This website uses cookies to personalize content and analyse traffic in order to offer you a better experience. Cookie policy

Accept

|| Choose Machine Learning Course From BIT

Features of BIT Coaching Classes ,Comprehensive Curriculum in BIT ,Hands on Learning ,Interactive classes at BIT ,Career Support at BIT,Advantages of taking Admission at Bit

Certificate

|| 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.


    Machine learning learning pathways ,machine learning roadmap ,python  , Advanced Python, sql ,mathematics ,data science and ML ,Project Development



    • 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.

Get in touch

|| Become An Expert Machine Learning

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

Certificate

|| Scope of Machine Learning.

Read more
placement report

|| Some Prominent Companies  in India that use Machine Learning

Read more

|| Machine Learning Career Option.

Read more

|| Job Roles and Salary

Machine Learning Job Roles ,Deep Learning Engineer ,machine learning Engineer ,AI Research scientist ,AI product manager ,data scientist ,Data Engineer

Certificate

|| Average Salary for Machine Learning Professionals.

Read more

|| Top Hiring Companies

Hiring Companies ,Top Companies ,Job Placement ,PSI ,SWIGGY ,NVIDIA,TESCO ,CISCO ,Top Hiring Companies at BIT

Certificate

|| 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

User Image
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.

User Image
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.

User Image
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.

|| 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.

Certificate

|| Frequently asked question

A Machine Learning Course teaches participants the fundamental concepts, techniques, and applications of machine learning, a subset of artificial intelligence focused on building algorithms that can learn from data and make predictions or decisions without explicit programming.

This course is suitable for individuals interested in pursuing a career in machine learning, data science, or related fields. It caters to beginners with little to no background in machine learning as well as professionals looking to expand their knowledge and skills in this area.

Most reputable Machine Learning courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Yes, many Machine Learning courses are available online, offering flexibility in terms of timing and location. Online courses often include video lectures, interactive coding exercises, and discussion forums to facilitate learning.

Yes, Machine Learning courses typically include hands-on projects and case studies to apply the techniques learned in real-world scenarios. This practical experience is crucial for developing proficiency and building a portfolio to showcase to potential employers.

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

You will need: A computer with internet access Python installed on your system (preferably Anaconda distribution) IDE or code editor (such as Jupyter Notebook, VS Code, or PyCharm) Libraries such as NumPy, pandas, Matplotlib, Scikit-learn, TensorFlow/Keras (installation instructions will be provided)

After completing the course, you might consider taking more advanced courses, participating in workshops, or working on more complex projects. You could also explore specializations in areas such as natural language processing, computer vision, or reinforcement learning.

Projects can range from simple applications of ML algorithms to real-world problems, such as: Building a spam email classifier. Predicting housing prices. Developing recommendation systems. Implementing a simple neural network for image recognition.
-->
Call Now!