Machine Learning Scientist in Python

About Course

Your Journey to Machine Learning Mastery: From Beginner to Kaggle Competitor

This is not just a course; it is a complete career roadmap. We have structured your path into four distinct phases, guiding you from basic Python scripting to handling Big Data and winning global competitions.

Phase 1: The Foundation – Data Analysis & Engineering

Before we build intelligence, we must master the data. You will start by mastering the industry-standard stack: NumPy and Pandas. But we go deeper than basics—you will learn advanced Data Preprocessing and Feature Engineering techniques. You will learn how to clean messy data, deal with text inputs, and select the most impactful features for your models.

Phase 2: The Modeler – Core Algorithms & Optimization

Build models that are accurate, fast, and efficient. Master the core of Machine Learning with Supervised (Regression, Classification) and Unsupervised (Clustering) algorithms. You won’t just build default models; you will learn Hyperparameter Tuning (Grid & Random Search) to find the perfect settings and use Dimensionality Reduction to optimize performance on complex datasets.

Phase 3: The Specialist – NLP, Time Series & Big Data

Expand your toolkit to handle any data type. This is where you separate yourself from the average data scientist. You will specialize in three critical areas:

Natural Language Processing (NLP): Build “Fake News” classifiers and process text using spaCy and Regular Expressions.
Time Series: Master forecasting techniques to predict future trends based on historical data.
Big Data with PySpark: Learn to handle massive datasets that don’t fit in memory using Apache Spark.

Phase 4: The Competitor – Real-World Strategy

Test your skills against the best. In the final leg, we take you into the competitive arena. The Kaggle Competition module teaches you the specific workflow, feature engineering tricks, and modeling strategies used by competition winners to top the leaderboards.

Master the Stack: distinct proficiency in Python’s data science library ecosystem: NumPy, Pandas, Scikit-Learn, and Seaborn.
Data Preprocessing: Learn how to clean dirty data, handle missing values, and scale features to prepare them for AI models.
Supervised Learning: Build models that predict the future, including Linear Regression, Logistic Regression, Decision Trees, KNN, and SVM.
Unsupervised Learning: Discover hidden patterns in data using K-Means, Hierarchical Clustering, and Association Rule Mining (Apriori).
Model Evaluation: accurately assess your models using Confusion Matrices, Cross-Validation, and the Bias-Variance tradeoff to prevent overfitting.
Real-World Application: Complete 5+ hands-on projects, including Car Price Prediction, Fruit Classification, and E-commerce Basket Analysis.

Course Content

Introduction

Course Overview
Introduction to Artificial Intelligence
Introduction to Machine Learning
Setting up the Work Environment

Introduction to NumPy Library

Introduction to Scikit-learn Library

Regressions

Classification

Classification using Logistic Regression

Classification using Decision Trees

Classification using K-Nearest Neighbors (KNN)

Support Vector Machine (SVM) Classifier

Models Evaluation

Overfitting and Underfitting
Example of Overfitting
Cross-Validation of Classification Models
Selecting the Best Learning Model
Clustering
Clustering using K-Means
Customer Segmentation using Partitioning Clustering
Introduction to Hierarchical Clustering
Practical Application of Hierarchical Clustering
Customer Segmentation using Hierarchical Clustering

Association

Project: Predictive Modeling for Agriculture
A farmer reached out to you as a machine learning expert seeking help to select the best crop for his field. Due to budget constraints, the farmer explained that he could only afford to measure one out of the four essential soil measures: Nitrogen content ratio in the soil Phosphorous content ratio in the soil Potassium content ratio in the soil pH value of the soil

Dimensionality Reduction in Python
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Preprocessing for Machine Learning in Python

Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.

Feature Engineering for Machine Learning in Python
Create new features to improve the performance of your Machine Learning models.

Hyperparameter Tuning in Python
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Introduction to Natural Language Processing in Python
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Natural Language Processing with spaCy
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

Feature Engineering for NLP in Python
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Introduction to PySpark
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!

Machine Learning with PySpark
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Winning a Kaggle Competition in Python
Learn how to approach and win competitions on Kaggle.

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About Course

Your Journey to Machine Learning Mastery: From Beginner to Kaggle Competitor

Phase 1: The Foundation – Data Analysis & Engineering

Phase 2: The Modeler – Core Algorithms & Optimization

Phase 3: The Specialist – NLP, Time Series & Big Data

Phase 4: The Competitor – Real-World Strategy

What Will You Learn?

Course Content

Introduction

Course Overview

Introduction to Artificial Intelligence

Introduction to Machine Learning

Setting up the Work Environment

Introduction to NumPy Library

Introduction to NumPy and Installation

Creating Arrays using NumPy

Array Attributes and Arithmetic Operations

Operations on Arrays using NumPy

Introduction to Scikit-learn Library

Introduction to Scikit-learn and Installation

Understanding Data Splitting

Understanding Data Encoding and Scaling

Regressions

Introduction to Regression

Linear Regression

Mathematical Foundations of Linear Regression

Sales Price Prediction using a Single Variable (Parts 1 & 2)

Sales Price Prediction using Multiple Variables (Parts 1 & 2)

Nonlinear Regression

Car Price Prediction (Parts 1 & 2)

Classification

Introduction to Classification

Model Evaluation

Evaluating Multi-class Classifiers

Classification using Logistic Regression

Learning Classification Rules using Logistic Regression

Predicting Customers Who Can Buy a Phone using Linear Regression

Predicting Car Driving Mode

Classification using Naive Bayes Classifiers

Applying Bayes’ Theorem with Machine Learning

Predicting Customers Who Can Buy a Phone using Naive Bayes

Classification using Decision Trees

Learning Classification Rules using Decision Trees

Predicting Customers Who Can Buy a Phone using Decision Trees

Approving a Bank Loan Request or Not

Classification using K-Nearest Neighbors (KNN)

Learning Classification Rules using K-Nearest Neighbors

Predicting Customers Who Can Buy a Phone using KNN

Finding Movie Recommendations

Support Vector Machine (SVM) Classifier

Learning Classification Rules using Support Vector Machine (SVM)

Fruit Classification Project

Predicting Customers Who Can Buy a Phone using SVM

Models Evaluation

Overfitting and Underfitting

Example of Overfitting

Cross-Validation of Classification Models

Selecting the Best Learning Model

Clustering

Clustering using K-Means

Customer Segmentation using Partitioning Clustering

Introduction to Hierarchical Clustering

Practical Application of Hierarchical Clustering

Customer Segmentation using Hierarchical Clustering

Association

Introduction to Association

Basic Concepts in Association Rule Mining

Finding Association Rules using Apriori Algorithm

Practical Application of Apriori Algorithm

Finding Association Rules in E-commerce Sales

Dimensionality Reduction in Python Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Exploring High Dimensional Data

Feature Selection I – Selecting for Feature Information

Feature Selection II – Selecting for Model Accuracy

Feature Extraction