MACHINE LEARNING COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is a leading training institute that caters to individuals at all skill levels, from beginners to intermediate and expert learners, looking to master machine learning. Our comprehensive courses are designed to provide a seamless learning experience, starting with the fundamentals for beginners, and progressing to advanced topics for experts.

Our expert instructors deliver hands-on, practical training that ensures you gain real-world skills and knowledge. We offer a structured curriculum with in-depth modules, making it easy for beginners to grasp the basics and for experts to fine-tune their expertise. Join LearnAI.co.in today to embark on a machine learning journey tailored to your level of experience and unlock the potential of this transformative technology.

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What is machine learning?

Machine learning is a technology that allows computers to learn from data and make decisions without being explicitly programmed. It's like teaching a computer to recognize patterns and make predictions, making it a powerful tool for automating tasks and improving decision-making processes.

Why to learn machine learning?

Learning machine learning can turbocharge your career growth as it's in high demand across industries. With expertise in this field, you'll have exciting opportunities to work on cutting-edge projects, tackle complex problems, and command a competitive edge in the job market.

Who can Pursue machine learning?

  • Any Degree pursuing or Graduated,
  • Bachelor’s, Master’s, PhD, and
  • Anyone interested in course
Enroll to Course Now

COURSE CURRICULUM

Python

  • Introduction
  • Installation
  • Fundamental of Python
  • Variables
  • Comments
  • Print Statement
  • Operators
  • Mutable Data Types
  • Data Types
  • Special Data Types
  • Conditions
  • Loops
  • Functions
  • Break, Continue and Pass Statements
  • String Object and working
  • List
  • Tuple
  • Set
  • Dictionaries
  • Map
  • Reduce
  • Filter
  • Classes
  • Objects
  • Inheritance
  • Multiple Inheritance
  • Modules
  • Error Handling

Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  • Cuflinks
  • Bokesh

Libraries

  • Numpy
  • Pandas
  • Random
  • Math
  • Scipy
  • sklearn
  • Keras
  • Tensorflow
  • OpenCV
  • NLTK
  • Spacy
  • Lot more…

Tableau

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basics charts and dashboards
  • Special Char Types
  • Dashboard design and principles
  • Connections with servers
  • Local file access
  • Hands on experience with worksheet

Web Scraping

  • Url
  • Beautiful Soup

Data base

  • SQL
  • MongoDB

Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Probability Distribution
  • Normal Distribution
  • Poison Distribution
  • Outlier Detection
  • Other Statistical Fundamentals

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi Supervised Learning
  • Reinforcement Learning

Supervised Learning

  1. Regression Models
  2. Classification Models

Regression Models

  • Introduction to Regression Models
  • Linear Models
  • Linear Regression
  • Multiple Regression
  • Ordinary Least Square Method
  • Non-Linear Models
  • Support Vector Regressor
  • Random Forest Regressor
  • Decision Tree

Evaluation Metrics for Regression Models

  • R-Square
  • Adjusted R Square
  • Mean Square Error
  • Root Mean Square Error
  • Mean Absolute Error

Classification Models

  • Introduction to Classification Models
  • Logistics Regression
  • Naïve Bayes
  • Support Vector Classifier
  • K-NN Classifier
  • Decision Tree Classifier
  • Random Forest Classifier

Evaluation Metrics for Classification Models

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC Curve

Unsupervised Learning

  • Introduction to Unsupervised Learning

Clustering Models

  • Introduction to Clustering Models
  • K- Mean Clustering
  • Hierarchical Clustering
  • High Dimensional Clustering

Dimension Reduction

  • Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

  • Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

  • Feature Extraction
  • Model Defects & Evaluation Metrics
  • Model Selection and Tuning
  • Comparison of machine learning models

Neural Networks

  • Introduction to Deep Learning
  • Fundamental of Neural Networks
  • TensorFlow and Keras
  • Artificial Neural Networks (ANN)
  • Convolution Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Evaluation of Deep Learning
  • Neural Networks Basics
  • Gradient Descent
  • Introduction to Perceptron & Neural Networks
  • Batch Normalization
  • Activation and Loss Functions
  • Hyper parameter tuning
  • SoftMax
  • Deep Neural Networks
  • Weights initialization

Image Pre-processing

  • Computer Vision
  • Open CV
  • Noise Detection
  • Noise Reduce
  • Low Pass Filters
  • Forward Propagation
  • Backward Propagation
  • Pooling & Padding

Natural Language Processing

  • Introduction to NLP
  • Corpus
  • Natural Language Understanding (NLU)
  • Natural Language Generator (NLG)
  • Tokenization (Word, Sentence, Blank, RegEx)
  • Frequency Distribution
  • Filtering
  • Stemming
  • Lemmatization
  • Stop Words
  • Regular Expressions
  • POS Tagging
  • Syntax Tree
  • Chunking
  • Lemmas
  • Hypernyms
  • Hyponyms
  • Synonyms
  • Antonyms
  • Distance Between words
  • Wordnet
  • Name Entity Recognition
  • Bag of words or Document Matrix
  • Count Vectorization
  • Term Frequency
  • Inverse Document Frequency
  • Sentimental Analysis

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • Artificial Intelligence (AI) Researcher
  • Data Analyst
  • Business Intelligence Analyst
  • Deep Learning Engineer
  • Computer Vision Engineer
  • Natural Language Processing (NLP) Engineer
  • Predictive Analytics Specialist
  • Research Scientist (Machine Learning)
  • AI Product Manager
  • Robotics Engineer
  • Machine Learning Consultant
  • Quantitative Analyst (Quant)
  • Image Recognition Engineer

Components of Machine Learning 

  • Data
  • Algorithms
  • Model Development
  • Feature Engineering
  • Training Data
  • Validation Data
  • Evaluation Metrics
  • Hardware and Software
  • Data Preprocessing
  • Model Deployment
  • Monitoring and Maintenance
  • Data Storage and Management
  • Security and Privacy
  • Collaboration Tools
  • Version Control
  • Interpretability and Explainability
  • Regulatory Compliance
  • Documentation

What We Offer

24/7 Portal Access
Domain Expertise Trainers
Industrial Standard Course Structure
Job Oriented Programs
Recording Sessions
Assignments on Real time Scenarios
Job Support
Resume Preparations
Internships
Job Assistance
Working on Real Time Projects
Course Completion Certifications

We Provide Higher Quality Services

AND YOU’LL GET SOLUTIONS FOR EVERYTHING
Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.

Branches

KPHBSR NagarSecunderabad
Phone: +91 9390023585
Email: info@learnai.co.in

Head Office

Address: 1st Floor, Rajadhani Theatre Complex, Pillar Number 1546, above Siri Mobiles, Dilsukhnagar, Hyderabad, Telangana 500060.
Phone: +91 9390023585
Email: info@learnai.co.in
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