AlgoAcademy

AI (Machine Learning & Deep Learning) Program

Unlock the full power of data with our Comprehensive Machine Learning & Deep Learning Course — a project-based, hands-on learning journey built for those who want to move from foundational understanding to expert-level implementation. Designed with a clear progression from beginner to advanced topics, this course focuses on real-world applications, industry-standard tools, and end-to-end project development, ensuring you're job-ready and technically sound.

Who Should Enroll?

Aspiring Data Scientists and Machine Learning Engineers.
Software Developers aiming to integrate ML into applications.
Analysts & Engineers seeking to automate insights and workflows.
Students or Professionals looking for a structured, practical entry into AI.
Anyone ready to learn hands-on, code-first, and outcome-driven ML.

What You'll Learn

This curriculum is divided into strategic learning modules that progress from machine learning fundamentals to advanced deep learning systems. Topics include:

Supervised Learning: Regression & Classification models.
Unsupervised Learning: Clustering & Dimensionality Reduction.
Model Evaluation, Tuning & Selection techniques.
Feature Engineering, Anomaly Detection, and Interpretability.
Deployment: Turning ML models into usable applications.
Deep Learning: Neural networks, CNNs, RNNs, LSTM with TensorFlow/Keras.

Learning Outcomes

By the end of this course, you will be able to:

Design, implement, and evaluate ML models using Python and scikit-learn.
Build and deploy deep learning systems with TensorFlow/Keras.
Apply advanced techniques like cross-validation, ensemble methods, and explainability tools.
Preprocess, visualize, and transform real-world datasets for optimal ML performance.
Create, fine-tune, and deploy full-stack ML applications via REST APIs or dashboards.
Understand and apply CNNs for image data and RNNs for sequences like text or time series.

Real-World Projects Include

Hands-on learning is at the heart of this course. You'll unview projects such as:

House Price Prediction using linear and polynomial regression
Titanic Survival Classifier with multiple classification models
Customer Segmentation using clustering algorithms
Credit Card Fraud Detection with XGBoost
Text Sentiment Analysis with LSTM networks
Fashion Image Classification using CNNs
Capstone Project: Choose from domains like healthcare, e-commerce, or NLP

Each project mirrors real industry use cases and helps solidify skills through practical coding, data wrangling, and result-driven model tuning.

Course Overview

At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio.

Aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto. Sam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.

Requirements

At vero eos et accusamus et iusto odio dignissimos ducimus
At vero eos et accusamus et iusto odio dignissimos ducimus
At vero eos et accusamus et iusto odio dignissimos ducimus
At vero eos et accusamus et iusto odio dignissimos ducimus
At vero eos et accusamus et iusto odio dignissimos ducimus

Certification

What you'll learn

At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus
At vero eos et accusamus

Course Curriculum

Module 1: Regression (Supervised Learning)

Simple & Multiple Linear Regression
Polynomial Regression
Regularization: Ridge, Lasso, ElasticNet
Model evaluation: MAE, MSE, RMSE, R²
Residual plots & assumptions of linear models
scikit-learn, statsmodels
Seaborn, Matplotlib for diagnostics
House Price Prediction (e.g., Boston Housing)
Student Score Predictor based on study hours
Energy Consumption Prediction using weather data

Module 2: Classification (Supervised Learning)

Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Naive Bayes
Support Vector Machines (SVM)
Model evaluation:
Accuracy, Precision, Recall, F1
ROC Curve & AUC
Confusion Matrix
scikit-learn, Seaborn
sklearn.metrics
Titanic Survival Prediction
Email Spam Classifier
Bank Loan Approval Prediction
Heart Disease Detection

Module 3: Clustering (Unsupervised Learning)

K-Means Clustering
Elbow Method, Silhouette Score
DBSCAN (density-based clustering)
Hierarchical Clustering
PCA for dimensionality reduction (optional visualization)
scikit-learn, scipy.cluster, yellowbrick
Customer Segmentation
Market Basket Clustering (group customers by purchase behavior)
Document or News Article Clustering

Module 4: Model Selection & Evaluation

Train/Test Split
K-Fold Cross-validation
Stratified K-Fold
sklearn.model_selection
scikit-learn plotting utilities
Revisit previous classification/regression models with cross-validation
Compare 3 classifiers on a dataset using learning curves

Module 5: Hyperparameter Tuning

Grid Search
Random Search
Nested CV
Cross-validation with hyperparameter tuning
GridSearchCV, RandomizedSearchCV (sklearn.model_selection)
Optuna (advanced, optional)
Tune Random Forest on Titanic Dataset
XGBoost Tuning for Loan Default Prediction
Lasso & Ridge Regression tuning on housing dataset

Module 6: Ensemble Methods

Bagging: Random Forest
Boosting:
AdaBoost
Gradient Boosting Machines (GBM)
XGBoost & LightGBM
Stacking (optional advanced)
sklearn.ensemble, xgboost, lightgbm
Credit Card Fraud Detection with XGBoost
Voting Classifier combining 3 algorithms
Stacked Regressor on Boston Housing dataset

