Machine Learning

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What you will learn

Tech Immersion

Begin a structured deep dive into the foundations and advanced concepts of Machine Learning.
From Python programming, NumPy, Pandas, and data visualization to statistics, probability, supervised and unsupervised algorithms, and real-world model development—this program equips you with the complete skill set required to excel in modern data-driven environments.
You will progress into deployment, MLOps fundamentals, Dockerization, APIs, SQL, and cloud readiness, ensuring you develop end-to-end ML competency.

Live Project Deployment

Apply your knowledge through hands-on, production-oriented projects that mirror real industry challenges.
You will build, evaluate, optimize, and deploy machine learning models using professional workflows.
This includes packaging models, creating APIs, leveraging Docker, integrating CI/CD fundamentals, and preparing your project for cloud deployment.
Your final capstone project will demonstrate your ability to handle complete ML pipelines from data ingestion to deployment.

Resume Engineering

Transform your resume into a powerful representation of your technical expertise.
You will learn to articulate ML projects, highlight quantitative achievements, and present your skills in a manner aligned with hiring trends in data science and machine learning roles.
The result is a polished, industry-ready resume that stands out to recruiters and technical reviewers alike.

LinkedIn Optimization

Strengthen your professional presence with a strategically optimized LinkedIn profile.
You will learn how to position yourself as an ML practitioner, present your portfolio, engage professionally, and build meaningful industry connections.
This module ensures your digital presence communicates credibility and technical authority.

Community Membership

Become part of the Codigo Aldea learning ecosystem, where collaboration and continuous improvement drive success.
Engage in discussions, exchange insights, access exclusive ML resources, and learn from mentors and peers.
Your journey extends beyond the classroom, connecting you to a network that supports your long-term growth as a machine learning professional.

Skills you will gain

Scikit-learn Python Pandas Numpy MLOps foundations Matplotlib Jupiter Notebook Git & GitHub

Roadmap to Advance Machine Learning

This Specialization builds on the success of the Machine Learning for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Machine Learning. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

Month-1 Course Info

• Python Basics - Syntax, Data Types, Control Flow
• Functions, Modules, Packages
• Object-Oriented Programming (OOP)
• NumPy - Arrays, Operations, Broadcasting
• Pandas - DataFrames, Cleaning, Preprocessing
• Matplotlib and Seaborn - Data Visualization
• Jupyter Notebook - Workflow and Documentation
• Git and GitHub Introduction
• Communication and Professional Skills

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Month-2 Course Info

• Statistics for Machine Learning - Mean, Median, Variance, Correlation
• Probability Basics - Distributions, Bayes Theorem
• Linear Algebra Foundations - Vectors, Matrices, Dot Product
• Exploratory Data Analysis (EDA)
• Feature Engineering - Encoding, Scaling, Transformation
• Train/Test Split and Cross Validation
• Resume Building
• Mock Interviews - Communication Skills

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Month-3 Course Info

• Supervised Learning Algorithms - Linear Regression, Logistic Regression
• Decision Trees and Random Forests
• Naive Bayes, KNN, SVM
• Evaluation Metrics - Accuracy, Precision, Recall, F1, AUC
• Dimensionality Reduction - PCA
• Hyperparameter Tuning - GridSearchCV, RandomSearch
• LinkedIn Profile Optimization
• Technical + Communication Mock Interviews

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Month-4 Course Info

• Unsupervised Learning - Clustering (KMeans, Hierarchical, DBSCAN)
• Association Rule Mining - Apriori
• Time Series Forecasting - ARIMA, Decomposition
• Ensemble Methods - Bagging, Boosting, Gradient Boosting, XGBoost
• Introduction to Deep Learning Concepts
• Model Pipelines with scikit-learn
• Presentation and Public Speaking Skills
• Body Language and Confidence Building

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Month-5 Course Info

• Model Deployment - Flask/FastAPI Basics
• ML Model Packaging with Docker
• Introduction to MLOps - CI/CD for ML
• Model Monitoring and Logging
• Working with APIs
• SQL Basics for Data Work
• Freelancing Guidance - Platforms, Niches, Proposal Writing
• Negotiation Skills for ML Professionals
• Financial Education - Budgeting, Investments, Taxes

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Month-6 Course Info

• End-to-End ML Project - Data Cleaning to Deployment
• Building ML Systems on Cloud (AWS Basics)
• Versioning Data and Models
• Scalable ML Architecture Concepts
• Security, Ethics, and Best Practices in ML
• Sales and Entrepreneurship Training
• Building a Personal Brand in Data and ML
• Capstone Project - Presentation and Defense
• Final Mock Interviews - Comprehensive Rounds

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Instructor

DHAWAL DESHPANDE

Why people choose Codigo Aldea for their career