AI & ML Training in Nagpur | Get 100% Placement Assurance

Best AI & ML Training

Best AI & ML Training by Sapalogy training offers you the best AI & ML training in nagpur for freshers and experience candidate in nagpur, with expert guidance and 100% guaranteed placement assistance. Our AI & ML 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 AI & ML in just 7 months.

All the topics will be covered in detail and also include.

Resume preparation
Interview practice
6 month internship
with 100 % job opportunities guaranteed program.

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What is AI & ML?

AI (Artificial Intelligence) is a branch of pc technological know-how focused on growing machines capable of sensible conduct.
ML (Machine Learning) is a subset of AI that involves education algorithms to study styles from statistics and make predictions or decisions.
ML algorithms enhance overall performance over the years via learning from revel in and adapting to new facts.
Supervised mastering entails schooling ML models on classified information, while unsupervised studying discovers styles in unlabeled records.
AI and ML packages span various industries, inclusive of healthcare, finance, and robotics, riding innovation and automation.

AI is the overarching area aiming to create wise machines, device getting to know is a subset the usage of algorithms to enable systems to study from records, and deep gaining knowledge of is a selected ML approach employing neural networks with multiple layers for elaborate sample reputation.

AI encompasses diverse branches including machine learning, herbal language processing, pc imaginative and prescient, and robotics, using advancements in various domain names.

Sapalogy taining provides AI & ML training in offline and online mode. Starting with real time AI & ML project based training.
IT background, non IT background, freshers, experience can start their career in AI & ML irrespective of their background.
Sapalogy is the best training institute in nagpur with the 100% job opportunities.

What is AI & ML | Job Opportunities in 2024 |

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Roadmap to learn AI & ML with Sapalogy

1. Introduction

What is AI & ML?
AI & ML course
AI & ML certification
AI & ML jobs in india
AI & ML jobs in nagpur

2. Foundational Skills

Math: Linear algebra, calculus, probability & statistics are crucial for understanding algorithms and analyzing data.
Programming: Python is the go-to language. Learn about datastructures, algorithms, and object-oriented programming. Explore libraries like NumPy, Pandas, Matplotlib.
Databases: Get familiar with SQL for querying and manipulating relational databases.

3. Data Science Core

Machine Learning: Supervised (regression, classification) and unsupervised (clustering, dimensionality reduction) learning algorithms.
Statistics & Hypothesis testing: Understand statistical testing, p-values, and confidence intervals.
Data Wrangling & Cleaning: Learn data manipulation techniques with Pandas and data cleaning practices.
Data Visualization: Effectively communicate insights using libraries like Matplotlib, Seaborn, Tableau.

4. Deepening the Knowledge

Advanced ML Algorithms: Ensemble methods, boosting, time series analysis, deep learning (neural networks, CNNs).
Domain Knowledge: Choose a specific domain (finance, healthcare, etc.) to gain industry-specific knowledge and problem-solving skills.
Big Data Technologies: Explore Apache Spark, Hadoop for handling large datasets.
Cloud Computing: Familiarize yourself with cloud platforms like AWS, Azure, Google Cloud Platform for data storage and computing.

5.Specialization & Projects

Specialization: Pick a niche area (e.g., NLP, computer vision, recommender systems) and dive deeper.
Portfolio Building: Work on real-world projects to showcase your skills and understanding. Contribute to open-source projects.
Communication & Collaboration: Hone your communication skills to explain complex technical concepts effectively. Learn to collaborate effectively in teams.

6. Feedback Loop and Iteration

Establish a feedback loop for continuous improvement based on user feedback and evolving business needs.
Iteratively update models and algorithms to adapt to changing conditions.

7. Cost Analysis and ROI Measurement

Conduct cost analysis for AI and ML implementation, including infrastructure, training, and maintenance.
Measure the return on investment (ROI) based on improved efficiency, cost savings, and other business metrics.

