Best Machine Learning Training Course in Vijayawada

Are you looking to get into machine learning but don’t know how? Look no more, as codegnan has come with its Machine learning institute in Vijayawada, where you get top-of-the-line learning from the best in the industry. 

Learn from ex-IITians and ex-employees of tech giants like Amazon, Google and IBM. Not only that, but you also get an authorized certification from codegnan to prove your caliber. So far we have trained over 2000 learners, so hurry up and enroll now.

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Become a Machine Learning developer

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

300 Hours Instructor
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Overview and Key Features of Machine Learning course in Vijayawada

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This Machine Learning course in Vijayawada by codegnan comes with the objective of helping IT and non-IT individuals enter the world of machine learning. It starts from the very basics so no need to have prior knowledge about this subject. 

Plus, here are some key features of the course that make it worth your time and money.

Career Scope for Machine Learning in Vijayawada

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Having Machine Learning as your skill can help you bag some of the highest salaries in India, even as a fresher. For example, the average salary of a machine learning engineer in India is around ₹8.8 LPA, that also for someone with 0-5 years of experience. 

However, depending on a lot of factors, it can range anywhere between ₹3.0 to ₹20.7 LPA. 

And if you are worried if you will be able to secure a job in this field or not, then you can search for machine learning engineering jobs in India and have a look at the long list of active jobs you can apply for.

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

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No matter if you are from a non-tech background or don’t have any knowledge of machine learning, by the end of this machine learning course by codegnan, here are some things you’ll be proficient at.

1. Fundamentals of Machine Learning

Build a strong understanding of machine learning concepts, covering supervised, unsupervised, and reinforcement learning, preparing you for diverse real-world challenges.

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2. Gain Project Execution Skills

Learn to execute end-to-end ML projects, from data engineering to model deployment, through practical, hands-on projects.

3. Proficiency in Python

Master Python and key data libraries (Pandas, NumPy), which are essential skills for data manipulation and model implementation.

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4. Data Visualization

Develop practical data visualization skills using Matplotlib, Seaborn, and Plotly, that help you to communicate insights and make informed decisions based on data analysis.

Machine Learning Course Curriculum in Vijayawada

1. Importance of Data in 21st Century
2. Types of Data and its usage
3. Python Crash course (using IDLE)

● Data Types
● Conditional Statements
● Control Statements
● Functions

4. What is Machine Learning and types of Learning
5. Anaconda Environment Setup and its usage .

1. Numpy
● Creating arrays, Difference between List and Array
● Accessing Elements, Slicing, Concatenation
● Universal Functions, Shape Manipulation
● Automatic Reshaping, Vector Stacking.
2. Pandas
● Pandas DataStructures
● Indexing, Selecting Data, Slicing functions
● Some Useful DataFrame Functions
● Handling Missing Values in DataFrame
● Time Series Analysis
3. Data Visualization Libraries
● Matplotlib
● Plotly
● Basic Plotting with Seaborn
● Projects on Data Analysis – Google Analysis, Market Analysis
4. Sklearn usage
● Playing with Scikit-learn, Understanding classes in Scikit-learn

Machine Learning Fundamentals
• What is machine learning?
• How Machine Learning works?
• Applications of machine learning
• Different types of machine learning
• How do we know machines are learning right?
• Different stages of machine learning projects.Data Transformation and Preprocessing
• Handling Numeric Features
• Feature Scaling
• Standardization and Normalization
• Handling Categorical Features
• One Hot Encoding, pandas get_dummies
• Label Encoding
• More on different encoding techniquesTrain,Test and Validation Split
• Simple Train and Test Split
• Drawbacks of train and test split
• K-fold cross validation
• Time based splittingOverfitting And Underfitting
• What is overfitting ?
• What causes overfitting?
• What is Underfitting ?
• What causes underfitting ?
• What are bias and Variance ?
• How to overcome overfitting and underfitting problems ?

Regression
• Introduction to Linear Regression
• Understanding How Linear Regression Works
• Maths behind Linear Regression
• Ordinary Least Square
• Gradient Descent
• R – square
• Adjusted R-square
• Polynomial Regression
• Multiple Regression
• Performance Measures – MSE, RMSE, MAE
• Assumption of Linear Regression
• Ridge and Lasso regression
• RFE (Recursive Feature elimination)
Hands On – Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset
Classification
Logistic regression
• Introduction to classification problems
• Introduction to logistic regression
• Why the name regression ?
• The sigmoid function
• Log odds
• Cost function
• Feature importance and model interpretability
• Collinearity of features
• Feature engineering for non-linearly separable data
Performance Metrics for Classification Algorithms
• Accuracy Score
• Confusion Matrix
• TPR, FPR, FNR, TNR
• Precision – Recall
• F1-Score
• ROC Curve and AUC
• Log LossHands On – Real World Case Study on IBM HR Employee Attrition datasetK Nearest Neighbors

