Slicing and Indexing

3 Python Data Structures

3.1 Lists - Access, Change, Add, Remove, Loop, Sort, Copy, Join, List Methods, List Comprehension

3.2 Tuples - Access, Update, Unpack, Loop, Join, Tuple Methods

3.3 Sets - Access, Add, Remove, Loop, Join, Set Methods

3.4 Dictionary - Access, Change, Add, Remove, Loop, Copy, Nested Dictionaries, Dictionary Methods

4 Python Conditional Statements - IF, ELIF, ELSE

5 Loops in Python

5.1 For Loops - Introduction, break, pass, Nested Loops

5.2 While Loops - Break, Continue, Else, Conditional Statements in While Loops

6 Python Functions - Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments,

Positional Arguments, Default Parameters, RETURN, LAMBDA

7 Python Classes and Objects

7.1 Introduction - Creation, __init__(), __str__(), Object Methods, SELF Statement, Pass Statement

7.2 Inheritance - Creation, Parent Class, Child Class, __init__(), super(), Inheritance Methods

8 Miscellaneous - Datetime, RegEx, String Formatting, TRY/EXCEPT

1 NumPy - Numerical Python
1.1 Introduction - Arrays (Indexing, Dimensions, Slicing, Shape, Reshape, Iteration, Joins, Split, Search, Sort, Filter, Copy vs. view
1.2 Random - Introduction, Pseudo vs True Random, Shuffle and Permutations, Random Distributions (Poisson, Normal,
Binomial, etc.)
1.3 Universal Functions - Simple Arithmetic, Rounding Decimals, Log, Summation, Product, Differences, LCM, GCD, Set
Operations, Trigonometric and Hyperbolic Functions
2 Data Wrangling using PANDAS
2.1 Introduction to Pandas - Installation (using PIP) and Import
2.2 Pandas Series and DataFrames - Difference, Labels, Key/Value Objects
2.3 Pandas DataFrames Tutorial - View, Info, Description, Location of Rows and Columns, Named Indexes, CSV/EXCEL Files
2.4 Pandas DataFrames Advanced Tutorial - Cleaning of Data, Handling Null Values (Dropna/Fillna), Handling Duplicate
Values, Handling Data in Wrong Format
2.5 Plotting of Data Using Pandas
3 Data Visualization Using Matplotlib and Seaborn
3.1 Introduction to Data Visualization - Installation (using PIP) and import of Matplotlib and Seaborn, Basics of Plotting
(Labels, X Axis, Y Axis, Headings)
3.2 Matplotlib and Seaborn - Line Plots, Bar Graphs, Scatter plots, Pie Charts, Heatmaps for Correlation, etc.
3.3 Exploring Outliers and Data Distributions - Boxplots, Histograms, KDE Plot, Violin Plot, Pair Plot, etc.
3.4 Advanced Plotting Tutorial - Adding a third Axis on a 2D plot, Fonts, Styles, Subplots, Axis, Grids, Texts on Plots
4 Web Scraping using Beautiful Soup
4.1 Introduction to Web Scraping and Beautiful Soup - Installation and Importing
4.2 Scrape HTML Content from a Page, Parse HTML Code with BeautifulSoup, Find Elements by ID, Find Elements by HTML
Class Names, Extract Texts, Identify Errors, Extract Attributes from HTML Elements
4.3 Building a Script using Web Scraping

1. Introduction to Statistics - Data Types: Numeric (Continuous, Discrete), Categorical (Binary, Ordinal, Nominal), Rectangular Data, Non-Rectangular Data
2. Descriptive Statistics
2.1 Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers)
2.2 Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom) 4 Power BI for Data Science
2.3 Skewness and Kurtosis
3. Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap,
Resampling
4. Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution
5. A/B Testing (Treatment Group, Control Group), Hypothesis Testing (Type 1 Error, Type 2 Error, Significance Value (Alpha)), Permutation Tests, Degrees of Freedom, and Statistical Significance using P-values
6. Correlation Coefficient, Coefficient of Determination, Simple Linear Regression in Statistics

