Machine Learning Course Training in Hyderabad

Give your career the ultimate boost with Codegnan’s elaborate Machine Learning course in Hyderabad. We are a premium machine learning training institute in Hyderabad with a proven track record of student success. 

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Become a Machine Learning developer

Talk to our expert Machine Learning mentors and learn how our training programs in Hyderabad can help you become a Machine Learning developer and get a high-paying job.

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Overview and Key Features of Machine Learning Course in Hyderabad


Our 60 hours machine learning certification offers students a comprehensive knowledge of machine learning algorithms and techniques by developing their analytical abilities and statistical thinking with real-time case studies. By the end of the course, they will be able to develop the practical skills for building, evaluating, and deploying ML models in different corporate settings.

Career Scope for Machine Learning in Hyderabad


Machine learning has emerged as one of the highest paying professions in the global technology sector. With a number of prominent tech giants established in Hyderabad, the city generates lucrative job opportunities in domains of AI, ML and data analytics every year.

1. Booming Software Industry

Hyderabad is a home to the largest campuses of renowned software companies including Google, Microsoft, Facebook and Apple. Apart from this, the city has seen a surge in technology startups in the last few years, making it a great choice for people looking to build or transition their career to machine learning. 


2. Wide Range of Industry Applications

Versatility is the beauty of machine learning. As the field aids directly to the growth of an organization, companies from a variety of sectors including healthcare, agriculture, finance, and e-commerce are using it. So even if you don’t have a prior background in IT, with just a little training, you can become an expert ML professional in your area of expertise.


3. High Job Availability

In recent years, machine learning has surfaced as the most promising profession with an average growth rate of more than 340% on a year-on-year basis. Getting advanced training in ML will open doors to a variety of jobs including AI/ML engineer, ML architect, NLP engineer, ML data scientist and AI/ML developer.


4. Demand for Machine Learning Engineers

The demand for machine learning engineers in Hyderabad has seen a massive growth. The city alone had witnessed more than 3,500 job openings in the AI, ML and deep learning fields in the last three months, making it one of the hottest jobs of the decade. If you want to learn machine learning in Hyderabad, now is the right time.


5. Salary in Hyderabad for Machine Learning

The average salary of a Machine Learning Engineer in Hyderabad is estimated at ₹ 9.1 Lakhs per annum, with the yearly payout ranging from ₹ 3.0 Lakhs to ₹ 18.0 Lakhs. The monthly salary of a ML engineer on the other hand ranges between ₹ 45k to ₹ 47k.

Machine Learning course curriculum in Hyderabad

  • Introduction to Machine Learning and its types (supervised, unsupervised, reinforcement learning)
  • Setting up the development environment (Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn)
  • Overview of the Machine Learning workflow and common data preprocessing techniques
  • Definition of data science and its role in various industries.
  • Explanation of the data science lifecycle and its key stages.
  • Overview of the different types of data: structured, unstructured, and semi-structured.
  • Discussion of the importance of data collection, data quality, and data preprocessing.
  • Introduction to Pandas, a Python library for data manipulation and analysis.
  • Overview of NumPy, a fundamental package for scientific computing with Python.
  • Explanation of key data structures in Pandas: Series and Data Frame.
  • Hands-on exploration of data using Pandas to summarize, filter, and transform data.
  • Data cleaning techniques, handling missing values, and dealing with outliers.
  • Statistical analysis of data using NumPy functions.
  • Introduction to data visualization and its importance in data analysis.
  • Overview of Matplotlib, a popular plotting library in Python.
  • Exploring different types of plots: line plots, scatter plots, bar plots, histogram, etc.
  • Customizing plots with labels, titles, colors, and styles.
  • Introduction to Seaborn, a Python data visualization library based on Matplotlib.
  • Advanced plotting techniques with Seaborn: heatmaps, pair plots, and categorical plots.
  • Introduction to Plotly, an interactive plotting library for creating web-based visualizations.
  • Creating interactive and dynamic visualizations with Plotly


Hands-on: Instagram Reach Analysis

  • Introduction to Data Engineering: Data cleaning, transformation, and integration
  • Data cleaning and Handling missing values: Imputation, deletion, and outlier treatment
  • Feature Engineering techniques: Creating new features, handling date and time variables, and encoding categorical variables
  • Data Scaling and Normalization: Standardization, min-max scaling, etc.
  • Dealing with categorical variables: One-hot encoding, label encoding, etc.
  • Introduction to web scraping: Tools, libraries, and ethical considerations
  • Scraping data from websites using libraries like BeautifulSoup and requests: HTML
    parsing, locating elements, and extracting data
  • Handling different types of data on websites: Tables, forms, etc.
  • Storing scraped data in appropriate formats: CSV, JSON, or databases

