DATA-SCIENCE-AND-MACHINE-LEARNING Course Overview

Welcome to AI Coach Mart's comprehensive Data Science and Machine Learning course! Our program is designed to provide you with a deep understanding of DATA-SCIENCE-AND-MACHINE-LEARNING to equip you with the skills needed to excel in DATA-SCIENCE-AND-MACHINE-LEARNING.

Highlights

1. Real-Time Experts
2. LIVE Project
3. Certification
4. Online training at Flexible times
5. Affordable Fees
6. Placement Support
7. Class recordings
8. Use AI tools and save 3 hours of your daily time.
9. The salary will grow three times as fast.
10. Unlock bonuses worth ₹ 50,000
11. Resume-Building Support
12. Mock interviews

Why Should You Learn Data Science and Machine Learning?

Learning Data Science and Machine Learning (DSML) is increasingly becoming a strategic move for individuals seeking to advance their careers in a data-driven world. Here’s a comprehensive look at why you should consider a course in DSML, covering opportunities, salaries, and industry applications.

1. Opportunities Worldwide

Expanding Job Market:

• High Demand: The demand for data scientists and machine learning experts is growing exponentially. With the rise of big data and AI, companies across all industries are seeking skilled professionals to interpret and manage data.
• Global Reach: Opportunities are not limited to one region. Countries like the United States, Canada, the United Kingdom, Germany, India, China, and Australia are actively hiring DSML experts.

Diverse Roles:

• Variety of Positions: Roles include Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, Data Engineer, and more.
• Cross-Industry Applications: The skills acquired in DSML are applicable across various sectors, ensuring a broad range of opportunities.

2. Average Salary Worldwide

Competitive Salaries:

• United States: According to Glassdoor, the average salary for a Data Scientist is around $113,000 per year, with Machine Learning Engineers earning slightly higher, averaging around $125,000 per year.
• Europe: In the UK, Data Scientists earn approximately £50,000 per year. In Germany, the average salary is about €60,000 per year.
• Asia: In India, the average salary for a Data Scientist is about ₹1,000,000 per year. In China, it's around ¥250,000 per year.
• Australia: Data Scientists earn an average of AUD 110,000 per year.

Salary Growth:

• Experience Matters: As professionals gain experience and expertise, their salaries can increase significantly. Senior Data Scientists and Machine Learning Engineers can earn well over $150,000 per year in the US.
• Specialized Skills: Expertise in niche areas such as deep learning, natural language processing, and AI can command even higher salaries.

3. Industry Applications

Finance:

• Fraud Detection: Banks and financial institutions use machine learning to detect fraudulent transactions and minimize risks.
• Algorithmic Trading: Data science models predict stock price movements, enabling automated, high-frequency trading.

Healthcare:

• Predictive Analytics: Machine learning models predict disease outbreaks, patient admission rates, and treatment outcomes.
• Personalized Medicine: Data science enables personalized treatment plans based on patient data and genetic information.

Retail and E-commerce:

• Customer Insights: Data science helps in understanding customer behavior, improving customer experiences, and personalizing recommendations.
• Supply Chain Optimization: Machine learning algorithms optimize inventory management and logistics.

Manufacturing:

• Predictive Maintenance: Using data from machinery, machine learning models predict failures before they happen, reducing downtime.
• Quality Control: Automated inspection systems using ML ensure high product quality and consistency.

Technology:

• Product Development: Tech companies use data science to inform product design and user experience improvements.
• AI and Automation: Leading advancements in AI are driven by machine learning research and applications.

Telecommunications:

• Network Optimization: Data science models optimize network performance and manage bandwidth allocation.
• Customer Churn Prediction: Machine learning helps predict and reduce customer churn by identifying at-risk customers.

Energy:

• Smart Grids: Data science enables the efficient management of energy distribution and load balancing.
• Predictive Maintenance: Similar to manufacturing, predictive analytics prevent equipment failures in energy plants.

