AI – Machine Learning Certification Course in Bangalore with AI & ChatGPT
- Earn Industry-recognized certification
- Build real-time projects with industry-aligned tools
- Live Interactive sessions from industry veterans
- Updated curriculum designed for AI-era
- Dedicated Job Assistance and Resume Building
Success Stories – They Believed, Learned & Achieved!
Our learners are working in leading organizations
Gain industry-ready skills and earn an official certificate from DataFlair.
Full Stack AI & Machine Learning Course in Bangalore Curriculum
- Concepts of Machine Learning
- Difference between AI and ML
- Machine Learning Types
- Important ML Terminologies
- Working with NumPy, Pandas & Matplotlib
- Features and Labels in ML
- Training and Testing in ML
- Overfitting vs. Underfitting
- Mathematical Foundations
- Algorithm Survey & Use Cases
- The Machine Learning Workflow
- Popular Machine Learning Algorithms
- Reinforcement Learning
- Types of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Machine Learning in Finance and Banking
- Machine Learning in Retail
- Machine Learning in Healthcare
- Machine Learning in Logistics and Supply Chain
- Machine Learning in the Technology Industry
- Machine Learning in Manufacturing
- Machine Learning in Agriculture
- Introduction to Regression
- Regression Real-World Use Cases
- Types of Regression Problems
- Linear Regression
- Evaluation Metrics
- Common Challenges with Regression
- Linear vs Polynomial vs Ridge vs Lasso Regression
- Regression Industry Applications
- What is Classification?
- Regression vs. Classification
- Classification Real-World Applications
- Types of Classification Problems
- Data Preparation for Classification
- Label Encoding vs One-Hot Encoding
- Feature Scaling in ML
- Train-Test Split in ML
- Handling Imbalanced Data in Classification
- Model Evaluation Metrics in Classification
- Accuracy in Classification
- Precision, Recall, and F1-Score in Classification
- Confusion Matrix in Classification
- ROC Curve and AUC Score in Classification
- Overfitting & Underfitting in ML
- Regularization Techniques in Classification
- What is k-NN?
- How k-NN Works
- KNN Distance Metrics
- Choosing the Right ‘k’ in KNN
- Data Preparation for KNN
- Categorical Features Handling in KNN
- Strengths and Limitations of KNN
- Evaluation Metrics in KNN
- KNN Model Tuning
- Cross-Validation in KNN for Optimal ‘k’
- Overview of Decision Tree
- Classification of Decision Trees
- Elements of a Decision Tree
- Decision Tree Splitting Criteria
- Gini Impurity in Decision Tree
- Entropy & Information Gain in Decision Tree N
- Advantages and Limitations of Decision Tree
- Pre-Pruning and Post-Pruning in Decision Tree
- Decision Tree Evaluation Metrics
- Decision Tree Hyperparameter Tuning
- Decision Tree Cross-Validation
- What is Random Forest?
- Reasons To Use Random Forest Over A Single Tree
- Concepts Behind Random Forest
- Random Forest Working System
- Random Forest Hyperparameters
- Random Forest Advantages and Limitations
- Random Forest Use Cases
- What is unsupervised learning?
- Supervised vs unsupervised learning
- Where to use Unsupervised learning
- Application of Unsupervised learning
- Popular Algorithms in Unsupervised Learning
- Real-world use cases of Unsupervised learning
- Evaluation Metrics
- Preface of Clustering Algorithms
- What is meant by Clustering?
- Differences Between Supervised and Unsupervised Learning
- Classification vs Clustering
- K-Means Clustering
- K-means Clustering: Real-world industry use cases
- Elbow method in K-means Clustering
- Hierarchical Clustering
- Agglomerative vs Divisive Approach in Hierarchical Clustering
- Hierarchical Clustering: Real-world industry use cases
- Dimensionality Reduction
- Enterprise Applications Overview
- Predictive Modeling & Segmentation
- Personalization Engines
- Time Series & Anomaly Detection
- What is Scikit-Learn and its Importance?
- Attributes of Scikit-Learn for Machine Learning
- Installing Scikit-Learn and Setting Up the Environment
- Gain knowledge about the Machine Learning Workflow with Scikit-Learn
- Deep Learning vs Machine Learning
- Deep Learning Introduction
- Deep Learning Case Studies in Industry
- Why Deep Learning?
- Need for Deep Learning in Industry
- Why Deep Learning is in Demand
- Key Deep Learning Terminologies
- What are Artificial Neural Networks
- History of Deep Learning
- Applications of Deep Learning
- Convolutional Neural Networks
- Activation Functions in Deep Learning
- Optimizers in Deep Learning
- ResNet50
- Vanishing gradients
- Transfer Learning
- DenseNet121
- Recurrent Neural Networks
- ANN vs CNN vs RNN
- LSTM
- RNN vs LSTM
- LSTM in deep learning
- Deep learning architectures: perceptron, feedforward neural networks
- Activation functions and network initialization
- Backpropagation algorithm and training neural networks
- Optimization techniques for deep learning: Adam, RMSprop, etc.