Module 7: Feature Engineering & Selection

One-Hot vs Label Encoding
Scaling: Standard, Min-Max, Robust
Creating new features
Feature importance (model-based & correlation)
Dimensionality reduction: PCA
sklearn.preprocessing, Pandas
SHAP, LIME for interpretability
Retail Sales Dataset: create time-based features
Feature Importance Visualizer using SHAP
Flight Price Prediction with extensive feature creation

Module 8: Anomaly Detection

Isolation Forest
One-Class SVM
Local Outlier Factor
Z-Score & IQR methods (statistical)
sklearn.ensemble, pyod
Network Intrusion Detection
Outlier Detection in sales or manufacturing data

Module 9: Explainability & Interpretability

Feature Importance (Random Forest, XGBoost)
SHAP (global & local interpretation)
LIME (local interpretation)
Partial Dependence Plots (PDPs)
shap, lime, sklearn.inspection
Explain Churn Model predictions with SHAP
Visual Dashboard for model interpretability

Module 10: Deployment (Optional but Recommended)

Export model with joblib or pickle
Build REST API with Flask or FastAPI
Simple dashboards with Streamlit
Flask / FastAPI
Streamlit
Docker (optional)
Deploy Your Spam Detector as a Flask API
Interactive Churn Predictor using Streamlit

Module 11: Final Capstone Project Ideas (Machine Learning)

Problem framing & EDA
Data preprocessing
Feature engineering
Model comparison and tuning
Evaluation & visualization
Optional: Explainability & deployment
Loan Default Prediction System
E-commerce Product Recommendation Engine
Healthcare Risk Scoring System
Job Resume Shortlisting Tool (w/ NLP features)
Marketing Campaign Success Predictor

Module 12: What is Deep Learning?

AI vs ML vs Deep Learning
Real-world DL applications: Healthcare, Finance, Entertainment
Introduction to neural networks (intuitive)
Neurons, layers, weights, activation functions (no math focus)
Tools setup: Google Colab, Keras GUI walkthrough

Module 13: Building Your First Neural Network

Using no-code/low-code environments (Keras/TensorFlow playground)
Input layer → Hidden layers → Output layer
Train, test, and evaluate a simple DL model
Hands-on project: Handwritten digit classification (MNIST)

Module 14: Understanding Deep Neural Networks

Feedforward vs Backpropagation (with visuals)
Epochs, batches, learning rate (intuitively explained)
Activation functions: ReLU, Sigmoid, Softmax
Model tuning: Overfitting, underfitting, loss/accuracy

Module 15: Hands-on Neural Networks

Train deeper networks with templates
Visualizing model performance
Intro to confusion matrix & accuracy curves
Project: Image classification with extended MNIST (Fashion MNIST)

Module 16: Convolutional Neural Networks (CNNs)

Why we need CNNs for images
Filters, convolutions, pooling (animated visual explanation)
Flattening, dense layers, dropout

Module 17: CNN Project

Build a CNN using guided templates
Application: Real-time object recognition or digit classification
Explore pretrained models like MobileNet (plug & play)

Module 18: Sequence Models

What are sequence data and use cases (text, time-series)
RNN basics and limitations
LSTM & GRU introduction (visuals only)

Module 19: LSTM Hands-on

Use templates to build a text sentiment model
Text preprocessing tools (drag-drop or template code)
Visualize how the model interprets text

Module 20: Capstone Projects & Deployment

Review of learned concepts
Project planning (pick a use case: image, text, or time-series)
Work on a guided capstone with mentor help

Module 21: Capstone Completion and Demo

Finish, polish, and deploy with Streamlit or shareable Colab
Project presentation & feedback session
Google Colab (preconfigured notebooks)
TensorFlow/Keras (via GUI and templates)
Teachable Machine (optional, for fast prototyping)
Streamlit (deployment - guided)
Interactive quizzes and visual concept checks
Template-based projects every 2 weeks
Final capstone project demo
Certificate upon completion

Jonathan Campbell

72 Videos
102 Lectures
Exp. 4 Year

At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi.

4.2 out of 5.0

5 Star 85%

4 Star 75%

3 Star 53%

1 Star 20%

Item Reviews - 3

Josaph Manrty27 Oct 2019

" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "

12 1 07

Rita Chawla2 Nov May 2019

" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "

12 1 07

Adam Wilsom10 Nov 2019

" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "

12 1 07

Submit Reviews

COURSE BENEFITS

PLACEMENT ASSISTANCE

*MIN 5 COMPANY WALK-INS

GitHub portfolio and Job-ready resume to enhance their career prospects.

Course Features

Resume & Linkedin Building
1-1 Mock Interviews
100% Hands-on
Certification

Enroll Now

Tools Covered

Throughout the course, you'll gain hands-on experience with:

scikit-learn

pandas

seaborn

matplotlib

statsmodels

SHAP

LIME

Yellowbrick

GridSearchCV

RandomizedSearchCV

Optuna

TensorFlow

Keras

Flask

FastAPI

Streamlit

Google Colab

Docker

Explore Top Categories

Unlock your potential with our live interactive classes taught by industry experts. Get your doubts clarified instantly and ensure you understand every concept thoroughly.