Data Science and Machine Learning Course Content

Introduction to Course

Environment Setup

Machine Learning overview

Python Crash Course

➢ OPTIONAL: Python Crash Course

➢ Python Crash Course - Part One

➢ Python Crash Course - Part Two

➢ Python Crash Course - Part Three

➢ Python Crash Course - Exercise Questions

➢ Python Crash Course - Exercise Solutions

NumPy

➢ Introduction to NumPy

➢ NumPy Arrays

➢ Coding Exercise Check-in: Creating NumPy Arrays

➢ NumPy Indexing and Selection

➢ Coding Exercise Check-in: Selecting Data from Numpy Array

➢ NumPy Operations

➢ Check-In: Operations on NumPy Array

➢ NumPy Exercises

➢ Numpy Exercises – Solutions

Pandas

➢ Introduction to Pandas

➢ Series - Part One

➢ Check-in: Labeled Index in Pandas Series

➢ Series - Part Two

➢ DataFrames - Part One - Creating a DataFrame

➢ DataFrames - Part Two - Basic Properties

➢ DataFrames - Part Three - Working with Columns

➢ DataFrames - Part Four - Working with Rows

➢ Pandas - Conditional Filtering

➢ Pandas - Useful Methods - Apply on Single Column

➢ Pandas - Useful Methods - Apply on Multiple Columns

➢ Pandas - Useful Methods - Statistical Information and Sorting

➢ Missing Data - Overview

➢ Missing Data - Pandas Operations

➢ GroupBy Operations - Part One

➢ GroupBy Operations - Part Two - MultiIndex

➢ Combining DataFrames - Concatenation

➢ Combining DataFrames - Inner Merge

➢ Combining DataFrames - Left and Right Merge

➢ Combining DataFrames - Outer Merge

➢ Pandas - Text Methods for String Data

➢ Pandas - Time Methods for Date and Time Data

➢ Pandas Input and Output - CSV Files

➢ Pandas Input and Output - HTML Tables

➢ Pandas Input and Output - Excel Files

➢ Pandas Input and Output - SQL Databases

➢ Pandas Pivot Tables

➢ Pandas Project Exercise Overview

➢ Pandas Project Exercise Solutions

Matplotlib

➢ Introduction to Matplotlib

➢ Matplotlib Basics

➢ Matplotlib - Understanding the Figure Object

➢ Matplotlib - Implementing Figures and Axes

➢ Matplotlib - Figure Parameters

➢ Matplotlib-Subplots Functionality

➢ Matplotlib Styling - Legends

➢ Matplotlib Styling - Colors and Styles

➢ Advanced Matplotlib Commands (Optional)

➢ Matplotlib Exercise Questions Overview

➢ Matplotlib Exercise Questions – Solutions

Seaborn Data Visualizations

➢ Introduction to Seaborn

➢ Scatterplots with Seaborn

➢ Distribution Plots - Part One - Understanding Plot Types

➢ Distribution Plots - Part Two - Coding with Seaborn

➢ Categorical Plots - Statistics within Categories - Understanding Plot Types

➢ Categorical Plots - Statistics within Categories - Coding with Seaborn

➢ Categorical Plots - Distributions within Categories - Understanding Plot Types

➢ Categorical Plots - Distributions within Categories - Coding with Seaborn

➢ Seaborn - Comparison Plots - Understanding the Plot Types

➢ Seaborn - Comparison Plots - Coding with Seaborn

➢ Seaborn Grid Plots

➢ Seaborn - Matrix Plots

➢ Seaborn Plot Exercises Overview

➢ Seaborn Plot Exercises Solutions

Data Analysis and Visualization Capstone Project Exercise

➢ Capstone Project overview

➢ Capstone Project Solutions - Part One

➢ Capstone Project Solutions - Part Two

➢ Capstone Project Solutions - Part Three

Machine Learning Concepts Overview

➢ Introduction to Machine Learning Overview Section

➢ Why Machine Learning?

