Best Machine learning Training

Best Machine learning Training by Sapalogy offers you the best Machine learning training in nagpur for freshers and experience candidate in nagpur, with expert guidance and 100% guaranteed placement assistance. Our Machine learning training course covers every technical topics in details and the student is left with real-time hand on experience.

Our practical, Job-oriented training program will not only provide you with a certificate but also with knowledge equivalent to around 2+ years of field experience. We value your time as much as over. Hence we invented to train you in Machine learning in just 4 months.

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What is Machine learning ?

Machine learning is a subset of synthetic intelligence that includes the development of algorithms and models that permit computers to research from information and enhance their overall performance over the years.
It specializes in the introduction of systems which can mechanically examine and interpret information, permitting machines to make predictions or decisions without express programming.
Machine mastering algorithms use statistical techniques to permit computers to pick out patterns, make sense of complex facts, and adapt their behavior based totally at the data available.
The aim of machine mastering is to expand systems that may generalize from past reports, allowing them to manage new, unseen facts and tasks efficaciously.
Sapalogy cover all the Machine learning skills to get you hired in 2024.
Sapalogy taining provides Machine learning training in offline and online mode. Support with real time Machine learning project based training.
IT background, non IT background, freshers, experience can start their career in Machine learning irrespective of their background.
Sapalogy is the best training institute in nagpur with the 100% job opportunities.

What is Machine Learning?| Job Opportunities in 2024 | Job Roles in Machine Learning

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Roadmap to learn Machine learning with Sapalogy

1. Introduction

What is Machine learning?
Machine learning course
Machine learning certification
Machine learning jobs in india
Machine learning jobs in nagpur

2. Foundational Skills

Learn programming languages such as Python or R.
Develop a solid understanding of mathematics and statistics.
Acquire proficiency in using essential tools and libraries like Jupyter Notebooks, NumPy, and Pandas.

3. Data Acquisition and Cleaning

Gain expertise in obtaining and collecting data from various sources.
Master techniques for cleaning and preprocessing data to ensure its quality and reliability.
Explore data storage solutions and databases.

4. Exploratory Data Analysis (EDA)

Learn exploratory data analysis techniques to understand the structure and patterns within the data.
Use data visualization tools like Matplotlib and Seaborn to create meaningful plots.
Develop the ability to ask relevant questions about the data and derive insights.

5. Machine Learning Fundamentals

Understand the basics of machine learning algorithms and their applications.
Learn how to train and evaluate models using popular frameworks like scikit-learn.
Explore supervised and unsupervised learning techniques.

6. Advanced Machine Learning and Deep Learning

Dive into more advanced machine learning topics such as ensemble methods and hyperparameter tuning.
Familiarize yourself with deep learning concepts and frameworks like TensorFlow or PyTorch.
Experiment with neural networks and understand their architectures.

7. Model Deployment and Communication

Learn how to deploy machine learning models to production environments.
Develop skills in model interpretation and explainability.
Practice effective communication of data-driven insights to both technical and non-technical stakeholders.

8. Resume preparation

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Key features of Machine learning Training

42+ Hours course duration

100% Job oriented Training

Free demo class available

Industry expert faculties

Completed 200+ Batches

Certifaction guidance

Machine learning Training syllabus

Python Basics
Python Functions and Packages
Working with Data Structures, Arrays,
Vectors & Data Frames
Google colab notebook
Pandas, NumPy, Matplotlib, Seaborn

Descriptive Statistics
Probability & Conditional Probability
Hypothesis Testing
Inferential Statistics
Probability Distributions

Data Types
Dispersion & Skewness
Uni & Multivariate Analysis
Data Imputation
Identifying and Normalizing Outlier

Linear Regression
Multiple Variable Linear Regression
Logistic Regression
Naive Bayes Classifiers
k-NN Classification
Support Vector Machines
3 PROJECTS ON SUPERVISED LEARNING

K-means Clustering
Hierarchical Clustering
Dimension Reduction-PCA
3 PROJECTS ON UNSUPERVISED LEARNING

