Welcome to the most advanced Machine Learning Course in Surat, designed especially for IT college students who want to step into the world of Artificial Intelligence (AI) and Data Science. At Karon IT Training, we provide practical, industry-ready ML training that goes beyond theory.
You’ll start with supervised & unsupervised learning, data preprocessing, feature engineering, and model evaluation, and move on to deep learning, neural networks, NLP, TensorFlow, and Scikit-learn. With real-world projects and expert mentoring, you’ll gain the confidence to work on AI-powered systems, predictive analytics, and intelligent applications.
Whether you’re a beginner eager to explore AI or an IT student preparing for future job opportunities, this course gives you the hands-on skills, certification, and career support to stand out in Surat’s booming IT market.
Machine Learning Training Schedule
10% OFF , Save ₹5,000
Machine Learning is transforming industries by enabling computers to learn from data and make intelligent decisions. Our Machine Learning with Python course in Surat teaches you how to develop smart applications, predictive models, and AI solutions using Python – the most popular language for AI development. Gain hands-on experience with real-world datasets and industry-grade projects.
Learning Machine Learning is all about practice. In our course, you’ll work on live projects like stock price prediction, recommendation systems, and image recognition. With guided coding sessions, you’ll understand how to preprocess data, train models, and evaluate results – making you job-ready.
Machine Learning experts are in huge demand in IT companies, startups, and research organizations. Completing our Python ML training in Surat will give you a competitive edge, helping you secure internships, higher-paying roles, and exciting opportunities in AI, Data Science, and software development.
Machine Learning Course Syllabus
What is Python, and why use it?
Installing Python and using an IDE (IDLE / VS Code)
Writing and running the first Python program
Writing and running the first Python program
Variables & data types
Input/Output (input(), print())
Operators (arithmetic, comparison, logical, assignment)
Type conversion & casting
If, else, elif
Nested if
Loops: for, while
Break, continue, pass
Strings & string functions
Lists, Tuples, Sets, Dictionaries
List comprehensions
Defining functions, return values
Arguments (default, keyword, variable-length)
Lambda functions
Importing and creating modules
Using built-in modules (math, random, datetime)
Opening, reading, and writing files
Working with CSV & JSON files
Exception handling (try, except, finally, raise)
Classes and objects
Constructors & destructors
Inheritance, polymorphism, encapsulation
Method overriding
Iterators and Generators
Decorators and context managers
Regular Expressions (Regex)
Virtual environments and pack
What is Machine Learning?
Types of ML: Supervised, Unsupervised, Reinforcement
ML in real life (recommendations, fraud detection, NLP, CV)
Python setup: VS Code
Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
Data collection & cleaning
Handling missing values, duplicates, and outliers
Data scaling (Normalization vs Standardization)
Feature engineering & encoding categorical variables
Train-test split, cross-validation
Regression Models: Linear, Polynomial, Ridge, Lasso
Classification Models: Logistic Regression, KNN, Decision Trees, Random Forest, SVM
Model evaluation: Accuracy, Precision, Recall
Clustering: K-Means, Hierarchical, DBSCAN
Dimensionality reduction: PCA, t-SNE
Anomaly detection
Neural Networks & Deep Learning (Intro)
Activation functions, loss functions, optimizers
Intro to TensorFlow & PyTorch
Building a simple NN for MNIST digit recognition
Ensemble methods: Bagging, Boosting (AdaBoost, XGBoost, LightGBM)
Feature selection & importance
Hyperparameter tuning (GridSearchCV, RandomizedSearchCV, Optuna)
Handling imbalanced datasets (SMOTE, class weights)
Text preprocessing: Tokenization, Stopwords, Stemming, Lemmatization
Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
Sentiment analysis using ML/DL
Image preprocessing (OpenCV, PIL)
CNN basics (Convolution, Pooling, Flattening)
Transfer learning (VGG, ResNet, MobileNet)
Saving & loading models (Pickle, Joblib)
Django API for ML models
Streamlit for quick ML apps
ML pipeline automation basics
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Offer Valid: Till Date 31st October, Limited Seats Available.