Hands-on: Working on Scraping Data from Static / Dynamic Website

  • Introduction to Regression: Definition, types, and use cases
  • Linear Regression: Theory, cost function, gradient descent, and assumptions
  • Polynomial Regression: Adding polynomial terms, degree selection, and overfitting
  • Lasso and Ridge Regression: Regularization techniques for controlling model complexity
  • Evaluation metrics for regression models: Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE)

Hands-On – Video Game Sales Prediction

  • Introduction to Classification: Definition, types, and use cases
  • Logistic Regression: Theory, logistic function, binary and multiclass classification
  • Decision Trees: Construction, splitting criteria, pruning, and visualization
  • Random Forests: Ensemble learning, bagging, and feature importance
  • Evaluation metrics for classification models: Accuracy, Precision, Recall, F1-score, and ROC curves
  • Implementation of classification models using scikit-learn library

Hands-On – Heart Disease Detection & Food Order Prediction

  • Support Vector Machines (SVM): Study SVM theory, different kernel functions (linear, polynomial, radial basis function), and the margin concept. Implement SVM classification and regression, and evaluate the models.
  • K-Nearest Neighbors (KNN): Understand the KNN algorithm, distance metrics, and the concept of K in KNN. Implement KNN classification and regression, and evaluate the models.
  • Naive Bayes: Learn about the Naive Bayes algorithm, conditional probability, and Bayes’ theorem. Implement Naive Bayes classification, and evaluate the model’s performance.

Hands-On – Contact Tracing & Sarcasm Detection

  • AdaBoost: Boosting technique, weak learners, and iterative weight adjustment
  • Gradient Boosting (XGBoost): Gradient boosting algorithm, Regularization, and hyperparameter tuning
  • Evaluation and fine-tuning of ensemble models: Cross-validation, grid search, and model selection
  • Handling imbalanced datasets: Techniques for dealing with class imbalance, such as oversampling and undersampling


Hands-On – Medical Insurance Price Prediction

  • Introduction to Clustering: Definition, types, and use cases
  • K-means clustering: Algorithm steps, initialization methods, and elbow method for determining the number of clusters
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Core
    points, density reachability, and epsilon-neighborhoods
  • Evaluation of clustering algorithms: Silhouette score, cohesion, and separation metrics

Hands-On – Credit Card Clustering

  • Introduction to Dimensionality Reduction: Curse of dimensionality, feature
    extraction, and feature selection
  • Principal Component Analysis (PCA): Eigenvectors, eigenvalues, variance explained, and dimensionality reduction
  • Implementation of PCA using scikit-learn library

Hands-On – MNIST Data

  • Cross-validation and model evaluation techniques
  • Hyperparameter tuning using GridSearchCV and RandomizedSearchCV
  • Model selection and comparison
  • Introduction to NLP: Understand the basics of NLP, its applications, and challenges.
  • Text Preprocessing: Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing.
  • Text Representation: Explore techniques such as Bag-of-Words (BOW), TF-IDF, and
    word embeddings (e.g., Word2Vec, Glove) for representing text data.
  • Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance.

Hands-On – Real Time Sentiment Analysis

  • Introduction to Recommendation Systems: Understand the concept of
    recommendation systems, different types (collaborative filtering, content-based, hybrid), and evaluation metrics.
  • Collaborative Filtering: Explore collaborative filtering techniques, including
    user-based and item-based approaches, and implement a collaborative filtering model.
  • Content-Based Filtering: Study content-based filtering methods, such as TF-IDF and cosine similarity, and build a content-based recommendation system.
  • Deployment and Future Directions: Discuss the deployment of recommendation systems and explore advanced topics in NLP and recommendation systems.


