Machine Learning Course

|| Machine Learning Certification Course

In a Machine Learning Training Course, you'll explore the foundational principles and practical applications of algorithms that enable computers to learn from data and make predictions or decisions autonomously. Topics typically include supervised learning, where algorithms are trained on labeled data to predict outcomes, and unsupervised learning, which involves discovering patterns and structures in unlabeled data. You'll also delve into reinforcement learning, where agents learn to make sequences of decisions through interaction with an environment. Practical skills in data preprocessing, model selection, evaluation, and optimization using tools like Python, TensorFlow, and scikit-learn are emphasized. By the end of the ML course, you'll be proficient in applying machine learning techniques to solve real-world problems across various industries, from healthcare and finance to natural language processing and computer vision.

The goal of an advanced Python for Machine Learning Course is to give students the know-how they need to solve machine learning challenges using Python programming. The course explores a wide range of machine learning methods and algorithms, assisting students in comprehending how to use Python to efficiently apply these solutions. The Machine Learning Landscape, which also lays the groundwork for later, more advanced subjects. The practical components of machine learning projects, such as data preprocessing, model selection, and evaluation, are covered in later courses. Comprehensive Machine Learning The project is especially concentrated on using the abilities acquired in a practical endeavor. Our Machine Learning course offers a comprehensive exploration of one of the most transformative technologies of our time. Through a blend of theoretical foundations and practical applications, participants will delve into the core concepts, algorithms, and techniques that power machine learning systems. From supervised and unsupervised learning to deep learning and reinforcement learning, students will gain a deep understanding of various machine learning paradigms and their real-world applications. Hands-on projects and coding exercises will enable participants to build and deploy machine learning models, analyze data, and derive valuable insights across diverse domains such as healthcare, finance, marketing, and more. Whether you're a beginner eager to enter the field or a seasoned professional seeking to enhance your skills, this course equips you with the knowledge and tools needed to harness the power of machine learning and drive innovation in today's data-driven world.

Machine Learning Fundamentals is an introductory course designed to provide students with a solid foundation in the principles and techniques of machine learning. The course covers a range of topics, from supervised and unsupervised learning to deep learning and reinforcement learning. Through a combination of lectures, hands-on exercises, and projects, students will gain practical experience in applying machine learning algorithms to solve real-world problems.

Please contact the nearest BIT training institute or send an email to inquiry@bitbaroda.com with any additional questions you may have regarding our Machine Learning training course. We offer a free demo by calling us at +91-9328994901. We offer top-notch Machine Learning classes in Vadodara-Sayajigunj, Vadodara - Waghodia Road, Vadodara - Manjalpur, Ahmedabad, Anand, and Nadiad.

|| Choose Machine Learning Course From BIT

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|| What will I learn?

Understand the basic concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
Review essential mathematical concepts such as linear algebra (vectors, matrices), calculus (derivatives, gradients), and probability theory (Bayesian inference, distributions).
Learn techniques to clean, preprocess, and transform raw data into a format suitable for machine learning algorithms.

Explore methods for visualizing and understanding data distributions, correlations, and anomalies.
Classification: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), Neural Networks.
Regression: Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression. Clustering: K-Means, Hierarchical Clustering, DBSCAN.

|| What will I learn?

Understand the basic concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
Review essential mathematical concepts such as linear algebra (vectors, matrices), calculus (derivatives, gradients), and probability theory (Bayesian inference, distributions).
Learn techniques to clean, preprocess, and transform raw data into a format suitable for machine learning algorithms.
Explore methods for visualizing and understanding data distributions, correlations, and anomalies.
Classification: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), Neural Networks.
Regression: Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression. Clustering: K-Means, Hierarchical Clustering, DBSCAN.

|| Requirements

Basic programming knowledge (preferably in Python)
Understanding of fundamental statistical concepts

Basic knowledge of linear algebra and calculus

|| Requirements

Basic programming knowledge (preferably in Python)
Understanding of fundamental statistical concepts
Basic knowledge of linear algebra and calculus

Our Machine Learning course content is meticulously designed to provide a comprehensive understanding of the foundational principles and advanced techniques in the field. Beginning with an introduction to machine learning concepts, participants will explore topics such as supervised learning, unsupervised learning, and reinforcement learning. They will delve into various algorithms, including linear regression, logistic regression, decision trees, support vector machines, k-nearest neighbors, clustering algorithms, and neural networks. Moreover, participants will gain proficiency in model evaluation and validation techniques, feature engineering, dimensionality reduction, and hyperparameter tuning. Through hands-on projects and practical exercises, students will apply their knowledge to real-world datasets, honing their skills in data preprocessing, model building, and performance evaluation. Additionally, the course covers advanced topics like deep learning, natural language processing, and computer vision, enabling participants to tackle complex problems across diverse domains. By the end of the course, students will be equipped with the expertise to develop and deploy machine learning models effectively, driving innovation and solving challenging problems in various industries.