8. Resume preparation

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Key features of AI & ML Training

180+ Hours course duration

100% Job oriented training

Industry expert faculties

Free demo class available

Completed 100+ batches

Certification guidance

AI & ML 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

Introduction to Perceptron & Neural Networks
Activation and Loss functions
Gradient Descent
Batch Normalization
2
TensorFlow & Keras for Neural Networks
Hyper Parameter Tuning

Introduction to Convolutional Neural Networks
Introduction to Images
Convolution, Pooling, Padding & its Mechanisms
Forward Propagation & Backpropagation for CNNs
CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
Transfer Learning
Object Detection
YOLO, R-CNN, SSD
Semantic Segmentation
U-Net
Face Recognition using Siamese Networks
Instance Segmentation
3 PROJECTS ON CV

Introduction to NLP
Stop Words.
Tokenization
Stemming and Lemmatization
Bag of Words Model
Word Vectorizer
TF-IDF
POS Tagging
Named Entity Recognition
Introduction to Sequential data
RNNs and its Mechanisms
Vanishing & Exploding gradients in RNNs
LSTMs – Long short-term memory
GRUs – Gated Recurrent Unit
LSTMs Applications
Time Series Analysis
LSTMs with Attention Mechanism
Neural Machine Translation
Advanced Language Models:
Transformers, BERT, XLNet
3 PROJECTS ON NLP

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

Introduction to GANS
Generative Networks
Adversarial Networks
How do GANs work?
DCGANS – Deep Convolution GANs
Applications of GANS

● EDA
● Time Series Forecasting
● Pre Work for Deep Learning
● Model Deployment
● Visualization using Tensor board
● GANS (Generative Adversarial Networks)
● Reinforcement Learning
● Recommendation Systems

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

Mathematical Fundamentals for Generative AI
VAES: First Generative Neural Networks
GANS: Photorealistic Image Generation
Conditional GANs and Stable Diffusion
Transformer Models: Generative Al for
Natural Language
Chat GPT: Conversational Generative Al
Hands-On Chat GPT Prototype Creation
Next Steps for Further Learning and
Understanding
3 PROJECTS ON Chat GPT and Prompt Engineering (1 Week)

Understanding profit center and its use
Profit center hierarchy & master data
Profit center derivation & document splitting

5+ projects on sap
20+ topic wise tests
Resume building
Interview preparation
6 month internship with cin no.
Job opportunities
One on one classroom interview practice

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Skills covered by Sapalogy

Upcoming batch schedule for AI & ML Training

Sapalogy provides flexible timings to all our students. Here is the AI & ML Training Class 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 timings.

Course	Batch time	Offline	Online	Enquire now

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

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AI & ML CERTIFICATION

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

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Frequently asked question

AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, decision-making, and learning.

ML is a subset of AI that focuses on developing algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed.

ML systems learn patterns from data by using algorithms that adjust parameters to minimize errors. Common techniques include supervised learning (using labeled data) and unsupervised learning (finding patterns in unlabeled data).

AI is a broader concept, while ML is a subset of AI. AI encompasses any technique that allows computers to mimic human intelligence, whereas ML specifically involves learning from data.

Deep Learning is a subset of ML that uses neural networks with multiple layers (deep neural networks) to model and solve complex problems, achieving high-level abstractions in data representation.

Some popular ML algorithms include Linear Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Networks.

In supervised learning, the algorithm is trained on labeled data, while unsupervised learning involves finding patterns in unlabeled data without predefined outputs.

NLP focuses on enabling computers to understand, interpret, and generate human language. Applications include language translation, sentiment analysis, and chatbots.

Reinforcement Learning involves training models to make sequences of decisions by receiving feedback in the form of rewards or penalties, commonly used in gaming, robotics, and autonomous systems.

Bias in AI refers to the unfair and disproportionate impact of algorithms on certain groups. It can occur due to biased training data, algorithm design, or unintended consequences during implementation.

Ethical concerns in AI include issues related to bias, transparency, accountability, job displacement, and privacy. Ensuring ethical AI development and use is crucial for responsible implementation.

Emerging trends include Explainable AI, Federated Learning, AI-driven automation, AI in healthcare, and the continued advancement of reinforcement learning and natural language processing.