• Introduction to KNN
• Effectiveness of KNN
• Distance Metrics
• Accuracy of KNN
• Effect of outlier on KNN
• Finding the k Value
• KNN on regression
• Where not to use KNN
Hands On – Different case study on KNN
Natural Language Processing
• Introduction to NLP
• Converting Text to vector
• Data Cleaning
• Preprocessing Text Data – Stop word removal, Stemming , Tokenization, Lemmatization
• Collecting Data from the web
• Developing a Classifier
• Building Pipelines for NLP projects
• Uni-grams,bi-grams and n-grams
• tf-idf
• Word2Vec
Hands On – Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation
Naive Bayes
• Refresher on conditional Probability
• Bayes Theorem
• Examples on Bayes theorem
• Exercise problems on Naive Bayes
• Naive Bayes Algorithm
• Assumptions of Naive Bayes Algorithm
• Laplace Smoothing
• Naive Bayes for Multiclass classification
• Handling numeric features using Naive Bayes
• Measuring performance of Naive Bayes
Hands On – Working on spam detection and Amazon Food Review dataset
Support Vector Machines
• Introduction to SVM
• What are hyperplanes ?
• Geometric intuition
• Maths behind svm’
• Loss Function
• Kernel trick
• Polynomial kernel, rbf and linear kernels
• SVM Regression
• Tuning the parameter
• GridSearch and RandomizedSearch
• SVM Regression
Hands On – Case Study SVM on Social network ADs and Gender recognition from voice datasetDecision Tree
• Introduction to Decision Tree
• Homogeneity and Entropy
• Gini Index
• Information Gain
• Advantages of Decision Tree
• Preventing Overfitting
• Advantages And Disadvantages
• Plotting Decision Trees
• Plotting feature importance
• Regression using Decision Trees
Hands-On – Decision Tree on US Adult income dataset
Ensemble Learning
• Introduction to Ensemble Learning
• Bagging (Bootstrap Aggregation)
• Constructing random forests
• Runtime
• Case study on Bagging
• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)
• Measuring model performance
• Boosting
• Gradient Boosting
• Adaboost and XGBoost
• Case study on boosting trees
• Hyperparameter tuning
• Evaluating performance
• Stacking Models
Hands-On – Talking Data Ad Tracking Fraud Detection case study

Clustering
• Introduction to unsupervised learning
• Applications of Unsupervised Learning
• Kmeans Geometric intuition
• Maths Behind Kmeans
• Kmeans in presence of outliers
• Kmeans random initialization problem
• Kmeans++
• Determining the right k
• Evaluation metrics for Kmeans
• Case study on Kmeans
• Hierarchical Clustering
• Agglomerative and Divisive
• Denodgrams
• Case study on hierarchical clustering
• Segmentation
• Case Study on Segmentation
• DBSCAN – Density based clustering
• MinPts and Eps
• Core Border and Noise Points
• Advantages and Limitation of DBSCAN
• Case Study on DBSCAN clustering
Hands On – Applying Unsupervised models on Retail data and mall customer datasetDimensionality Reduction Techniques
• What are dimensions?
• Why is high dimensionality a problem ?
• Introduction to MNIST dataset with (784 Dimensions)
• Into to Dimensionality reduction techniques
• PCA (Principal Component Analysis) for dimensionality reduction
• t-sne (t-distributed stochastic neighbor embeddingHands-on: Applying Dimensionality Reduction on MNIST data

• Introduction
• Markov Decision Process
• Expected Return
• Policy and Value Function
• Q-Learning
• Exploration vs Exploitation
• OpenAI Gym and python for Q-learning
• Training Q-Learning Agent
• Watching Q-Learning Play GamesHands On – Working with OpenAI Gym and Q-Learning

Tools You Will Learn with Our Machine Learning Course in Vijayawada

What tools you master in your course is very important in assessing your skills. So, to give you a brief idea, here are some of the tools you’ll learn to use during this machine learning course in Vijayawada by codegnan.

Python : It is a universal programming language for ML which helps you in data manipulation, analysis, and model implementation.

MySQL : This database management tool is vital for handling large datasets, improving data retrieval efficiency, and integrating with machine learning projects.

XGBoost : This is a boosting algorithm for ML which enhances model performance, crucial for accurate predictions, and widely applied in competitions and real-world scenarios.

Keras : A High-level neural networks API that simplifies deep learning model creation, making it accessible for beginners and enabling rapid prototyping.

Pandas : It is a Python library for data manipulation that streamlines data cleaning, preprocessing, and analysis, enhancing efficiency in handling structured data.

Tableau : This data visualization tool enables the creation of interactive, insightful visuals from ML outputs, aiding in the effective communication of findings.

Matplotlib : This is a Python library for 2D plotting. It is essential for visualizing data distributions, trends, and patterns, facilitating data interpretation and storytelling.

Tensorflow : An open-source ML library that supports building and deploying ML models, especially deep learning. It is widely adopted in the industry for its flexibility.

Seaborn : This statistical data visualization library enhances Matplotlib, simplifying the creation of complex visualizations and improving overall data exploration.

NumPy : A numerical computing library for Python that optimizes mathematical operations, critical for efficient handling and manipulation of numerical data in ML.

Become a Machine Learning developer

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

Machine Learning Projects (3 Projects) you will work on

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Each section of the curriculum is taught with hands-on implementations, but apart from that, you get to work on multiple projects that help you assess your overall learnings. Here are some project topics under the course and their learning objectives.