Python Basic Building
Python Keywords and identifiers
Comments, indentation, statements
Variables and data types in Python
Standard Input and Output

Operators
Control flow: if else elif
Control flow: while loop
Control flow: for loop
Control flow: break & continue
Python Data Structures

Strings
Lists, Lists comprehension
Tuples, Sets
Dictionary, Dictionary Comprehension

Python Functions
Python Builtin Functions.
Python Userdefined Functions.
Python Recursion Functions.
Python Lambda Functions.
Python Exception Handling,
Logging And Debugging

Exception Handling
Custom Exception Handling
Logging With Python
Debugging With Python
Python OOPS
Python Objects And Classes
Python Constructors
Python Inheritance
Abstraction In Python
Polymorphism in Python
Encapsulation in Python

File Handling
Create
Read
Write
Append

Introduction to NumPy
NumPy Array
Creating NumPy Array
Array Attributes,
Array Methods
Array Indexing,
Slicing Arrays
Array Operation
Iteration through Arrays

Introduction to Pandas
Pandas Series
Creating Pandas Series
Accessing Series Elements
Filtering a Series
Arithmetic Operations
Series Ranking and Sorting
Checking Null Values
Concatenate a Series

Data Frame Manipulation
Pandas Dataframe
Introduction Dataframe Creation
Reading Data from Various Files
Understanding Data
Accessing Data Frame Elements using Indexing
Dataframe Sorting
Ranking in Dataframe
Dataframe Concatenation
Dataframe Joins
Dataframe Merge
Reshaping Dataframe
Pivot Tables,
Cross Tables
Dataframe Operations

Checking Duplicates
Dropping Rows and Columns
Replacing Values
Grouping Dataframe
Missing Value Analysis & Treatment
Visualization using Matplotlib
Plot Styles & Settings
Line Plot,
Multiline Plot
Matplotlib Subplots
Histogram, Boxplot
Pie Chart ,Scatter Plot
Visualization using Seaborn
Strip Plot ,Distribution Plot
Joint Plot,
Violin Plot,
Swarm Plot
Pair Plot,
Count Plot
Heatmap
Visualization using Plotly
Boxplot
Bubble Chart
Violin Plot
3D Visualization

EDA and Feature Engineering
Introduction of EDA
Dataframe Analysis using Groupby
Advanced Data Explorations

Working with SQL Using MySQL
Work Bench / SQL Server"
USE, DESCRIBE,
SHOW TABLES
SELECT, INSERT
UPDATE & DELETE
CREATE TABLE
ALTER: ADD, MODIFY, DROP
DROP TABLE, TRUNCATE, DELETE
LIMIT, OFFSET
ORDER BY
DISTINCT
WHERE Clause
HAVING Clause
Logical Operators
Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
GROUP BY
SQL Primary And Foreign Key
Join and Natural Join
Inner, Left, Right and Outer joins

Advance SQL
Subqueries/Nested Queries/Inner Queries
SQL Function And Stored Procedures
SQL Window Function
CTE In SQL
Normalization In SQL

Basic Math
Linear Algebra
Probability
Calculus
Develop a comprehensive understanding of coordinate geometry and linear algebra.
Build a strong foundation in calculus, including limits, derivatives, and integrals.

