Machine Learning Algorithms with Python Online Course

Machine Learning Algorithms with Python Online Course

This course offers a complete journey into machine learning, starting with foundational concepts, terminology, and types of problems like regression and classification. You’ll explore the importance of data, essential statistics, and Python programming, covering everything from basic syntax to advanced data manipulation with libraries like NumPy and pandas. Through hands-on projects and case studies, you’ll implement algorithms including linear and logistic regression, Naive Bayes, decision trees, random forests, and support vector machines, with an introduction to neural networks. By the end, you’ll be equipped to build, optimize, and evaluate machine learning models for real-world applications.

Who should take this course?

This course is designed for aspiring data scientists, Python developers, and machine learning enthusiasts who want to understand and implement key ML algorithms. It’s also ideal for students and professionals looking to build predictive models and apply machine learning to real-world problems.

What you will learn

• Implement regression and classification algorithms
• Perform exploratory data analysis and data preprocessing
• Utilize Python libraries for data manipulation and visualization
• Build and optimize machine learning models
• Understand the principles of deep learning
• Develop skills to solve real-world machine learning problems

Course Outline

Introduction to Machine Learning

• Course Introduction
• Introduction to Machine Learning
• Machine Learning Terminology
• History of Machine Learning
• Machine Learning Use Cases and Types
• Role of Data in Machine Learning
• Challenges in Machine Learning
• Machine Learning Life Cycle and Pipelines
• Regression Problems
• Regression Models and Performance Metrics
• Classification Problems and Performance Metrics
• Optimizing Classification Metrics
• Bias and Variance

Statistical Techniques

• Statistics and Experiments
• Types of Data and Descriptive Statistics
• Random Variables and Normal Distribution
• Histograms and Normal Approximation
• Central Limit Theorem
• Probability Theory
• Binomial Theory - Expected Value and Standard Error
• Hypothesis Testing

Learning Python

• Introduction to Python
• Starting with Python with Jupyter Notebook
• Python Variables and Conditions
• Python Iterations 1
• Python Iterations 2
• Python Lists
• Python Tuples
• Python Dictionaries 1
• Python Dictionaries 2
• Python Sets 1
• Python Sets 2
• Numpy Arrays 1
• Numpy Arrays 2
• Numpy Arrays 3
• Pandas Series 1
• Pandas Series 2
• Pandas Series 3
• Pandas Series 4
• Pandas DataFrame 1
• Pandas DataFrame 2
• Pandas DataFrame 3
• Pandas DataFrame 4
• Pandas DataFrame 5
• Pandas DataFrame 6
• Python User Defined Functions
• Python Lambda Functions
• Python Lambda Functions and Date-Time Operations
• Python String Operations

Exploratory Data Analysis

• Exploratory Data Analysis
• Tools and Processes of EDA
• EDA Project 1
• EDA Project 2
• EDA Project 3
• EDA Project 4
• EDA Project 5
• EDA Project 6
• EDA Project 7

Linear Regression

• Linear Regression Introduction
• Training and Cost Function
• Cost Functions and Gradient Descent
• Linear Regression - Practical Approach
• Feature Scaling and Cost Functions
• OLS Assumptions and Testing
• Car Price Prediction
• Data Preparation and Analysis 1
• Data Preparation and Analysis 2
• Data Preparation and Analysis 3
• Model Building
• Model Evaluation and Optimization
• Model Optimization

Logistic Regression

• Logistic Regression Introduction
• Logit Model
• Telecom Churn Case Study
• Data Analysis and Feature Engineering
• Build the Logistic Model
• Model Evaluation - AUC-ROC
• Model Optimization 1
• Model Optimization 2

Naive Bayes Classification Algorithm

• Naive Bayes Probability Model
• Naive Bayes Probability Computation
• Employee Attrition Case Study
• Model Building and Optimization

Decision Tree Classifier

• Decision Tree - Model Concept
• Decision Tree - Learning Steps
• Gini Index and Entropy Measures
• Pruning and Hyperparameter Tuning
• Iris Dataset Case Study
• Model Optimization using Grid Search Cross Validation

Random Forest Ensemble

• Ensemble Techniques Bagging and Random Forest
• Random Forest Steps Pruning and Optimization
• Model Building and Hyperparameter Tuning using Grid Search CV
• Optimization Continued

Support Vector Machine

• Support Vector Machine Concepts
• Support Vector Machine Metrics and Polynomial SVM
• Support Vector Machine Project 1
• Support Vector Machine Predictions
• Support Vector Machine - Classifying Polynomial Data

Dimensionality Reduction - Principal Component Analysis (PCA)

• Principal Component Analysis - Concepts
• Principal Component Analysis - Computations 1
• Principal Component Analysis - Computations 2
• Principal Component Analysis Practicals

Unsupervised Learning using K-Means Clustering

• Unsupervised Learning - K-Mean Clustering
• K-Means Clustering Computation
• K-Means Clustering Optimization
• K-Means - Data Preparation and Modelling
• K-Means - Model Optimization

Introduction to Deep Learning

• Introduction to Deep Learning