6 Week Machine Learning Summer Training

Batch starting on 20th June

Dive into the exciting world of Machine Learning with our 6-week summer training program designed for students, fresh graduates, and working professionals eager to upskill. This structured, project-driven course introduces you to the key concepts of machine learning and guides you through the most widely used tools and algorithms in the industry today. Whether you're aiming to enhance your resume or kickstart a career in data science, this program offers the foundation you need.

 

Course Structure

• Machine Learning Introduction to ML
• What is ML? Why ML?

• Introduction to Supervised

• ML Introduction to Unsupervised

• ML Difference between AI|DL|ML

• Application and Use.

 

Tools Required For Development: Anacondа, Jupyter Nb/ Google Colab/ Spyder

 

• ML libraries
• Numpy: Introduction to Numpy
• pandas:Introduction | DataFrame | Loading
• datasets | Loading data from database | pandas
• Operation.
• Matplotlib: Introduction | Line Chart | Pie Chart|
• Scatter Plot | Bar chart | Histogram.
• Sklearn: : Introduction | Sklearn-API|
• Statsmodels.api.
• ML GlossaryVariable types, k-fold CV, AUC,
• F1 score, Overfitting/Underfitting,
• Generalization,ROC| Confusion matrix
• Mathematical Background for ML- Matrix ops
• Probability Theory (Bayes' Theorem)
• Statistical knowledge for ML- Mean, Median,
• Mode, Z-scores, bias-variance dichotomy
• Exploratory Data Analysis using Visualization
• Scikit-learn Library for ML
• Code Exercises

 

Steps of Machine Learning

 

• Data Collection: The quantity & quality of your data dictate how accurate our model is....
• Data Preparation: Wrangle data and prepare it for training | Data wrangling using Pandas | Preprocessing data and feature engineering| Data split. Choose a Model.
• Train the Model. ...
• Evaluate the Model....
• Parameter Tuning| hyper parameter training Make Predictions.

 

Supervised Learning

 

Introduction | Maths behind Supervised Machine Learning and Algo.

 

Regression

 

• Linear Regression
• Multi-Linear Regression
• Lasso/Rigde
• Decision Tree Regressor
• Support Vector Regressor

 

Classification

 

• Logistic Regression
• KNN- K Nearest Neighbors
• Support Vector Classifier(SVM-SVC)
• Decision Tree Classifier(DTC)
• Random Forest
• Naïve Bayes
• Ensemble Learning

 

Unsupervised Machine Learning Clustering

 

• Introduction: Mathematics behind Clustering

 

K-Means Clustering

 

• Implementation of K-mean Clustering

 

H-Clustering

 

• Implementation of H-clustering
• Code Exercises

 

Association Rule

 

• Apiori Rule

 

By the end of the program, you’ll not only understand how machine learning works- you’ll be able to build, evaluate, and deploy your own models. The training concludes with a capstone project to showcase your skills and a certificate of completion to enhance your portfolio. Whether you're preparing for further study, industry roles, or freelancing opportunities, this summer training gives you a strong and practical head start in the world of AI and ML. Enrol now and avail 100% placement assistance services from CodeSquadz.