Decision Trees
Bagging
Random Forests
Boosting
3 PROJECTS ON Ensemble Techniques

Feature Engineering
Model Selection and Tuning
Model Performance Measures
Regularising Linear Models
MI Pipeline
Bootstrap Sampling
Grid Search Cv
Randomised Search Cv
K Fold Cross-validation
A PROJECT ON Featurization, Model Selection & Tuning

RL Framework
Component of RL Framework
Examples of RL Systems
Types of RL Systems
Q-learning

Introduction To DBMS
ER Diagram
Schema Design
Key Constraints and Basics of Normalization.
Joins
Subqueries Involving Joins and Aggregations
Sorting
Independent Subqueries
Correlated Subqueries
Analytic Functions.
Set Operations
Grouping and Filtering
2 PROJECTS ON DBMS

-Timeline of NLP and Generative Al

Frameworks for Understanding ChatGPT
and Generative Al
-Implications for Work, Business, and
Education
-Output Modalities and Limitations
-Business Roles to Leverage ChatGPT
-Prompt Engineering for Fine-Tuning
Outputs
-Practical Demonstration and Bonus
Section on RLHF
-Introduction to Generative Al
-Al vs ML vs DL vs GenAl
-Supervised vs Unsupervised Learning.
-Discriminative vs Generative Al
A Brief Timeline of GenAl
-Basics of Generative Models
Large Language Models
-Word Vectors
-ChatGPT: The Development Stack
-Attention Mechanism
-Business Applications of ML, DL and GenAl
-Hands-on Bing Images and ChatGPT
-2 PROJECTS ON ChatGPT

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Upcoming Batch Schedule for Machine learning Training

Sapalogy provides flexible timings to all our students. Here are the Machine learning Training Classes in Nagpur Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timing.

Course	New batch date	Offline	Online	Enquire now

Machine learning	Starts every week	Nagpur	India	Enquire now
ML & AI	Starts every week	Nagpur	India	Enquire now
Data science	Starts every week	Nagpur	India	Enquire now

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Machine learning certification

Sapalogy training certification will serve as proof that the courses were completed by Sapalogy.
The Machine learning certification offered by Sapalogy will equip you with valuable skills, enhancing your competitiveness in the job market.
Sapalogy provides comprehensive guidance for your Machine learning global certification, ensuring a 100% passing guarantee in examinations such as Machine learning Certification, Machine learning Platform Development Certification, and various other global exams.

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Frequently asked questions

Machine learning falls under the umbrella of artificial intelligence and focuses on designing algorithms and models that empower computers to learn from data and autonomously make forecasts and choices, bypassing the need for explicit instructions.

In supervised learning, the algorithm is trained on a labeled dataset with input-output pairs, while unsupervised learning deals with unlabeled data, aiming to find patterns or relationships within the data.

A neural network is a computational model inspired by the human brain, consisting of interconnected nodes (neurons) organized into layers. It’s commonly used for tasks like pattern recognition and decision-making.

Overfitting occurs when a model learns the training data too well, capturing noise or outliers and performing poorly on new, unseen data. It’s essential to strike a balance to create a model that generalizes well.

Feature engineering involves selecting, transforming, or creating relevant features (variables) from raw data to improve a machine learning model’s performance.

Regression predicts a continuous outcome, while classification predicts a categorical outcome. For example, predicting house prices is a regression task, while spam detection is a classification task.

Precision measures the accuracy of positive predictions, while recall gauges the ability of a model to capture all relevant instances. Balancing both is crucial for a comprehensive evaluation of a classifier.

Reinforcement learning involves training an agent to make sequences of decisions in an environment to maximize a cumulative reward. It’s often used in tasks where an agent learns to navigate and make decisions through trial and error.

Hyperparameters are settings external to the model that affect its learning process. Examples include learning rates and regularization parameters. Tuning these hyperparameters is crucial for optimizing a model’s performance.

Deep learning is a subset of machine learning that specifically involves neural networks with multiple layers (deep neural networks). It excels in tasks like image and speech recognition, leveraging hierarchical feature representations.