Hands-On – News Recommendation System

  • Introduction to Reinforcement Learning: Agent, environment, state, action, and reward
  • Markov Decision Processes (MDP): Markov property, transition probabilities, and value functions
  • Q-Learning algorithm: Exploration vs. exploitation, Q-table, and learning rate
  • Hands-on reinforcement learning projects and exercises

Hands-On – Working with OpenAl Gym

  • Introduction to Flask / Stream lit web framework
  • Creating a Flask/Streamlit application for ML model deployment
  • Integrating data preprocessing and ML model
  • Designing a user-friendly web interface
  • Building a web application for Machine Learning models: Creating forms, handling user input, and displaying results
  • Deployment using AWS (Amazon Web Services): Setting up an AWS instance, configuring security groups, and deploying the application
  • Deployment using PythonAnywhere: Uploading Flask application files, configuring WSGI, and launching the application

  • Work on a real-world Machine Learning project: Identify a problem, gather data,
    and define project scope
  • Apply the learned concepts and algorithms: Data collection, preprocessing, model building, and evaluation
  • Deployment of the project on AWS or PythonAnywhere: Showcase the developed application and share the project with others
  • Presentation and discussion of the project: Demonstrate the project, explain design decisions, and receive feedback

Projects & Case Study

  • Install necessary libraries
  • Obtain an API key
  • Fetch live weather data
  • Preprocess the data
  • Train a machine learning model
  • Evaluate the model
  • Integrate the model with Flask
  • Display the results
  • Test and debug
  • Deploy the application
  • Continuously update the weather data
  • Gather the dataset
  • Explore and preprocess the dataset
  • Define the problem
  • Feature engineering
  • Build the recommendation model
  • Train the model
  • Evaluate the model
  • Generate recommendations
  • Deploy the recommendation system
  • Continuously update the dataset and retrain the model
  • Identify a reliable source for house price data
  • Understand the website structure
  • Perform web scraping
  • Preprocess the scraped data
  • Explore and preprocess additional data sources (if applicable)
  • Define the problem
  • Split the data
  • Train the model
  • Evaluate the model
  • Fine-tune the model (optional)
  • Deploy the model
  • Continuously update the dataset and retrain the model
  • Import necessary libraries
  • Collect stock price data
  • Preprocess the data
  • Explore and visualise the data
  • Feature engineering
  • Split the data
  • Choose an ML algorithm
  • Train the model
  • Evaluate the model
  • Predict stock prices
  • Analyze the results
  • Iterate and improve
  • Import necessary libraries
  • Collect GDP data
  • Preprocess the data
  • Explore and visualize the data
  • Feature engineering
  • Split the data
  • Choose an ML algorithm
  • Train the model
  • Evaluate the model
  • Predict GDP
  • Analyze the results
  • Iterate and improve
  • By the end of this course, you will have a strong understanding of machine learning concepts, algorithms, and techniques. You will be able to build, evaluate, and deploy machine learning models for various tasks. Additionally, you will have the skills to perform data engineering, data preprocessing, and web scraping. You will also be proficient in deploying your ML models using AWS and PythonAnywhere, and building user-friendly web interfaces with Flask.

Tools You Will Learn with Our Machine Learning Course in Hyderabad

Through our well devised course, you will be able to kickstart your machine learning and AI career with contemporary tools and technologies including Python, Numpy, Pandas, Matplotlib, Jupyter, Seaborn, Anaconda, Flask and scikit-learn. You will also get a chance to solve exciting challenges and showcase your certification on our HackerRank platforms.

Become a Machine Learning developer

Talk to our expert Machine Learning mentors and learn how our training programs in Hyderabad can help you become a Machine Learning developer and get a high-paying job.

Machine Learning Projects You Will Work On


At Codegnan, we allow students to engage in industry projects to help them get a taste of what real world problems actually look like. Our goal is to help you make the best use of your potential. Here are the three machine learning projects you will work on:

1. Real Time Rain Prediction

Students will learn how to fetch and preprocess live data, install necessary libraries, obtain an API key, and successfully train and deploy a machine learning model. They will be equipped with vital skills in collection, data cleaning, model building, evaluation, and many more.


2. Stock Price Prediction

This hands-on project helps students work on stock price data assimilation and prediction. The core competencies included are exploring and visualizing data, feature engineering, ML algorithm selection, data splitting and analysis.


3. House Price Prediction

Get the best out of machine learning and data analytics with the real time project on predicting price of houses from a reliable source. The goal of the project is to teach students the complexities of web scraping, data scraping, model fine-tuning, and updating and retraining the model among others.


Who is This Machine Learning Course For?