Descriptive Statistics
Sampling Techniques
Measure of Central Tendency
Measure of Dispersion
Skewness and Kurtosis
Random Variables
Bassells Correction Method
Percentiles and Quartiles
Five Number Summary
Gaussian Distribution
Lognormal Distribution
Binomial Distribution
Bernoulli Distribution

Inferential Statistics
Standard Normal Distribution
ZTest
TTest
ChiSquare Test
ANOVA / FTest
Introduction to Hypothesis Testing
Null Hypothesis
Alternet Hypothesis

Probability Theory
What is Probability?
Events and Types of Events
Sets in Probability
Probability Basics using Python
Conditional Probability
Expectation and Variance

Introduction to Machine Learning
Machine Learning Modelling Flow
"Supervised and Unsupervised
Types of Machine Learning Algorithms

Linear Regression using OLS
Introduction of Linear Regression
Types of Linear Regression
OLS Model
Math behind Linear Regression
Decomposition Variability
Metrics to Evaluate Model
Feature Scaling
Feature Selection
Regularisation Techniques
Ridge Regression
Lasso Regression
ElastivNet Regression

Optimisation Techniques
What is Optimisation?
Gradient Descent
Adagrad Algorithm
Adam Algorithm
Linear Regression with SGD
Prerequisites

Introduction to Stochastic Gradient Descent (SGD)
Preparation for SGD
Workflow of SGD
Implementation of SGD on Linear Regression

Logistic Regression
Maximum Likelihood Estimation
"Logistic Regression Using Sigmoid
Activation Function"
Performance Metrics
Confusion Matrix
Precision, Recall, F1Score
Receiver Operating Characteristic Curve

KNN
Euclidean Distance
Manhattan Distance
Implementation for KNN

SVM
Support Vector Regression
Support Vector Classification
Polynomial Kernel
Cost Function
GridSerchCV

Decision Trees
Decision Tree for Classification
Decision Tree for Regression
ID3 Algorithm
CART Algorithm
Entropy
Gini Index
Information Gain
Decision Tree: Regression
Mean Square Error
PrePruning and PostPruning

Naive Bayes
Introduction to Bayes Theorem
Explanation for naive bayes

Ensemble Technique
Bagging
Random Forest Classifier
Random Forest Regression
Random Forest – Why & How?
Feature Importance
Advantages & Disadvantages

Boosting
Bootstrap Aggregating
AdaBoost
XgBoost
Project For Random Forest
Project Penguin Classification
Project Texi Prediction

Kmeans Clustering
Prerequisites
Cluster Analysis
Kmeans
Implementation of Kmeans
Pros and Cons of Kmeans
Application of Kmeans
Elbow Method
Model building for Kmeans Clustering

Hierarchical Clustering
Types of Hierarchical Clustering
Dendrogram
Pros and Cons of Hierarchical Clustering
Model building for Hierarchical Clustering

DBSCAN Clustering
Introduction for DBSCAN Clustering
implementation of DBSCAN

Principal Components Analysis
Prerequisites
Introduction to PCA
Principal Component
Implementation of PCA
Case study
Applications of PCA
Project on PCA

Time Series Modelling
Understand Time Series Data
Visualising Time Series Components
Exponential Smoothing
ARIMA
SARIMA
SARIMAX
Project on Forecasting
Cloud Basics
ML on Cloud

Introduction of MLOps
What and why MLOps
MLOps fundamentals
MLOps vs DevOps
Why DevOps is not sufficient for MLOps
Challenges in traditional ML Pipeline
DevOps and MLOps tools and platform
What is SDLC?
Types of SDLC
Waterfall vs AGILE vs DevOps vs MLOps

MLOps Foundation
Fundamental of Linux for MLOps and data scientist
Important Linux Commands
Source code managements using GIT
GIT configuration and GIT commands
YAML for Configuration Writing
YAML vs JSON Schema
Docker for Containers
Docker Basic Command, Dockerhub, Dockerfile
Cloud Computing and Cloud Infrastructure
Cloud Service Provider- AWS, GCP, AZURE
Data Managements and Versioning with DVC
Monitoring, Alerting, Retraining With Grafana and
prometheus
Experiment tracking with MLFLOW
Model Serving With BENTOML

End to End project implementation with Deployment implementation with Deployment
Understanding Machine learning Workflow and Project Setup
Project Template Setup with GitHub
Modular workflow Introduction and Implementation
Understanding the Training Pipeline and Its Components

Data Ingestion, Data Transformation Model Trainer Model
Evaluation
Creating Prediction Pipeline and End Point Creation
Continues Integration, Continues Delivery and Continues
Training understanding and Project Deployment