1 Advanced Tutorial on Microsoft Excel

1.1 Introduction to Excel - Formatting, Insertion, Basic Functions (SUM, AVG, etc.)

1.2 Pivot Tables and LOOKUP Functions ( VLOOKUP, HLOOKUP, XLOOKUP, etc.)

1.3 Logical and Statistical Functions

1.4 Chart Data Techniques

1.5 Date/Time, Text, Math Functions

1.6 Advanced Filtering and Sorting

1.7 Summarizing, Importing and Exporting Data from Databases and Web

2 Structured Query Language

2.1 Introduction to Databases - (What are Databases), (What is MySQL), (What is RDBMS), (RDBMS v/s NoSQL)

2.2 Data Base Workflows - Understanding Entity Relationship Diagram, Understanding Normalization (1NF, 2NF, 3NF, BCNF)

2.3 Structured Query Language (SQL) - CRUD Operations

2.4 Data Aggregation Functions - (GroupBy), (OrderBy), (HAVING), (COUNT, SUM, MIN, MAX, AVG)

2.5 Joins in SQL - Primary Key and Foreign Key, Constraints, Set Operations, DML - Savepoint, Rollback

3 Tableau for Data Science

3.1 Installation, Setup, Importing CSV and Excel Files, Connecting SQL Databases and Cloud Services

3.2 Data Cleaning and Wrangling - Handling Missing Values, Handling Duplicates, Formatting of Data, Joins in Tableau

3.3 Basic Visualizations - Bar Chart, Line Chart, Pie Chart, Scatter Plot, Geographical Data Visualization on Maps, Dashboards

in Tableau

3.4 Advanced Visualizations - Heat Maps, Tree Maps, Boxplots, Histograms, Parameters for interactivity and flexibility, Calculated Fields to create new metrics and dimensions

3.5 Analytics and Statistical Tools - Trend Lines and Forecasting, Clustering Techniques and Distribution Analysis

3.6 Filters, Highlighters, Actions to create interactive dashboards, Dashboard Designs

3.7 Case Study - Real World use case of Tableau for Data Science

4. Power BI for Data Science

4.1 Installation, Setup, Importing Files, Connecting SQL Databases and Cloud Services, Direct Query Methods in Power BI

4.2 Power Query - Data Cleaning and Transformation (Handling Missing Values and Duplicates, Formatting of Data)

4.3 Data Modeling - Calculated Columns, Managing Data Models, Creating Relationship between two tables

4.4 Basic and Advanced Data Analysis Expressions (DAX) Tutorial

4.5 Basic and Advanced Visualizations - (Basic - Bar Charts, Pie Charts, Matrices, Maps, etc.), (Advanced - Custom

Visualizations, Slicers, Filters, Waterfall Charts, Funnel Charts, Gauge Charts, etc.)

4.6 Automated Quick Insights and AI Visuals, Dashboard Designs in Power BI

4.7 Case Study - Real World use case of Power BI for Data Science

1.1 What is ML
1.2 Why ML
1.3 Types of ML
1.4 Main Challenges - Overfitting, Underfitting, Poor Quality data, Irrelevant Features etc
1.5 What are Hyperparameters
1.6 How to Select ML model

2.1 Accuracy
2.2 Recall
2.3 Precision
2.4 F1 Score
2.5 Confusion Matrix
2.6 Classification Report
2.7 Precision/Recall Tradeoff
2.8 ROC Curve
2.9 AOC Curve
2.10 Binary and Multilabel Classification
2.11 Feature Engineering and Feature Importance/Selection

3.1 Gradient Descent and Stochastic Gradient Descent
3.2 Logistic Regression
3.3 K Nearest Neighbors
3.4 Naive Bayes
3.5 Support Vector Machines
3.6 Linear Discriminant Analysis
3.7 Decision Trees
3.8 Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV

4.1 Bagging - Eg: Voting Classifiers
4.2 Boosting - XG Boost, Adaboost, etc
4.3 Cross-Validation
4.4 Random Forest Classifier
4.5 XG Boost Classifier
4.6 Stacking
4.7 Hyperparameter Tuning

5.1 Simple Linear Regression
5.2 Multiple Linear Regression
5.3 Polynomial Regression
5.4 Cost Function and Gradient Descent
5.5 Performance Metrics - MSE, RMSE, MAE etc
5.6 Heteroskedasticity, Normality and Correlated Errors
5.7 Hyperparameter Tuning

6.1 Decision Tree Regressor
6.2 Support Vector Machines
6.3 K Nearest Neighbors
6.4 Random Forest
6.5 Boosting
6.6 Hyperparameter Tuning

7.1 Introduction to Unsupervised Learning
7.2 K Means Clustering
7.3 Hierarchical Clustering
7.4 Model-Based Clustering
7.5 DBSCAN
7.6 Anamoly Detection using Gaussian Mixtures

Dimensionality Reduction - Principal Component Analysis (1 class on this topic )

Recommendation Systems (2 Class on this Topic )

1.1 What is ML
1.2 Why ML
1.3 Types of ML
1.4 Main Challenges - Overfitting, Underfitting, Poor Quality data, Irrelevant Features etc
1.5 What are Hyperparameters
1.6 How to Select ML model

