Machine Learning with Python Training is a hands-on program designed to take learners from Python fundamentals to advanced machine learning techniques using real-world data. The course covers Python programming, data analysis, statistics, and machine learning models to help learners build strong analytical and predictive skills.

Participants learn to work with NumPy, Pandas, and Matplotlib for data manipulation and visualization, apply statistical methods and hypothesis testing, and build predictive models using regression, classification, time series, clustering, and ensemble techniques. Real-world case studies reinforce practical learning throughout the course.

By the end of the program, learners will be able to build, evaluate, and improve machine learning models using Python and apply data-driven insights across business and technical domains.

This course is designed to support learners from beginner to advanced levels and does not require prior machine learning experience. However, the following will help learners get the most value from the program:

• Basic computer skills and familiarity with using applications

• Interest in data analysis, statistics, or machine learning concepts

• Willingness to learn Python programming from the ground up

• Helpful but not required:

• Basic understanding of mathematics (algebra and simple equations)

• Familiarity with Excel or working with data

• Exposure to programming or scripting concepts

This course is suitable for students, professionals, and career changers who want a structured, end-to-end path into machine learning using Python.

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

• Write and execute Python programs using popular IDEs and notebooks

• Use NumPy and Pandas to clean, transform, and analyze datasets

• Import, export, and manage data from CSV, Excel, and JSON sources

• Create meaningful data visualizations using Matplotlib

• Apply descriptive statistics to summarize and understand data

• Perform hypothesis testing including z-tests, t-tests, and ANOVA

• Prepare datasets for machine learning through feature engineering and data cleaning

• Build and evaluate regression models for prediction and forecasting

• Implement logistic regression for classification problems

• Analyze time series data using AR, ARMA, and ARIMA models

• Apply unsupervised learning techniques such as K-Means clustering

• Build and compare decision tree and random forest models

• Evaluate model performance using appropriate metrics and validation techniques

• Identify and address issues such as overfitting and model bias

• Apply machine learning techniques to real-world business and analytical use cases

Completing this course prepares learners for a wide range of data, analytics, and machine learning roles across industries. After completing the training, learners will be better prepared for positions such as:

• Junior Data Analyst

• Data Analyst

• Machine Learning Engineer (Entry-Level)

• Junior Data Scientist

• Data Science Associate

• Business Analyst (Data & Analytics)

• Quantitative Analyst (Entry-Level)

• AI / ML Analyst

• Predictive Analytics Specialist

• Research Analyst (Data-Focused)

Module 1: Introduction to Python Programming

• Overview of Python and its role in data science and machine learning

• Installing Python and working with IDEs (Anaconda, Spyder, jupyter, IDLE)

• Writing and executing Python code

• Performing calculations and working interactively

• Installing and importing libraries using pip

• Introduction to core libraries: NumPy, Pandas, Matplotlib

Module 2: Pandas & NumPy Fundamentals

• Pandas data structures: Series and DataFrames

• Creating and working with different data types

• Indexing, slicing, iterating, and boolean conditions

• Importing and exporting data (CSV, Excel, JSON)

• Aggregation, sorting, and ranking

• Handling missing data (NaN values)

• Hierarchical indexing and summary statistics

• Introduction to A/B testing concepts

Module 3: Data Manipulation with Pandas

• Data preparation and cleaning

• Merging, joining, concatenating, and combining datasets

• Pivoting and reshaping data

• Removing duplicates and transforming data

• Binning, discretization, and outlier detection

• String manipulation and regular expressions

• Group By operations and advanced data aggregation

Module 4: Data Visualization with Matplotlib

• Introduction to Matplotlib and its architecture

• Creating line charts, scatter plots, and histograms

• Customizing plots, axes, legends, and annotations

• Working with multiple figures and subplots

• Creating interactive charts

• Saving visualizations and charts as images

Module 5: Statistics and Descriptive Analysis

• Mean, median, and mode

• Measuring variation and standard deviation

• Interpreting averages and distributions

• Applying statistical measures using Python

Module 6: One-Sample Hypothesis Testing

• Hypotheses, sampling distributions, and errors

• Z-tests and t-tests in Python

• Working with t-distributions

• Variance testing and chi-square distributions

• Visualizing statistical distributions

Module 7: Two-Sample Hypothesis Testing

• Comparing two populations

• Independent and paired sample t-tests

• Equal vs. unequal variances

• Hypothesis testing for paired samples

• Testing differences in variances

Module 8: Testing More Than Two Samples

• Introduction to ANOVA

• One-way ANOVA in Python

• Trend analysis and interpretation

Module 9: Advanced Statistical Testing

• Two-way ANOVA

• Working with multiple variables simultaneously

• Interpreting complex statistical results

Module 10: Introduction to Machine Learning

• What machine learning is and where it is used

• Types of machine learning problems

• How machines learn from data

• Overview of machine learning workflows in Python

Module 11: Regression Models for Forecasting

• Simple and multiple linear regression

• Ordinary least squares estimation

• Model building, training, and evaluation

• Overfitting and goodness of fit

• RMSE and performance metrics

• Case studies

Module 12: Logistic Regression

• Binary classification concepts

• Simple and multiple logistic regression

• Handling categorical variables and dummy encoding

• Data preparation and exploratory data analysis

• Model evaluation and confusion matrix

• Customer satisfaction analysis

Module 13: Time Series Analysis

• Time series concepts and notation

• Stationarity, seasonality, and autocorrelation

• ACF and PACF analysis

• AR, ARMA, and ARIMA models

• Automatic ARIMA modeling

Module 14: Cluster Analysis

• Unsupervised learning concepts

• K-Means clustering theory and implementation

• Visualizing clustering results

• Selecting the number of clusters

• Evaluating clustering performance

• Comparing clustering algorithms

Module 15: Decision Trees & Random Forest

• Decision tree fundamentals and splitting criteria

• Entropy, information gain, and pruning

• Tree-based classification models

• Random forest and ensemble techniques

• Model validation, interpretation, and accuracy comparison

• Decision Trees vs. Random Forests