Master core ML concepts
Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!
Lo que serás capaz de hacer
Read to jumpstart the world of Machine Learning & Artificial intelligence? This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions" Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques. This comprehensive course is taught by Prateek Narang & Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years . We are providing you this course to you at a fraction of its original cost ! This is action oriented course , we not just delve into theory but focus on the practical aspects by building 8+ projects . With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science. Some of the topics that you will learn in this course. Logistic Regression Linear Regression Principal Component Analysis Naive Bayes Decision Trees Bagging and Boosting K-NN K-Means Neural Networks Some of the concepts that you will learn in this course. Convex Optimisation Overfitting vs Underfitting Bias Variance Tradeoff Performance Metrics Data Pre-processing Feature Engineering Working with numeric data, images & textual data Parametric vs Non-Parametric Techniques Sign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!
- Jumpstart the world of AI & ML
- Maths of Machine Learning
- Regression & Classification Techniques
- Linear & Logistic Regression
- K-Nearest Neighbours, K-Means
- Naive Bayes, Text Classification
Temario
UNIDAD 1 - Introduction · 11 leccionesUNIDAD 1 - Introduction
UNIDAD 1 - Introduction
- Course Overview
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Computer Vision
- Natural Language Processing
- Automatic Speech Recognition
- Reinforcement Learning
- Pre-requisites
- Code Repository
- Quiz Time!
UNIDAD 2 - Supervised vs Unsupervised Learning · 4 leccionesUNIDAD 2 - Supervised vs Unsupervised Learning
UNIDAD 2 - Supervised vs Unsupervised Learning
- Supervised Learning Introduction
- Supervised Learning Example
- Unsupervised Learning
- Quiz Time!
UNIDAD 3 - Linear Regression · 18 leccionesUNIDAD 3 - Linear Regression
UNIDAD 3 - Linear Regression
- Introduction to Linear Regression
- Notation
- Hypothesis
- Loss / Error Function
- Training Idea
- Gradient Descent Optimisation
- Gradient Descent Code
- Gradient Descent - for Linear Regression
- The Math of Training
- Code 01 - Data Generation
- Code 02 - Data Normalisation
- Code 03 - Train Test Split
- Code 04 - Modelling
- Code 05 - Predictions
- R2 Score
- Code 06 - Evaluation
- Code 07 - Visualisation
- Code 08 - Trajectory [Optional]
UNIDAD 4 - Linear Regression - Multiple Features · 12 leccionesUNIDAD 4 - Linear Regression - Multiple Features
UNIDAD 4 - Linear Regression - Multiple Features
- Introduction
- Hypothesis
- Loss Function
- Training & Gradient Updates
- Code 01 - Data Prep
- Code 02 - Hypothesis
- Code 03 - Loss Function
- Code 04 - Gradient Computation
- Code 05 - Training Loop
- A Note about Shapes
- Code 06 - Evaluation
- Linear Regression using Sk-Learn
UNIDAD 5 - Logistic Regression · 15 leccionesUNIDAD 5 - Logistic Regression
UNIDAD 5 - Logistic Regression
- Binary Classification Introduction
- Notation
- Hypothesis Function
- Binary Cross-Entropy / Loss Function
- Gradient Update Rule
- Code 01 - Data Prep
- Code 02 - Hypothesis / Logit Model
- Code 03 - Binary Cross Entropy Loss
- Code 04 - Gradient Computation
- Code 05 - Training Loop
- Code 06 - Visualise Decision Boundary
- Code 07 - Predictions & Accuracy
- Logistic Regression using Sk-Learn
- Multiclass Classification : One Vs Rest
- Multiclass Classification : One Vs One
UNIDAD 6 - Dimensionality Reduction/ Feature Selection · 6 leccionesUNIDAD 6 - Dimensionality Reduction/ Feature Selection
UNIDAD 6 - Dimensionality Reduction/ Feature Selection
- Curse of Dimensionality
- Feature Selection Vs. Feature Extraction
- Filter Method
- Wrapper Method
- Embedded Method
- Feature Selection - Code
UNIDAD 7 - Principal Component Analysis (PCA) · 9 leccionesUNIDAD 7 - Principal Component Analysis (PCA)
UNIDAD 7 - Principal Component Analysis (PCA)
- Introduction to PCA
- Conceptual Overview of PCA
- Maximising Variance
- Minimising Distances
- Eigen Values & Eigen Vectors
- PCA Summary
- Understanding Eigen Values
- PCA Code
- Choosing the right dimensions
UNIDAD 8 - K-Nearest Neigbours · 9 leccionesUNIDAD 8 - K-Nearest Neigbours
UNIDAD 8 - K-Nearest Neigbours
- Introduction
- KNN Idea
- KNN Data Prep
- KNN Algorithm Code
- Euclidean and Manhattan Distance
- Deciding value of K
- KNN and Data Standardisation
- KNN Pros and Cons
- KNN using Sk-Learn
UNIDAD 9 - PROJECT - Face Recognition · 9 leccionesUNIDAD 9 - PROJECT - Face Recognition
UNIDAD 9 - PROJECT - Face Recognition
- OpenCV - Working with Images
- OpenCV - Video Input from WebCam
- Object Detection using Haarcascades
- Face Detection in Images
- Face Detection in Live Video
- Face Recognition Project Intro
