Program Overview
In today’s rapidly advancing technological landscape, data science and machine learning have become crucial fields that drive innovation and deliver valuable insights across industries. The Professional Certification Course in Data Science with Machine Learning is meticulously designed for professionals and enthusiasts in the USA, equipping them with the expertise needed to excel in this transformative domain.
Why Choose a Professional Certification Course in Data Science with Machine Learning?
The explosion of data in the digital era has revolutionized industries worldwide, making data science and machine learning critical skills for businesses. This certification program offers a structured and practical learning path, covering core concepts and cutting-edge tools to analyze data, build predictive models, and derive actionable insights from complex datasets.
Program Highlights of Digicrome’s Professional Certification in Data Science with Machine Learning
1. Comprehensive Curriculum
Gain an in-depth understanding of essential topics, including:
- Python Programming for Data Science
- Exploratory Data Analysis (EDA)
- Statistical Modeling and Predictive Analytics
- Machine Learning and AI Fundamentals
- Big Data Analytics and more!
The curriculum is designed for a smooth progression, gradually introducing advanced concepts to provide a complete understanding of data science and machine learning.
Expert Faculty & Mentorship
Learn from industry leaders and data science experts who bring real-world insights into the classroom. With access to:
- Live sessions and hands-on workshops
- Guidance from top professionals in data science and ML
- Dedicated program managers to support your learning journey
3. Hands-On Projects & Real-World Applications
The program emphasizes practical knowledge through:
- Industry-relevant projects and case studies
- Capstone projects that simulate real-world data science challenges
- Personalized feedback to ensure skill enhancement
4. Networking Opportunities
Connect with a global network of professionals, mentors, and industry experts. The program facilitates collaboration and learning through:
- Virtual meetups
- Peer-to-peer learning forums
- Opportunities to work with mentors and data scientists
5. Flexible and Personalized Learning Support
- 1:1 Doubt-Clearing Sessions via video calls, emails, or chat
- Access to a dedicated support team for academic and technical queries
- Self-paced learning modules to fit your schedule
6. Certification from Industry Leaders
Earn globally recognized certifications from Digicrome, IBM, and Microsoft, making you stand out in the competitive job market.
Why Enroll in Digicrome’s Professional Certification Course?
- Gain job-ready skills with practical experience
- Master data science tools and techniques used by top companies
- Build your portfolio and gain industry exposure
- Enjoy lifetime career support with resume-building workshops, mock interviews, and job placement assistance
Embark on Your Data Science Journey
Transform your career with Digicrome’s Professional Certification in Data Science with Machine Learning. Stay ahead in the fast-evolving world of technology and gain a competitive edge in the global job market.
📌 Apply Now and take the first step toward becoming a leader in data science and machine learning. Shape your future with confidence!
Professional Certification Course in Data Science with Machine Learning
- ₹172500.00
Features
- 06-Months Live Online Program
- AI Based Curriculum
- AI, Projects and Case Studies
- Topic Wise Case Study Provide
Key Highlights
06-Months Live Online Program
AI Based Curriculum
AI, Projects and Case Studies
Topic Wise Case Study Provide
Latest Tool & Technology Covered
Lifetime LMS Support
1:1 Doubt Session
5 Types of Certificate
Program Objective
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, Memebership, Bitwise), Slicing and Indexing
3 Python Data Structures
4 Python Conditional Statements - IF, ELIF, ELSE
5 Loops in Python
6 Python Functions - Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments, Positional Arguments, Default Parameters, RETURN, LAMBDA.
7 Python Classes and Objects
8 Miscellaneous - Datetime, RegEx, String Formatting, TRY/EXCEPT
1 NumPy - Numerical Python
2 Data Wrangling using PANDAS
3 Data Visualization Using Matplotlib and Seaborn
4 Web Srcaping using Beautiful Soup
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
2.1 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
3.1 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
4.1 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 Introduction to Machine Learning
1.1 What is ML, Why ML, Types of ML, (Training, Validation, and Testing Set)
1.2 Train/Test Split, Preprocessing of Data (LabelEncoder, OneHotEncoder), Standardization of Data
1.3 Hyperparameters, Selection and Fine Tuning of Models, (Main Challenges - Overfitting, Underfitting, Poor Quality Data, Irrelavant Features, etc.)
2.1 Descriptive Statistics - Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers), Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom), Skewness and Kurtosis
2.2 Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling
2.3 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
2.4 Correlation Coffecient, Coefficient of Determination, Simple Linear Regression in Statistics
3.1 Performance Metrics - Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve
3.2 Classification Models - Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Decission Trees
3.3 Ensembling Methods - Bagging (Voting Classifer, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random Forest Classifer, Stacking
3.4 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and L2 Regularization for overfitting, Handling Class Imabalance
3.5 Classification Project - Real World Use Case
4.1 Introduction - Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient Descent
4.2 Performance Metrics - Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.
4.3 Challenges - Heteroskedasticity, Non - Normality of Data, Multicollinearity of Data, etc.
4.4 Regression Models - Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)
4.5 Ensemble Models - Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods
4.6 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization
4.7 Regression Project - Real World Use Case
4.1 Introduction to Unsupervised Learning
4.2 Clustering Methods - KMeans, Hierarchical, Model Based Clustering, DBSCAN Clustering, Anamoly Detection using Gaussian Mixture Models
4.3 Dimensionality Reduction using Principal Component Analysis
4.4 Building and Working of Recommendation Engines
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