Palin Analytics : #1 Training institute for Data Science & Machine Learning
New Announcement! The upcoming batch of data science is from 25th Dec at 09:00 PM.
Machine Learning
Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on developing systems and algorithms that can learn and make predictions or decisions based on data. The primary goal of machine learning is to enable computers to learn patterns, relationships, and insights from data without being explicitly programmed for a specific task. In other words, machine learning algorithms allow systems to improve their performance on a task as they are exposed to more data over time. Now a days in Business world data analytics plays a vital role to form decisions more scientifically and help to increase operational efficiency. We provide one of the best Machine Learning Course, Training & Certification in Gurgaon.
- English
- English, Hindi
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Upcoming Batch !!!
Starting from Upcoming Saturday
10:00 am – 2:00 pm
- 30 Hours Live Online Classroom Sessions
- 8 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
5,162 Students Enrolled
What we will learn
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Training Data
- Features and Labels
- Algorithms
- Supervised Learning
- Unsupervised Learning
- Training and Testing Phases
- Feature Engineering
- Model Evaluation
- Overfitting and Underfitting
- Deployment
Machine learning is a versatile field with applications in various domains, including finance, healthcare, marketing, natural language processing, image recognition, and many others. The field continues to evolve with advancements in algorithms, model architectures, and techniques, contributing to the broader landscape of artificial intelligence. In this course you will get to learn apply machine learning algorithms the essential part of data science.
By the end of this you will be able to extract, read and write data from csv files, data cleansing, data manipulation, data visualization, run inferential statistics, understand the business problems, based on problems you will be able to select and apply machine learning models and deploy it.
Who can go for this
Machine learning is a field which is meant for a diverse range of individuals, and it can be learned by people with varying backgrounds and interests. Programmers, Statisticians, Data Scientist, Computer Science Students, Here are some groups of people who can benefit from learning machine learning
Want to discuss your roadmap to be a Machine Learning Expert?
Are you interested in pursuing a career as a machine learning expert, it’s essential to create a roadmap that outlines the key steps and milestones along the way. Join us for an inspiring conversation where we will deep dive into your own journey and discuss the clear cut roadmap to become a data scientist. Let’s start the journey to be a data scientist in the exciting world of data science together!
Advantages
Countless Batch Access
Learn from anywhere
Industry Endorsed Curriculum
Industry Expret Trainers
Career Transition Guidance
Interview Preparation Techniques
Shareable Certificate
Real-Time Projects
Class recordings
Course Mentor
Tushar Anand
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi I am Tushar and I am super excited that you are reading this.
Professionally, I am a data science management consultant with over 8+ years of experience in Banking, Capital Market, CCT, Media and other industry. I was trained by best analytics mentor at dunnhumby and now a days I leverage Data Science to drive business strategy, revamp customer experience and revolutionize existing operational processes.
From this course you will get to know how I combine my working knowledge, experience and qualification background in computer science to deliver training step by step.
Course Content
| LESSONS | LECTURES | DURATION |
|---|
- Definition of Machine Learning
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- Applications of Machine Learning
- Overview of the Machine Learning Workflow
- Linear Algebra Concepts
- Calculus Basics
- Probability and Statistics for Machine Learning
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- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
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- Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Cross-Validation
- Hyperparameter Tuning Techniques
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- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Learning
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- Basics of Neural Networks
- Perceptron and Multilayer Perceptron
- Backpropagation Algorithm
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- Introduction to Deep Learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer Learning
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- Text Preprocessing
- Bag of Words Model
- Word Embeddings (e.g., Word2Vec, GloVe)
- Sentiment Analysis
- Image Processing Basics
- Convolutional Neural Networks for Image Classification
- Object Detection
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What is machine learning | 1 Lecture | 15:00 |
Different stages of ML project | 1 Lecture | 18:00 |
Supervised vs Unsupervised ML | 1 Lecture | 25:00 |
Algorithms in Supervised and Unsupervised learning | 1 Lecture | 28:00 |
Introduction to Sklearn | 1 Lecture | 21:00 |
Data preprocessing | 1 Lecture | 22:00 |
Scaling techniques | 1 Lecture | 26:00 |
Training /testing / validation datasets | 1 Lecture | 28:00 |
Feature Engineering | 1 Lecture | 38:00 |
How to deal with Categorical Variables – Dummy variables | 1 Lecture | 26:00 |
Categorical embedding | 1 Lecture | 16:00 |
- Basics of Reinforcement Learning
- Markov Decision Processes
- Q-Learning and Deep Q Networks (DQN)
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- Model Deployment Strategies
- Integration with Web Applications
- Model Monitoring and Maintenance
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- Bias and Fairness in Machine Learning
- Ethical Considerations in AI
- Responsible AI Practices
| TOTAL | 28 LECTURES | 84:20:00 |
|---|
What Our Students Say About Us
Palin Analytics
We are dedicated to empowering professionals as well as freshers with the skills and knowledge which is needed to upgrade in the field of Data Science. Whether you’re a beginner or a professional, our structured training programs are well designed to handle all levels of expertise.