- Perceptron & Feedforward Designs
- Activation & Initialization
- Backpropagation & Optimizers
- Introduction to CNNs for image analysis
- Convolution and pooling layers
- Object detection and image segmentation
- Transfer learning with pre-trained CNNs
- RNN fundamentals: architecture, hidden states, and memory cells
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
- Sequence generation and language modeling
- Applications of RNNs: text generation, machine translation, speech recognition
- Details on OpenCV?
- Installing OpenCV Using pip
- Setting Up OpenCV in PyCharm
- How to Install and Use OpenCV in PyCharm
- Using imread() and imwrite() Functions to Load and Save Images
- Getting OpenCV Suggestions in PyCharm
- Displaying Images in OpenCV
- Resizing Images with OpenCV
- Rotating Images in OpenCV
- Merging Multiple Images
- Flipping Images Using Bitwise NOT in OpenCV
- Changing Image Colors with cvtColor() in OpenCV
- Capturing Video with OpenCV
- Recording Video from Webcam
- Drawing Lines on Images and Videos
- Drawing Circles on Images
- Adding Text to Images in OpenCV
- Difference Between add() and addWeighted() Functions
- Working with Image Properties (Shape & Size)
- Cropping Images with OpenCV
- Using hconcat() and vconcat() for Image Concatenation
- Blurring Images in OpenCV
- Creating a Graphical Image Rotation App with Tkinter
- Showing Multiple Dynamic Images with Tkinter
- Dynamically Resizing Images Using Tkinter
- Basic Video Operations with Dialog Box in OpenCV
- Resizing Videos and Images with Specified Dimensions
- Drawing Various Shapes in OpenCV
- Applying Image Transformations in OpenCV
- Converting BGR Images to RGB in OpenCV
- Using Different Color Codes in OpenCV
- Detecting Edges Using Canny Edge Detection in OpenCV
- What is meant by Natural Language Processing?
- Applications of NLP
- Text Preprocessing
- Text Classification
- Sentiment Analysis
- Artificial Intelligence Fundamentals
- Understanding Gen AI
- Agentic AI Essentials
- Historical evolution: From reactive systems to proactive agents
- Working with Agentic AI
- Applications of Agentic AI
- Agent vs. Model vs. Tool: Understanding the distinction
- Agentic AI vs AI Agents
- Multi-Agent Systems
- Core characteristics of AI agents: autonomy, proactivity, adaptability
- Real-world use cases of AI agents
- Agent architectures
- Open-source agent frameworks
Objectives of the AI and Machine Learning Course in Bangalore
After this Machine Learning course ends, you will:
- Understand the Core Concepts of Machine Learning: Know about it, the working mechanism, and the reasons behind its use in almost every industry at present.
- Master Key Machine Learning Algorithms: Develop practical experience by using popular algorithms like Linear Regression, Decision Trees, K-Nearest Neighbours, and others.
- Work with Real-Time Data: Learn how to collect, clean, and refine raw data for creating correct and trustworthy Machine Learning models.
- Develop Practical Problem-Solving Skills: ML techniques usage in real-world situations, like predicting sales, customer segmentation, detecting fraud, and others.
- Build and Evaluate Machine Learning Models: Learn how to train models, calculate their performance, and enhance accuracy using hands-on tools and libraries, such as Python, Pandas, and Scikit-Learn.
This AI/ML online certification training in Bangalore is designed to make you ready for the industry with immediately applicable skills after learning.
Why should you learn AI and Machine Learning?
Machine Learning (ML) is not just a popular concept, but a skill that is required for hiring in companies. ML has vast applications like predicting customers’ behaviour patterns, detecting fraud, or strong automatic cars.
During the online Machine Learning classes in Bangalore, you will:
- Understand Machine Learning from Scratch: There is no complex terminology. We begin from the basics and slowly move towards advanced concepts.
- Practice with Real-Life Datasets: You will work on practical issues and learn how things work in the real world.
- Develop Job-Ready Skills: Here, we teach those topics and tools that are used in industries at present.
- Boost Your Career Growth: Including Machine Learning skills in your profile can unlock thrilling job opportunities and a better pay scale.
To get an idea about smart technologies’ working mechanisms and build solutions with real impact, this course is the best place to start.
What is Machine Learning?
Machine Learning is about making computers learn from experience and patterns similar to humans. Here, machines find patterns on their own by analysing fed bulk data rather than step-by-step different instructions. Without doing explicit programming for each task, the machine gets better at solving issues, making decisions, and predicting future outcomes over time.
Some real-life examples of Machine Learning work are recommendations on YouTube for videos of your liking, filtering out spam emails automatically, and many others.
Reasons Why Machine Learning is All Around Today:
- More than 97% of large organizations use Machine Learning to enhance their services and stay updated.
- The Machine Learning industry is going to create enormous career opportunities, as this industry is expected to cross $225 billion by 2030.
