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The Complete Machine Learning & Deep Learning Bootcamp
New

The Complete Machine Learning & Deep Learning Bootcamp

Master Scikit-Learn and PyTorch through 19 real projects, then deploy models into Flutter and Django apps.
New
Created byGeneral Gichohi Kihara
Last updated 7/2026
English

What you'll learn

  • Build and evaluate machine learning models in Scikit-Learn, from linear regression to XGBoost ensembles
  • Diagnose and fix the real problems that break ML projects: overfitting, data leakage, class imbalance, and multicollinearity
  • Master PyTorch 2.x fundamentals — tensors, autograd, custom nn.Module architectures, and training loops written from scratch
  • Train convolutional neural networks for image classification, using transfer learning with ResNet-18 and EfficientNet-B0
  • Build sequence models (RNN, LSTM, GRU) for real-world time-series forecasting
  • Fine-tune Transformer models (DistilBERT) for practical NLP and social-media sentiment analysis
  • Build and train generative models — autoencoders, VAEs, GANs, and diffusion models
  • Implement reinforcement learning agents using Q-Learning, Deep Q-Networks, and PPO
  • Deploy trained models to production with FastAPI, ONNX Runtime, and TFLite
  • Integrate deployed models into real Flutter and Django applications

Course content

18 sections • 84 lectures • 17h 54m total length

  • What Is Machine Learning, and When Should You Actually Use It?18:45
  • Supervised vs. Unsupervised vs. Semi-Supervised vs. Reinforcement Learning12:46
  • Batch Learning vs. Online Learning7:01
  • Instance-Based vs. Model-Based Learning10:22
  • The Three Real Challenges: Bad Data, Overfitting & Underfitting12:32
  • Programming vs. Learning
  • Naming the Learning Type
  • Instance vs. Model-Based
  • Diagnosing the Failure

Requirements

  • Basic Python programming (variables, functions, loops) — no prior machine learning experience required
  • A free Google account to use Google Colab — all model training happens in the cloud, no GPU purchase needed
  • Basic high-school-level math is helpful, but every concept is explained from first principles
  • No prior Flutter or Django experience is needed for the deployment chapters — all integration code is provided and explained line by line

Description

Most machine learning courses teach the same four datasets everyone has already seen a hundred times — Iris, Titanic, MNIST, Boston Housing. You finish knowing the theory and freeze in an interview the moment someone hands you real, messy, imbalanced data.

This course is built differently. Every one of the 19 chapters runs on a real, public dataset: diamond pricing data, African banking-crisis history, SME financial-health surveys, satellite crop imagery, social-media sentiment analysis, and more. These are the kind of datasets you actually meet on the job — not the kind that make every course's GitHub look identical.

You will start with the foundations of machine learning and work through the full classical toolkit in Scikit-Learn — linear and logistic regression, SVMs, decision trees, ensembles and XGBoost, dimensionality reduction, and clustering. Then you move into modern deep learning with PyTorch 2.x: convolutional networks, recurrent networks and LSTMs, Transformers, generative models (VAEs, GANs, diffusion), and reinforcement learning.

By the end, you will not just have a folder of notebooks. You will know how to serve a trained model with FastAPI, convert it to ONNX and TFLite, and wire it into a working Flutter or Django application — the exact set of skills that separates a portfolio project from a homework assignment.

What makes this course different:

  • 19 real datasets, not toy datasets — sourced from Kaggle, Zindi, HuggingFace, FAO STAT, and open government data

  • Every algorithm is taught by fixing a real problem in real data: imbalanced classes, data leakage, collinearity, unstable clustering — not by fitting a clean synthetic dataset that behaves perfectly on the first try

  • A complete PyTorch 2.x deep learning track — no legacy TensorFlow 1.x code to unlearn later

  • End-to-end deployment: FastAPI, ONNX Runtime, TFLite, and direct Flutter integration

  • A downloadable Colab notebook, trained model, and cleaned dataset for every chapter

  • A short quiz after every section so you can confirm you actually absorbed the material before moving on

Whether you are a Python developer moving into machine learning, a data analyst who wants to build and ship models instead of only reporting on data, or a mobile or web developer who wants to add real AI features to your own apps, this course gives you the complete path — from your first pandas DataFrame to a working, deployed AI product.


Who this course is for:

  • Python developers who want to move into machine learning and deep learning
  • Data analysts who want to build and deploy models, not just report on data
  • Mobile and web developers who want to add real AI features to their Flutter or Django apps
  • Students and career-changers who want a portfolio built on real, defensible projects instead of toy datasets
  • Anyone who finished a theory-only ML course and now wants hands-on, dataset-driven practice

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