➢ Types of Machine Learning Algorithms

➢ Supervised Machine Learning Process

➢ Companion Book - Introduction to Statistical Learning

Linear Regression

➢ Introduction to Linear Regression Section

➢ Linear Regression -Algorithm History

➢ Linear Regression - Understanding Ordinary Least Squares

➢ Linear Regression - Cost Functions

➢ Linear Regression - Gradient Descent

➢ Python coding Simple Linear Regression

➢ Overview of Scikit-Learn and Python

➢ Linear Regression - Scikit-Learn Train Test Split

➢ Linear Regression - Scikit-Learn Performance Evaluation - Regression

➢ Linear Regression - Residual Plots

➢ Linear Regression - Model Deployment and Coefficient Interpretation

➢ Polynomial Regression - Theory and Motivation

➢ Polynomial Regression - Creating Polynomial Features

➢ Polynomial Regression - Training and Evaluation

➢ Bias Variance Trade-Off

➢ Polynomial Regression - Choosing Degree of Polynomial

➢ Polynomial Regression - Model Deployment

➢ Regularization Overview

➢ Feature Scaling

➢ Introduction to Cross Validation

➢ Regularization Data Setup

➢ L2 Regularization -Ridge Regression - Theory

➢ L2 Regularization - Ridge Regression - Python Implementation

➢ L1 Regularization - Lasso Regression - Background and Implementation

➢ L1 and L2 Regularization - Elastic Net

➢ Linear Regression Project - Data Overview

Feature Engineering and Data Preparation

➢ A note from Jose on Feature Engineering and Data Preparation

➢ Introduction to Feature Engineering and Data Preparation

➢ Dealing with Outliers

➢ Dealing with Missing Data : Part One - Evaluation of Missing Data

➢ Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows

➢ Dealing with Missing Data : Part 3 - Fixing data based on Columns

➢ Dealing with Categorical Data - Encoding Options

Cross Validation, Grid Search, and the Linear Regression Project

➢ Section Overview and Introduction

➢ Cross Validation - Test | Train Split

➢ Cross Validation - Test | Validation | Train Split

➢ Cross Validation - cross_val_score

➢ Cross Validation - cross_validate

➢ Grid Search

➢ Linear Regression Project Overview

➢ Linear Regression Project – Solutions

Logistic Regression

➢ Early Bird Note on Downloading .zip for Logistic Regression Notes

➢ Introduction to Logistic Regression Section

➢ Logistic Regression - Theory and Intuition - Part One: The Logistic Function

➢ Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic

➢ Logistic Regression - Theory and Intuition - Linear to Logistic Math

➢ Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood

➢ Logistic Regression with Scikit-Learn - Part One - EDA

➢ Logistic Regression with Scikit-Learn - Part Two - Model Training

➢ Classification Metrics - Confusion Matrix and Accuracy

➢ Classification Metrics - Precison, Recall, F1-Score

➢ Classification Metrics - ROC Curves

➢ Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation

➢ Multi-Class Classification with Logistic Regression - Part One - Data and EDA

➢ Multi-Class Classification with Logistic Regression - Part Two - Model

➢ Logistic Regression Exercise Project Overview

➢ Logistic Regression Project Exercise – Solutions

KNN-K Nearest Neighbors

➢ Introduction to KNN Section

➢ KNN Classification - Theory and Intuition

➢ KNN Coding with Python - Part One

➢ KNN Coding with Python - Part Two - Choosing K

➢ KNN Classification Project Exercise Overview

➢ KNN Classification Project Exercise Solutions

Support Vector Machines

➢ Introduction to Support Vector Machines

➢ History of Support Vector Machines

➢ SVM - Theory and Intuition - Hyperplanes and Margins

➢ SVM - Theory and Intuition - Kernel Intuition

➢ SVM - Theory and Intuition - Kernel Trick and Mathematics

➢ SVM with Scikit-Learn and Python - Classification Part One

➢ SVM with Scikit-Learn and Python - Classification Part Two

➢ SVM with Scikit-Learn and Python - Regression Tasks

➢ Support Vector Machine Project Overview

➢ Support Vector Machine Project Solutions

Tree Based Methods: Decision Tree Learning