Prompt Engineering
Why Prompt Engineering?
ChatGPT
Few Standard Definitions:
Label
Logic
Model Parameters (LLM Parameters)
Basic Prompts and Prompt Formatting
Elements of a Prompt:
Context
Task Specification
Constraints
General Tips for Designing Prompts:
Be Specific
Keep it Concise
Be Contextually Aware
Test and Iterate
Prompt Engineering Use Cases
Information Extraction
Text Summarization
Question Answering
Code Generation
Text Classification
Prompt Engineering Techniques
N-shot Prompting
Zero-shot Prompting
Chain-of-Thought (CoT) Prompting
Generated Knowledge Prompting

In a Machine Learning course, training features related to project development typically include:

Data Collection and Preprocessing Techniques: Students learn how to gather and preprocess data for training models. This involves techniques such as data cleaning, handling missing values, outlier detection, normalization, and feature scaling.
Feature Engineering: Feature engineering is a crucial aspect of building effective machine learning models. Students learn various techniques for creating new features, transforming existing ones, handling categorical variables, and extracting meaningful information from raw data.
Model Selection: Students are introduced to a variety of machine learning algorithms and techniques, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and ensemble methods (e.g., random forests, gradient boosting). They learn how to choose the most appropriate model for different types of problems and datasets.
Model Training and Evaluation: Students learn how to train machine learning models using training data and evaluate their performance using appropriate metrics. They understand the concepts of overfitting, underfitting, cross-validation, and hyperparameter tuning to ensure robust model performance.
Project-based Learning: Hands-on projects are integral to the course, allowing students to apply their knowledge and skills to real-world problems. They work on end-to-end machine learning projects, from data exploration and preprocessing to model building, evaluation, and deployment.
Tools and Libraries: Students gain proficiency in popular machine learning tools and libraries such as Python, scikit-learn, TensorFlow, and PyTorch. They learn how to use these tools effectively to implement machine learning algorithms, visualize data, and interpret model results.
Ethical Considerations: The course covers ethical considerations in machine learning, including fairness, bias, privacy, and transparency. Students learn about the ethical implications of their models and how to mitigate potential biases and risks.

Overall, the training features in a Machine Learning course provide students with a comprehensive understanding of machine learning concepts and practical skills to tackle real-world problems effectively.

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|| Become An Expert Machine Learning

Machine learning Skill to Master , python ,Data visualization ,Communication Skill ,Domain Knowledge ,Programming Languages ,statistical Analysis

|| Scope of Machine Learning.

In India, the scope of machine learning is significant and growing rapidly across various sectors. Here's a glimpse into how machine learning is being applied and its scope in India:

Healthcare: Machine learning is being used for medical image analysis, disease diagnosis, personalized treatment plans, drug discovery, and predictive analytics for patient outcomes. Indian startups and research institutions are actively involved in developing AI-powered healthcare solutions to address the country's healthcare challenges.
Finance: In the finance sector, machine learning is applied for fraud detection, risk assessment, algorithmic trading, credit scoring, and customer relationship management. Indian banks, financial institutions, and fintech startups are leveraging machine learning to enhance operational efficiency, mitigate risks, and improve customer experience.
E-commerce: Machine learning algorithms power recommendation systems, personalized marketing, demand forecasting, supply chain optimization, and fraud prevention in the e-commerce industry. Indian e-commerce giants and startups are increasingly adopting AI and machine learning to drive sales, improve customer engagement, and streamline operations.
Education: Machine learning is transforming education in India through adaptive learning platforms, personalized tutoring systems, student performance analysis, and automated grading systems. Edtech startups in India are leveraging machine learning to offer scalable and personalized learning experiences to students across diverse educational levels and subjects.
Agriculture: Machine learning is being applied in precision agriculture for crop yield prediction, pest detection, soil health monitoring, and agricultural supply chain optimization. Indian agritech startups are developing AI-powered solutions to address challenges faced by farmers and improve agricultural productivity.
Manufacturing: In the manufacturing sector, machine learning is used for predictive maintenance, quality control, supply chain optimization, inventory management, and process automation. Indian manufacturing companies are embracing AI and machine learning technologies to enhance productivity, reduce downtime, and optimize resource utilization.
Smart Cities: Machine learning plays a crucial role in building smart cities in India by enabling intelligent transportation systems, energy management, waste management, urban planning, and public safety solutions. Indian cities are deploying AI-driven technologies to address urban challenges and improve the quality of life for residents.
Language and Speech Processing: With India's diverse linguistic landscape, machine learning is used for natural language processing (NLP), speech recognition, machine translation, and chatbots in various Indian languages. Indian startups and research organizations are developing AI-powered solutions to bridge the language barrier and facilitate communication in multilingual contexts.