4.1 Bagging - Eg: Voting Classifiers
4.2 Boosting - XG Boost, Adaboost, etc
4.3 Cross-Validation
4.4 Random Forest Classifier
4.5 XG Boost Classifier
4.6 Stacking
4.7 Hyperparameter Tuning

6.1 Decision Tree Regressor
6.2 Support Vector Machines
6.3 K Nearest Neighbors
6.4 Random Forest
6.5 Boosting
6.6 Hyperparameter Tuning

7.1 Introduction to Unsupervised Learning
7.2 K Means Clustering
7.3 Hierarchical Clustering
7.4 Model-Based Clustering
7.5 DBSCAN
7.6 Anamoly Detection using Gaussian Mixtures

Dimensionality Reduction - Principal Component Analysis (1 class on this topic )

Recommendation Systems (2 Class on this Topic )

Internship Program

Get Industry Ready

Find Your Strength

Master Data Tools

Work With Data

Manage Time Effectively

Upgrade Your Resume

Grow Your Network

Build Your Portfolio

Expert Feedback Matters

Earn Internship Certificate

Sample Projects You'll Build

Get hands-on experience with real-world inspired projects. These are some examples of what you'll build during the course.

Retail Demand Forecasting System

Build a data-driven model to forecast product demand across different stores and regions for a retail chain. The system helps optimize inventory, reduce overstocking, and improve supply chain efficiency.
Skills Required: Time Series Forecasting (ARIMA, Prophet), Python (Pandas, NumPy), Data Cleaning, Data Visualization (Matplotlib, Seaborn)

Customer Segmentation for E-Commerce Platforms

Analyze user behavior and transaction data to segment customers based on purchasing patterns and demographics. This project helps companies build targeted marketing campaigns for each customer group.
Skills Required: Clustering (K-Means, Hierarchical), Data Preprocessing, PCA, Python, Matplotlib, Scikit-learn

Credit Card Fraud Detection System

Work with real transaction data to detect fraudulent activities using classification models. The project involves dealing with imbalanced datasets and real-time anomaly detection.
Skills Required: Classification Models (Logistic Regression, Random Forest), Imbalanced Data Handling (SMOTE), Python, ROC-AUC Met

Netflix Data Analysis and Insights Dashboard

Analyze Netflix’s global content library to uncover trends in genres, release patterns, and viewer preferences. Present findings using interactive dashboards for business insights.
Skills Required: Exploratory Data Analysis (EDA), Data Cleaning, Python (Pandas, Seaborn), Dashboarding (Tableau or Power BI)

Predicting Housing Prices with Machine Learning

Create a regression model that accurately predicts house prices based on features like location, size, and amenities. This is a classic supervised learning project with practical industry relevance.
Skills Required: Regression (Linear, Ridge, Lasso), Feature Engineering, Scikit-learn, Data Visualization, Hyperparameter Tuning

Employee Attrition Prediction for HR Teams

Help HR departments retain talent by predicting which employees are most likely to leave using historical employee data. This enables proactive retention strategies and workforce planning.
Skills Required: Classification, Exploratory Data Analysis, Logistic Regression, Feature Importance, Python

Airbnb Market Analysis and Price Optimization

Analyze Airbnb listings to uncover pricing trends across cities and build a pricing recommendation engine based on property features and location demand.
Skills Required: Data Cleaning, Regression Models, Geospatial Data Analysis, Python, Seaborn/Matplotlib, Feature Selection

Application Process for Digicrome

Our Acknowledged features offerings

Career Consultation

Assess eligibility

Personalized Guidance

Acceptance letter

Easy Registration

Pay booking amount

Start Upskilling

Access curriculum

Ongoing Support

Mentorship & guidance

Our Course Comes with Offerings

By Joining Our Program, Underlying Are The Key Featuers You Will Get

Get Lifetime Access to LMS

Your growth doesn’t end with the course—and neither does your access. With lifetime entry to our Learning Management System, you can revisit lessons, sharpen your skills, and stay up to date whenever you need. Learn at your pace, on your terms—because real learning is lifelong.

Live Interactive Online Sessions

" Make your weekends count with live sessions guided by experienced industry professionals. These aren’t just classes—they’re your space to ask, discuss, and truly understand. Connect with peers, solve real-world challenges together, and build confidence that lasts beyond the classroom. All scheduled keeping your time and growth in mind.

Regular Evaluations for better learning

We don’t just track your progress—we walk with you every step of the way. Through timely check-ins, constructive feedback, and personalised support, we help you understand what’s working and where to improve. It’s not about marks, it’s about momentum—so you keep moving forward with clarity and confidence.