Are you ready to explore your Data Science adventure? Watch a live recorded demo video now and discover the endless possibilities way of teaching, way of handling queries. Awaiting for you at Palin Analytics!
Student feedback
Deepika
Kushal is a good instructor for Data science. He cover all real world projects. He provided very good study materials and high support provided by him for interview prepration. Overall best online course for Data Science.Â
Deepak Jaiswal
This is a very good place to jump start on your focus area. I wanted to learn python with a focus on data science and i choose this online course. Kushal who is the faculty, is an accomplished and learned professional. He is a very good trainer. This is a very rare combination to find.Â
Instructor
Thank you Deepak…
Add Reviews about your experience with us.
FAQ's
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions based on data. The primary distinction between machine learning and traditional programming lies in how they handle tasks and acquire knowledge:
Supervised learning involves training a model on a labeled dataset, where the input data is paired with corresponding output labels.
Unsupervised learning involves training a model on an unlabeled dataset where the algorithm tries to find patterns or structures in the data without explicit guidance on the output.
Reinforcement learning involves an agent interacting with an environment and learning to make decisions by receiving feedback in the form of rewards or penalties.
Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support. We Provide one of the best online data science course.
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
you will have access to all the recordings, you can go through the recording as many times as you want.
During the training and after as well we will be on the same slack channel, where trainer and admin team will share study material, data, project, assignment.
Data analytics is the process of analyzing, interpreting, and gaining insights from data. It involves the use of statistical and computational methods to discover patterns, trends, and relationships in data sets.
Data analytics involves a variety of techniques, such as data mining, machine learning, and data visualization. Data mining is the process of discovering patterns and relationships in large data sets, while machine learning is a type of artificial intelligence that enables computer systems to learn from data and improve their performance over time. Data visualization is the process of presenting data in a visual format, such as charts and graphs, to help people understand complex data sets.
The goal of data analytics is to turn data into insights that can be used to make informed decisions. This can involve identifying opportunities for business growth, improving operational efficiency, or predicting future trends and outcomes. Data analytics is used in many industries, including finance, healthcare, marketing, and government, to name a few.
In summary, data analytics is the process of analyzing data to gain insights and make informed decisions. It involves a range of techniques and tools to extract valuable information from data sets.
There are many companies that offer internships in data analytics. Some of the well-known companies that provide internships in data analytics are:
Google: Google offers data analytics internships where you get to work on real-world data analysis projects and gain hands-on experience.
Microsoft: Microsoft provides internships in data analytics where you can learn about big data and machine learning.
Amazon: Amazon offers data analytics internships where you can learn how to analyze large datasets and use data to make business decisions.
IBM: IBM provides internships in data analytics where you can work on real-world projects and learn about data visualization, machine learning, and predictive modeling.
Deloitte: Deloitte offers internships in data analytics where you can gain experience in areas such as data analytics strategy, data governance, and data management.
PwC: PwC provides internships in data analytics where you can learn how to analyze data to identify trends, insights, and opportunities.
Accenture: Accenture offers internships in data analytics where you can work on projects related to data analytics, data management, and data visualization.
Facebook: Facebook provides internships in data analytics where you can gain experience in areas such as data modeling, data visualization, and data analysis.
These are just a few examples of companies that provide internships in data analytics. You can also search for internships in data analytics on job boards, company websites, and LinkedIn.
you can write your questions at info@palin.co.in we will address your questions there.