Overall, the scope of machine learning in India is vast and encompasses diverse industries and applications. With growing investments, research initiatives, and collaborations, India is poised to leverage machine learning technologies to drive innovation, economic growth, and societal development.

|| Some Prominent Companies in India that use Machine Learning

Many companies in India are leveraging machine learning across various industries to improve processes, enhance customer experiences, and drive innovation. Some notable examples include:

Flipkart: One of India's largest e-commerce platforms, Flipkart utilizes machine learning for product recommendations, personalized marketing, fraud detection, supply chain optimization, and demand forecasting.
Amazon India: Amazon India employs machine learning algorithms for various purposes, including product recommendations, search relevance, inventory management, customer service automation, and delivery route optimization.
Paytm: Paytm, a leading digital payments and financial services company, integrates machine learning into its platform for fraud prevention, risk management, customer segmentation, and personalized offers.
Ola: Ola, a prominent ride-hailing service in India, uses machine learning for dynamic pricing, route optimization, demand prediction, driver allocation, and customer support automation.
Zomato: Zomato, a popular food delivery and restaurant discovery platform, applies machine learning techniques for personalized recommendations, menu analysis, delivery time estimation, and customer sentiment analysis.
Reliance Jio: Reliance Jio, a telecommunications company, employs machine learning for network optimization, customer analytics, churn prediction, targeted advertising, and content recommendation on its digital platforms.
Tata Consultancy Services (TCS): TCS, one of India's largest IT services firms, offers machine learning solutions to its clients across industries, including banking, retail, healthcare, and manufacturing, for tasks such as predictive maintenance, risk assessment, and customer segmentation.
Infosys: Infosys provides machine learning services to global clients for business process automation, predictive analytics, anomaly detection, and personalized marketing, leveraging its expertise in AI and data science.
Wipro: Wipro delivers machine learning solutions for enterprises in areas such as predictive maintenance, supply chain optimization, customer experience enhancement, and fraud detection, helping clients drive digital transformation initiatives.
HDFC Bank: HDFC Bank, one of India's largest private sector banks, utilizes machine learning for credit risk assessment, fraud detection, customer segmentation, personalized marketing campaigns, and chatbot-based customer service.

|| Machine Learning Career Option.

In India, machine learning offers a wide range of career options and abundant job opportunities across various sectors. Some prominent career paths and job roles in machine learning include:

Data Scientist: Data scientists analyze large datasets to extract insights and make data-driven decisions. They use machine learning algorithms and statistical techniques to uncover patterns, trends, and correlations in data. Data scientists are in high demand across industries such as finance, healthcare, e-commerce, and telecommunications.
Machine Learning Engineer: Machine learning engineers design, develop, and deploy machine learning models and systems. They work on tasks such as feature engineering, model training, optimization, and scaling for production environments. Machine learning engineers are essential for building AI-powered applications, recommendation systems, and predictive analytics platforms.
AI Researcher: AI researchers focus on advancing the state-of-the-art in machine learning and artificial intelligence. They conduct research in areas such as deep learning, natural language processing, computer vision, and reinforcement learning. AI researchers often work in academia, research institutions, or corporate research labs.
AI Product Manager: AI product managers oversee the development and implementation of AI-powered products and solutions. They collaborate with cross-functional teams to define product requirements, prioritize features, and drive product strategy. AI product managers need a strong understanding of both technical and business aspects of AI.
Business Analyst/Data Analyst: Business analysts and data analysts use machine learning techniques to derive insights from data and inform business decision-making. They perform tasks such as data preprocessing, exploratory data analysis, and reporting to support strategic planning and operational efficiency.
AI Ethics and Policy Expert: With the increasing adoption of AI and machine learning, there is a growing need for professionals specializing in AI ethics, fairness, transparency, and policy. AI ethics experts work on issues related to bias mitigation, privacy protection, and responsible AI deployment.
Consultant/Advisor: Consultants and advisors provide strategic guidance to organizations on adopting and implementing machine learning solutions. They assess business requirements, recommend suitable AI technologies, and help companies integrate machine learning into their workflows effectively.
Freelancer/Independent Contractor: Many machine learning professionals choose to work as freelancers or independent contractors, offering their expertise to multiple clients on a project basis. Freelancers may work on diverse projects ranging from data analysis and model development to AI strategy consulting.

|| Job Roles and Salary

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|| Average Salary for Machine Learning Professionals.