- ML technology is changing the future in all sectors, whether it be healthcare, finance, entertainment, or self-driving cars.
Machine Learning is not only about code, but you are making machines to learn and improve with time. Machine Learning unlocks thrilling and effective innovations for customer behaviour prediction, fraud detection, or building smart applications.
Want to see how machines can learn and make your life smarter? Dive into the technology world with this course.
What to do before you begin?
This AI and Machine Learning online certification course in Bangalore is designed for inquisitive minds, whether they are college students seeking career options, experienced professionals looking to upskill, or complete novices to this field. It assists in making things smooth and enjoyable if you have:
- Some Programming Experience: You don’t have to be a coding expert, but it will be easy to solve the practical exercises if you have some knowledge about Python or any similar programming language.
- Comfort with Basic Math: It’s helpful to know simple math concepts such as algebra, probability, and basic statistics. But we will recall all points when these topics arise.
- A Problem-Solving Mindset: You are already thinking like a Machine Learning professional if you like solving puzzles, exploring patterns, or asking queries.
It is not necessary to have prior experience in Machine Learning. We begin with the basics and slowly move towards advanced concepts by increasing your practical knowledge, and you are ready to apply skills.
We’ll provide clarity to your curiosity!
Who should choose the best AI & Machine Learning courses in Bangalore?
Anyone captivated by learning Machine Learning can choose this course. No requirement of being a tech expert or having an advanced background. Some basic knowledge about computers and a willingness to learn are sufficient.
- College Students: Start here if you want an early start to the AI and Machine Learning world.
- Working Professionals: Seeking skills upgrade for better job opportunities? This course will help.
- Complete Beginners: Even if you are new to data and programming, you can still follow along with ease.
- Career Switchers: If you have a non-technical background, no issues! We begin from scratch and grow your skills steadily.
These AI/ML classes in Bangalore are designed to answer your curiosity about how technology creates intelligent decisions and your willingness to learn these skills.
You can expect these benefits by enrolling in the AI and Machine Learning course in Bangalore:
- Practically understand Machine Learning: You will get real projects using actual datasets for understanding how Machine Learning works in practical world situations.
- Start from Basics, Grow to Advanced: This course starts from basics to advanced concepts, whether you are a novice or have some knowledge.
- Industry-Relevant Tools and Techniques: Learn industry-relevant tools like Python, Pandas, and Scikit-Learn, and methods used by top companies’ professionals.
- Boost Your Career Opportunities: Adding Machine Learning to your skillset, you are eligible for demanding roles like Data Analyst, ML Engineer, and AI Specialist.
- Certificate of Completion: Gain a certificate to illustrate your skills and enhance your resume for better job offers.
This AI & Machine Learning course in Bangalore with placement is designed to provide valuable, future secure, and high-demand skills.
Jobs after Learning this Best AI & Machine Learning courses in Bangalore
After completion of this Machine Learning course, you will be eligible for some of the most fascinating and well-paying roles in the tech industry. Different sectors like finance, healthcare, e-commerce, and entertainment are actively recruiting skilled Machine Learning professionals.
You can explore the following top career options:
- Machine Learning Engineer: Create innovative systems that can learn from data and make decisions as humans do.
- Data Scientist: Helping companies to make better choices by working with large datasets and finding functional patterns based on facts, not guesses.
- AI/ML Developer: Create smart applications and tools that work on Machine Learning and AI to make daily tasks easy and efficient.
- Data Analyst: Find patterns, generate reports, and support business strategies by working with data.
- Business Intelligence (BI) Analyst: By studying market trends and business data, they help companies to make judicious choices.
- Research Analyst: Search for new methods to apply ML techniques in advanced research and development projects.
You will not be restricted to the tech industry. Machine Learning skills will be required in every sector seeking to innovate and grow.
Our students are working in leading organizations
AI and Machine Learning Course FAQs
The online AI and Machine Learning course in Bangalore is a training program that provides learners with practice job-ready skills. Anyone interested in learning this course can join, whether you are a beginner or an experienced professional.
Having basic knowledge of any programming language, especially Python, is helpful but not compulsory. We’ll guide you through the coding essentials required for Machine Learning.
We start teaching from scratch, and learners from non-coding backgrounds can easily learn. We include Python concepts that are needed for AI and Machine Learning.
All the live classes are conducted online by the expert instructors, providing an interactive learning environment.
You will be doing coding practice during the live session to learn implementation. As this course is designed to provide a practical learning experience
Our industry expert mentors are available to help students in their learning journey on understanding difficult concepts by making them easy to learn using simple language.
Yes, you will get a completion certificate that can be added to your resume and LinkedIn profile to show your skills.
These online AI & Machine Learning classes in Bangalore is practical. The conceptual theory is explained, but the primary focus is to provide hands-on experience and solve real-time problems.
Yes, after completing this course, you will be capable of training models, measuring their performance, and improving accuracy using practical tools and libraries.
Yes, beginners from non-technical backgrounds can also enroll for this course as we make learning easy by starting from the basics.