Personalized Doubt Sessions

Every learner has unique challenges—and that’s why we offer one-on-one mentor support tailored just for you. From untangling tough topics to guiding your next steps, our mentors are here to listen, support, and keep you moving forward. Because real learning happens when someone’s genuinely there for you.

Hands-On Projects & Case Studies

Whichever path you choose—be it Data Science, AI, or Analytics—you won’t just study concepts, you’ll apply them. Through practical projects inspired by real industry scenarios, you’ll build the skills and confidence to solve challenges that companies face every day. It’s not just learning—it’s preparing for what comes next.

Focused Learning Tracks

Your career journey is unique—and your learning should reflect that. Choose a course that matches your goals, and dive deep into the skills that matter most in your domain. Whether you're drawn to tech, business, or the creative world, you'll gain focused expertise that sets you up for success, your way

Interview Preparation

We don’t just help you learn—we help you land the job. With personalised career guidance and realistic mock interviews, you’ll get the support you need to present your strengths, handle tough questions, and walk into every interview prepared and self-assured. Because your success is our goal, right from day one.

Our FAQs

Imperative FAQs About Us!

This is a 6-month live online program with hands-on work and expert guidance. It teaches you the basics and advanced parts of data, covering how to understand, work with, and analyse data using Python, machine learning, and more.

You can be a 12th pass or a graduate from any accredited institute or a professional looking to enhance their Data and ML skills. It is not mandatory to be from a tech background. We teach everything from beginner to advanced level, step by step.

Anyone interested in Data Analytics & ML or willing to learn and earn from it, be it a student, working professional, or job seeker interested in upskilling regardless of their background, can take this course.

This course teaches you all the tools, methods, and techniques used in data analytics and the role of ML in it. You will learn Python, data analysis, data visualization, machine learning, and deep learning through practical experience with capstone projects and assessments.

Yes, you will be certified after course completion. You can share it on your LinkedIn or include it in your resume. This will enhance your job opportunities and make you industry-ready and credible for roles in Data Analytics and Machine Learning.

Throughout the course, you will be supported in various ways, including 1-on-1 guidance, regular doubt sessions, and dedicated program managers. You will be prepared for job interviews through mock sessions and will also be equipped with the necessary soft skills. Additionally, mentors will be available through calls, emails, and video calls throughout the course.

You can apply for roles like Data Analyst, Business Analyst, ML Engineer, or Data Scientist. These are high-demand roles across all industries — from tech to finance to healthcare.

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Advanced Certification Online Data Analytics and Machine Learning Course

What Our Program Offers?

Trusted by world's best Organisations

About the Program

Practice as You Learn

Learn from Expert Professionals

Interactive Learning with Practical Work

Flexible Learning for Busy Schedules

Connect with Experts and Advance Your Career

Placement-Focused Training

Lifetime Access to Learning Materials

Industry Projects for Practical Learning

Earn with Skills – Freelance Ready

Course Overview

Course Includes :

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Our Course Curriculum

Fundamentals of Python Programming

Exploratory Data Analysis using Advanced Python Libraries

Practical Statistics for Data Scientists

Data Analytics Tools and Structured Query Language

Introduction to Machine Learning

Classification Metrics

Classification Models

Ensemble Techniques

Regression Techniques

Regression Models

Unsupervised Learning

Dimensionality Reduction - Principal Component Analysis

Recommendation Systems

Fundamentals of Python Programming

Exploratory Data Analysis using Advanced Python Libraries

Practical Statistics for Data Scientists

Data Analytics Tools and Structured Query Language

Introduction to Machine Learning

Classification Metrics

Classification Models

Ensemble Techniques

Regression Techniques

Regression Models

Unsupervised Learning

Dimensionality Reduction - Principal Component Analysis

Recommendation Systems

Internship Program

Languages and Tools Covered

Sample Projects You'll Build

Retail Demand Forecasting System

Customer Segmentation for E-Commerce Platforms

Credit Card Fraud Detection System

Netflix Data Analysis and Insights Dashboard

Predicting Housing Prices with Machine Learning

Employee Attrition Prediction for HR Teams

Airbnb Market Analysis and Price Optimization

Trusted by millions of learners around the Globe

Moments of Honour

Our Placed Learners In Different Big Firms

Mohd. Jubair Khan

NAMAN SHARMA

Abhinav Tiwari

Raushan Kumar

Santosh Singh

Mansi Dwivedi

Asha Sandeep

Avantika Verma

Shivani Kiran

Manohar Ramchandra

Mukesh Ghodeswar

Ritika Sadhwani

What Students Say About
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