- Face Recognition 01 - Data Collection
- Face Recognition 02 - Loading Data
- Face Recognition 03 - Predictions using KNN
UNIDAD 10 - K-Means · 6 leccionesUNIDAD 10 - K-Means
UNIDAD 10 - K-Means
- K-Means Algorithm
- Code 01 - Data Prep
- Code 02 - Init Centers
- Code 03 - Assigning Points
- Code 04 - Updating Centroids
- Code 05 - Visualizing K-Means & Results
UNIDAD 11 - Project - Dominant Color Extraction · 5 leccionesUNIDAD 11 - Project - Dominant Color Extraction
UNIDAD 11 - Project - Dominant Color Extraction
- Introduction
- Reading Images
- Finding Clusters
- Dominant Color Swatches
- Image in K-Colors
UNIDAD 12 - Naive Bayes Algorithm · 12 leccionesUNIDAD 12 - Naive Bayes Algorithm
UNIDAD 12 - Naive Bayes Algorithm
- Bayes Theorem
- Derivation of Bayes Theorem
- Bayes Theorem Question
- Naive Bayes Algorithm
- Naive Bayes for Text Classification
- Computing Likelihood
- Understanding Golf Dataset
- CODE - Prior Probability
- CODE - Conditional Probability
- CODE - Likelihood
- CODE - Prediction
- Implementing Naive Bayes - Sklearn
UNIDAD 13 - Multinomial Naive Bayes · 8 leccionesUNIDAD 13 - Multinomial Naive Bayes
UNIDAD 13 - Multinomial Naive Bayes
- Multinomial Naive Bayes
- Laplace Smoothing
- Multinomial Naive Bayes | Example
- Bernoulli Naive Bayes
- Bernoulli Naive Bayes | Example
- Bias Variance Tradeoff
- Gaussian Naive Bayes
- CODE - Variants of Naive Bayes
UNIDAD 14 - PROJECT : Spam Classifier · 6 leccionesUNIDAD 14 - PROJECT : Spam Classifier
UNIDAD 14 - PROJECT : Spam Classifier
- Project Overview
- Data Clearning
- WordCloud
- Text Featurization
- Model Building
- Model Evaluation
UNIDAD 15 - Decision Trees · 9 leccionesUNIDAD 15 - Decision Trees
UNIDAD 15 - Decision Trees
- Decision Trees Introduction
- Decision Trees Example
- Entropy
- CODE : Entropy
- Information Gain
- CODE : Split Data
- CODE : Information Gain
- Construction of Decision Trees
- Stopping Conditions
UNIDAD 16 - Decision Trees Implementation · 11 leccionesUNIDAD 16 - Decision Trees Implementation
UNIDAD 16 - Decision Trees Implementation
- CODE - Decision Tree Node
- CODE - Train Decision Tree
- CODE - Assign Target Variable to Each Node
- CODE - Stopping Conditions
- CODE - Train Child Nodes
- CODE - Explore Decision Tree Model
- CODE - Prediction
- Handling Numeric Features
- Bias Variance Tradeoff
- Decision Trees for Regression
- Decision Tree Code - Sklearn
UNIDAD 17 - PROJECT : Titanic Survival Prediction · 7 leccionesUNIDAD 17 - PROJECT : Titanic Survival Prediction
UNIDAD 17 - PROJECT : Titanic Survival Prediction
- Project Overview
- Exploratory Data Analysis
- Exploratory Data Analysis - II
- Data Preparation for ML Model
- Handling Missing Values
- Decision Tree Model Building
- Visualize Decision Tree
UNIDAD 18 - Ensemble Learning : Bagging · 6 leccionesUNIDAD 18 - Ensemble Learning : Bagging
UNIDAD 18 - Ensemble Learning : Bagging
- Ensemble Learning
- Bagging Model
- Why Bagging Helps
- Random Forest Algorithm
- Bias Variance Tradeoff
- CODE : Random Forest
UNIDAD 19 - Ensemble Learning : Boosting · 9 leccionesUNIDAD 19 - Ensemble Learning : Boosting
UNIDAD 19 - Ensemble Learning : Boosting
- Boosting Introduction
- Boosting Intuition
- Boosting : Mathematical Formulation
- Concept of Pseudo Residuals
- GBDT Algorithm
- Bias Variance Tradeoff
- CODE - Gradient Boosting Decision Trees
- XGBoost
- Adaptive Boosting (AdaBoost)
UNIDAD 20 - PROJECT : Customer Churn Prediction · 7 leccionesUNIDAD 20 - PROJECT : Customer Churn Prediction
UNIDAD 20 - PROJECT : Customer Churn Prediction
- Project Overview
- Exploratory Data Analysis
- Data Visualisation
- Finding relations
- Data Preparation
- Model Building
- Hyperparameter tuning
UNIDAD 21 - Deep Learning Introduction - Neural Network · 11 leccionesUNIDAD 21 - Deep Learning Introduction - Neural Network
UNIDAD 21 - Deep Learning Introduction - Neural Network
- Biological Neural Network
- A Neuron
- How does a perceptron Learns?
- Gradient Descent Updates
- Neural Networks
- 3 Layer NN
- Why Neural Nets?
- Tensorflow Playground
- CODE -Data Preparation
- CODE - Model Building
- CODE - Model Training and Testing
UNIDAD 22 - PROJECT : Pokemon / Image Classification · 10 leccionesUNIDAD 22 - PROJECT : Pokemon / Image Classification
UNIDAD 22 - PROJECT : Pokemon / Image Classification
- Introduction
- The Data
- Structured Data
- Data Loading
- Data Preprocessing
- Model Architecture
- Softmax Function
- Model Training
- Model evaluation
- Predictions
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
22 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
2 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Mohit Uniyal.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 22 unidades · 200 lecciones · 60 h
- 12 meses de acceso
- 2 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Mohit Uniyal; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