As of my last update in January 2022, the average salary for machine learning professionals in India can vary based on factors such as experience, skill set, industry, and location. However, I can provide you with a general idea based on available data:

Entry-Level Positions (0-2 years of experience):
Machine Learning Engineer: ₹500,000 - ₹1,000,000 per year
Data Scientist: ₹600,000 - ₹1,200,000 per year.

Mid-Level Positions (2-5 years of experience):
Machine Learning Engineer: ₹800,000 - ₹1,800,000 per year
Data Scientist: ₹1,000,000 - ₹2,500,000 per year

Senior-Level Positions (5+ years of experience):
Machine Learning Engineer: ₹1,500,000 - ₹3,500,000 per year
Data Scientist: ₹2,000,000 - ₹5,000,000 per year

These figures are approximate and can vary depending on factors such as the specific company, location (e.g., salaries may be higher in cities like Bangalore, Mumbai, or Hyderabad compared to smaller cities), and the candidate's qualifications and negotiation skills.

|| Top Hiring Companies

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|| The Placement Opportunities for Machine Learning Professionals in India

In India, the demand for professionals skilled in machine learning is rapidly growing across various industries, offering abundant placement opportunities for aspiring candidates. With the country emerging as a global technology hub, numerous companies, ranging from established tech giants to innovative startups, are actively seeking talent proficient in machine learning. Industries such as e-commerce, finance, healthcare, cybersecurity, and manufacturing are increasingly leveraging machine learning techniques to drive innovation and gain competitive advantage. Roles such as machine learning engineer, data scientist, AI researcher, and business analyst are in high demand, offering lucrative salaries and career growth prospects. Additionally, India's vibrant startup ecosystem provides ample opportunities for machine learning enthusiasts to work on cutting-edge projects and contribute to groundbreaking innovations. As businesses continue to prioritize data-driven decision-making, the demand for machine learning talent in India is expected to soar, making it an opportune time for individuals to pursue careers in this dynamic and rapidly evolving field.

Tech giants like Google, Microsoft, Amazon, and IBM have established a significant presence in India and continuously recruit machine learning talent for their research, development, and product teams. Additionally, Indian-based companies such as Flipkart, Ola, Paytm, and Zomato are increasingly investing in machine learning to enhance their products and services, thereby creating more opportunities for professionals in this field. Furthermore, consulting firms and research organizations also offer avenues for machine learning professionals to apply their skills in solving complex problems across various domains. Overall, the placement opportunities for machine learning professionals in India are diverse, promising, and continually expanding as businesses across industries recognize the importance of harnessing data-driven insights for competitive advantage and innovation.

|| Empowering Your Career Transition From Learning To Leading

Megha Bhatt

Megha Bhatt, an ML Engineer at Cognizant, demonstrates prowess by leveraging unique tools such as Alteryx for advanced data blending and Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

Darshna Dave

Darshna Dave, excelling as a Data Analyst at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-Business Analytics course, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post learning Data Analytics course, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ after studying a Full-Stack Data science course from BIT. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, which showcases his expertise in essential data processing tools.

Vanshika Patel

Vanshika Patel, demonstrating mastery as a Sr. ML Engineer at Genpact, demonstrates proficiency in essential tools such as Python, TensorFlow, and scikit-learn. Her adept use of these critical software contributes to effective machine learning solutions.

Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ post our IT institute. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, showcasing expertise in essential data processing tools.

|| Get Machine Learning Certification

Three easy steps will unlock your Machine Learning Certification:

Finish the online / offline course of Machine Learning Course and the Assignment
Take on and successfully complete a number of industry-based Projects
Pass the Machine Learning certification exam

The certificate for this Machine Learning course will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile.

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|| Machine Learning Certification Course

|| Choose Machine Learning Course From BIT

|| What will I learn?

|| What will I learn?

|| Requirements

|| Requirements