➢ Introduction to Tree Based Methods

➢ Decision Tree - History

➢ Decision Tree - Terminology

➢ Decision Tree - Understanding Gini Impurity

➢ Constructing Decision Trees with Gini Impurity - Part One

➢ Constructing Decision Trees with Gini Impurity - Part Two

➢ Coding Decision Trees - Part One - The Data

➢ Coding Decision Trees - Part Two -Creating the Model

Random Forests

➢ Introduction to Random Forests Section

➢ Random Forests - History and Motivation

➢ Random Forests - Key Hyperparameters

➢ Random Forests - Number of Estimators and Features in Subsets

➢ Random Forests - Bootstrapping and Out-of-Bag Error

➢ Coding Classification with Random Forest Classifier - Part One

➢ Coding Classification with Random Forest Classifier - Part Two

➢ Coding Regression with Random Forest Regressor - Part One - Data

➢ Coding Regression with Random Forest Regressor - Part Two - Basic Models

➢ Coding Regression with Random Forest Regressor - Part Three - Polynomials

➢ Coding Regression with Random Forest Regressor - Part Four - Advanced Models

Boosting Methods

➢ Introduction to Boosting Section

➢ Boosting Methods - Motivation and History

➢ AdaBoost Theory and Intuition

➢ AdaBoost Coding Part One - The Data

➢ AdaBoost Coding Part Two - The Model

➢ Gradient Boosting Theory

➢ Gradient Boosting Coding Walkthrough

Supervised Learning Capstone Project-Cohort Analysis and Tree Based Methods

➢ Introduction to Supervised Learning Capstone Project

➢ Solution Walkthrough - Supervised Learning Project - Data and EDA

➢ Solution Walkthrough - Supervised Learning Project - Cohort Analysis

➢ Solution Walkthrough - Supervised Learning Project - Tree Models

Naive Bayes Classification and Natural Language Processing (Supervised Learning)

➢ Introduction to NLP and Naive Bayes Section

➢ Naive Bayes Algorithm - Part One - Bayes Theorem

➢ Naive Bayes Algorithm - Part Two - Model Algorithm

➢ Feature Extraction from Text - Part One - Theory and Intuition

➢ Feature Extraction from Text - Coding Count Vectorization Manually

➢ Feature Extraction from Text - Coding with Scikit-Learn

➢ Natural Language Processing - Classification of Text - Part One

➢ Natural Language Processing - Classification of Text - Part Two

➢ Text Classification Project Exercise Overview

➢ Text Classification Project Exercise Solutions

Unsupervised Learning

➢ Unsupervised Learning Overview

K-Means Clustering

➢ Introduction to K-Means Clustering Section

➢ Clustering General Overview

➢ K-Means Clustering Theory

➢ K-Means Clustering - Coding Part One

➢ K-Means Clustering Coding Part Two

➢ K-Means Clustering Coding Part Three

➢ K-Means Color Quantization - Part One

➢ K-Means Color Quantization - Part Two

➢ K-Means Clustering Exercise Overview

➢ K-Means Clustering Exercise Solution - Part One

➢ K-Means Clustering Exercise Solution - Part Two

➢ K-Means Clustering Exercise Solution - Part Three

Hierarchical Clustering

➢ Introduction to Hierarchical Clustering

➢ Hierarchical Clustering - Theory and Intuition

➢ Hierarchical Clustering - Coding Part One - Data and Visualization

➢ Hierarchical Clustering - Coding Part Two - Scikit-Learn

DBSCAN-Density-based spatial clustering of applications with noise

➢ Introduction to DBSCAN Section

➢ DBSCAN - Theory and Intuition

➢ DBSCAN versus K-Means Clustering

➢ DBSCAN - Hyperparameter Theory

➢ DBSCAN - Hyperparameter Tuning Methods

➢ DBSCAN - Outlier Project Exercise Overview

➢ DBSCAN - Outlier Project Exercise Solutions

Principal Component Analysis and Manifold Learning

➢ Introduction to Principal Component Analysis

➢ PCA Theory and Intuition - Part One

➢ PCA Theory and Intuition - Part Two

➢ PCA - Manual Implementation in Python

➢ PCA - SciKit-Learn

➢ PCA - Project Exercise Overview

➢ PCA - Project Exercise Solution

Model Deployment

➢ Model Deployment Section Overview

➢ Model Deployment Considerations

➢ Model Persistence

➢ Model Deployment as an API - General Overview

➢ Note on Upcoming Video

➢ Model API - Creating the Script

➢ Testing the AP