Codegnan’s machine learning course in Hyderabad is for all those tech savvy people who want to become a part of the global machine learning army and bring about cutting edge AI developments. We ensure that you get enlightened with machine learning and Python problem solving in the most efficient ways. Our course is perfect for:  

1. College students/ fresh graduates

The curriculum is easy-to-understand, making it suitable for college students and fresh graduates who don’t hold much experience in technical areas like machine learning, data analysis and AI.


2. Beginner developers/ engineers

The classes are held in a highly interactive environment to help beginners clarify their doubts and queries by connecting with experienced students and industry experts all over the world.


3. IT professionals

Professionals in the information technology and software industries can upskill themselves with the latest skills and abilities in the machine learning landscape by working on actual time case studies and projects. 


4. Practically anyone interested in machine learning

You don’t necessarily need to have a degree in computer science, IT, statistics or any related area. Our course is structured to suit the needs of all programming and math enthusiasts. All you need to bring is a curiosity to learn.


What You Will Learn with Our Machine Learning Training Classes in Hyderabad


Machine Learning Course Certification in Hyderabad


Codegnan offers students an opportunity to receive globally recognized certification upon completion of the course. Our job assists have helped students get hired by topmost organizations including SAP, Amazon, EY, Teksan, Cognizant, TCS, Wipro, Temenos and so many more. 

We not only prepare our students to be subject experts. We train them to be top professionals.


Meet Your Machine Learning Course Trainers

Manohar Chary Vadla

Manohar Chary Vadla

Manohar Chary Vadla is a Data Scientist and Mentor with a Bachelor of Commerce in Computer Science background, having 2+ years of hands-on experience in Data Extraction from documents using Python and deep learning techniques.

His areas of expertise include research and implementation in machine learning and deep learning, such as regression, classification, neural networks, and natural language processing (NLP).

As part of the AI-for India Event, in collaboration with GUVI Geek Networks and IITM Research Park, he developed a facial recognition application using the Python programming language.

Codegnan Learners success

1250+ Companies Have Hired Codegnan Learners


Machine Learning Course Training Fees in Hyderabad— Get Highest ROI


We, at Codenan, ensure that our students get premium quality learning at a budget that suits their pockets. Our carefully designed 1-month training course is priced at a cost-effective rate of ₹ 10,000. However, codegnan is currently offering the course only for ₹7,999 for a limited time. Not only will you be able to gain a myriad of lifetime skills, but you will also be well prepared to bag some of the most high-paying positions in the machine learning industry.

Our other machine learning training location(s)

codegnan's other training courses in Hyderabad

Phone Number

+91 98887 48888


Kothwal Madhava Reddy Plaza, Beside Indian Oil Petrol Bunk, JNTUH Metro Station, Nizampet X Roads, Hyderabad - 500072

Machine Learning Course in Hyderabad FAQs


There are no criteria for enrolling in the course. You can be a school or college student, a fresher or a professional, this one size fits all type of certification program is suited for all.

Codegnan offers 60 hours of learning which includes placement assistance with more than 50 hours of instructor-led training at only ₹ 10,000. Currently, get our machine learning training program only for ₹7,999.

You will receive an industry recognized machine learning course completion certificate by Codegnan.

This machine learning course in Hyderabad has a duration of 1 month, with the timeline being the same for both online and offline modes.  

There are only two prerequisites for the course – a knack for AI, and a desire to transform your career. Apart from that, nothing is required from a candidate’s end.

Yes, even people from non-technical backgrounds including management, arts, or any other non-computer related field can enroll in the course. The curriculum is designed to be easily understood by candidates of any academic and professional expertise.

After completing Codegnan’s machine learning course in Hyderabad, one can build a career in AI, ML, data science or similar fields. AI/ML engineer, ML architect, NLP engineer, ML data scientist and AI/ML developer are some of the most notable professions our students have been hired in. 

Python is not necessarily needed for machine learning. However, it is hands down the most popular programming language as far as machine learning is concerned. Python is consistent and simple, that’s why most of the companies use it. 

Yes, you can learn machine learning in 6 months. In this duration, you will easily grasp basic and intermediate level ML tools and techniques which you can later apply to your own projects.

Codegnan offers its machine learning course both online and offline. Enrolled students have an opportunity to engage in live classes from top industry professionals, and complete their projects in a real classroom.

We will assist you in case of any queries, even after the completion of your Java online training. You are always welcome to reach through our customer care number or email us your query. We would love to assist you.

You will get 24*7 support and lifetime access to the LMS, where course material like presentations, installation guides & class recordings are available. Email support will always be there to clear your doubts.


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