1. Real - Time Rain Prediction

Predict real-time rain using live weather data, covering the end-to-end ML workflow and deployment.

Skills used: Data Collection, Model Training, Model Evaluation, Application Deployment

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2. Netflix Recommendation System

Create a recommendation system for Netflix, focusing on feature engineering and personalized recommendations.

Skills used: Dataset Exploration, Feature Engineering, Building Recommendation Models.

3. GDP/ House/ Stock Price Prediction

Predict GDP/ house/ stock prices by integrating web scraping, data preprocessing, and model deployment.

Skills used: Web Scraping, Data Preprocessing, Model Training, Model Deployment, Data Visualization, Feature Engineering.

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Who is This Machine Learning Course For?

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Wondering if this course is a right fit for you or not? Then, let us share our ideal candidate base and how this course can help them. 

1. College Students/Fresh Graduates

The course provides a comprehensive understanding, enabling you to tackle real-world problems making you an attractive candidate for data analyst, machine learning engineer, or data scientist positions. So it is great for you to get a competitive edge over your peers as you enter the job market. 

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2. Beginner Developers/Engineers

The course equips you with the essential skills to contribute to ML projects, making you a valuable asset in software development teams and opening doors to specialized ML roles.

3. IT Professionals

This course will help professionals acquire proficiency in Python, data engineering, and model deployment, enabling them to leverage ML for data analysis and decision-making in IT projects. The course teaches them ML techniques to improve system efficiency and problem-solving.

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4. Anyone Interested in Machine Learning

This course is structured and covers the entire ML workflow. From data preprocessing to model deployment, you'll gain practical skills and a deep understanding of algorithms. Making it equally ideal for self-learners and enthusiasts looking to explore or transition into the exciting field of machine learning.

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Machine Learning Course Certification in Vijayawada

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This one-month (30 days) long machine learning certification course in Vijayawada by codegnan aims to teach you the fundamentals of ML in a systematic manner. This course covers great application-based projects that you can showcase in your resume, but that isn’t enough to show your merit, right?

This is why codegnan also gives an authorized certification upon successful compilation of the course. So you can hone your skills with proof in hand.

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Meet Your Machine Learning Course Trainers

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Saketh Kallepu

Saketh Kallepu is a Data science professional in the IT field with a background in Computational Intelligence. Having 7+ years of experience in this field, he is an outstanding Data Science Mentor and trainer.


Saketh Kallepu believes, “Don’t work hard, just work with heart.” With this belief, he joined as Co-Founder, mentor, and trainer at Codegnan to change the education platform and build a new technical arena for passionate learners.

Codegnan Learners success

1250+ Companies Have Hired Codegnan Learners

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Machine Learning Course Training Fees in Vijayawada— Get 20% off

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The fee of the machine learning course in Vijayawada by codegnan is only ₹10,000, for both online and in-classroom modes, which is nothing compared to how much you are expected to pay for a high-value course like machine learning. 

Besides, you also get lifetime free access and updates on the learning materials and job placement assistance. Which increases the value of the course manyfold. 

However, if the price still seems out of your budget, you can avail of a limited-period offer of a 20% discount and get the course for the effective price of only ₹8,000.

Our other machine learning training location(s)

codegnan's other training courses in Vijayawada

Machine Learning Course in Vijayawada FAQs

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There are no such criteria for enrolling in codegnan’s machine learning course. Anyone with school-level graduation and upwards is eligible for these courses. However, for a course like machine learning, knowing the basics of coding and programming languages like Python can certainly help you grasp the concepts better.

The fee for codegnan’s machine learning course is ₹10,000 for both online and offline modes. However, with an additional discount, you can get it for as low as ₹8,000 as well.

Upon completing the course, you will get an authorized certificate from codegnan. This certification is acknowledged in tech companies across the globe, especially by the ones that are in association with codegnan.

The duration of the machine learning course in Vijayawada by codegnan is 30 days or 1 month. Hence, whether you are a fresh graduate who wants to maximize your final semester or someone who wants to transition into tech jobs, this brief course can help you achieve that. 

No, there are no such prerequisites for this Machine Learning course in Vijayawada by codegnan. The minimum educational qualification for enrolling in this course is school graduation with any stream.  But there are some sections that require haveing a basic idea of Python, but again, it is not a must.

Yes, since it revolves around the fundamentals of machine learning and gives you hands-on training, individuals without a non-technical background can also pursue it.

Here are some job opportunities you can bag after completing this course, even if you are from a non-technical background- data analyst, machine learning engineer, NLP engineer, data engineer, web developer with ML integration, reinforcement learning developer, data scientist, recommendation system developer, and AI consultant.

You don’t have to be proficient in Python to enroll in this machine learning course. But knowing the basics of it is enough for you to start. 

Yes, you can learn machine learning in 6 months or less. codegnan’s machine learning course is only 1 month long, including the time of working on the projects.

Yes, codegnan offers both online and classroom training for their machine learning course in Vijayawada. So whether you are a local resident, you have the option of choosing between the two. 

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