AI & Machine Learning

Models, data and deployment.

1 month (4 weeks)
20 topics
Final exam

Book a trainer

Your learning path

4 weeks from start to certificate

1
Week 1
Python & data foundations
2
Week 2
Core machine learning
3
Week 3
Deep learning & AI tools
4
Week 4
Capstone
Finish
Exam & certificate

Every week

How each week works

Learn

Live class with your trainer

Practice

Guided hands-on exercises

Build

Mini project for your portfolio

Review

Feedback and Q&A

Concept lab

See how the core concepts connect

Follow the path from foundations to real workplace application. Every concept feeds the next project.

Week 1

Python for Data Science setup

Week 1

NumPy for numerical computing

Week 2

Introduction to Machine Learning

Week 2

Supervised Learning: Regression

Week 3

Introduction to Deep Learning

Week 3

Building Neural Networks with Keras

Week 4

Machine Learning Project Lifecycle

Week 4

Advanced Model Improvement

Topics per week

20 topics · 80 lessons

Week 15

Week 25

Week 35

Week 45

Course curriculum

Week 1 · 5 topics · 20 lessons

Python & data foundations

By the end of this week: Learners will be able to set up their Python environment, manipulate data using fundamental libraries, and visualize insights.

1.1Python for Data Science setup
- Anaconda and virtual environments
- Jupyter Notebook/Lab basics
- Basic Python syntax, data types
- Functions, loops, conditionals
1.2NumPy for numerical computing
- Arrays and array operations
- Broadcasting rules
- Vectorization for efficiency
- Linear algebra with NumPy
1.3Pandas for data manipulation
- DataFrames and Series
- Data loading (CSV, Excel)
- Data cleaning and preprocessing
- Grouping, merging, joining data
1.4Matplotlib & Seaborn visualization
- Basic plot types (scatter, bar)
- Customizing plots
- Statistical plots with Seaborn
- Interpreting data distributions
1.5Version control with Git & GitHub
- Git initialization and commits
- Branches and merging
- GitHub repositories and collaboration
- Pull requests and code review

Week 1
Python & data foundations

Python for Data Science setup

NumPy for numerical computing

Pandas for data manipulation

Matplotlib & Seaborn visualization

Version control with Git & GitHub

Hands-on project

Exploratory Data Analysis (EDA) of a public dataset

Week 2 · 5 topics · 20 lessons

Core machine learning

By the end of this week: Learners will be able to apply fundamental supervised and unsupervised machine learning algorithms, evaluate their performance, and understand model selection.

2.1Introduction to Machine Learning
- ML types: supervised, unsupervised
- Regression vs. Classification
- Bias-variance trade-off
- Overfitting and underfitting
2.2Supervised Learning: Regression
- Linear Regression (Scikit-learn)
- Polynomial Regression
- Evaluation metrics (MAE, RMSE, R²)
- Feature scaling and engineering
2.3Supervised Learning: Classification
- Logistic Regression
- Decision Trees, Random Forests
- Support Vector Machines (SVM)
- Evaluation (accuracy, precision, recall, F1)
2.4Model Evaluation & Selection
- Train-test split, cross-validation
- Confusion Matrix interpretation
- ROC curve, AUC score
- Hyperparameter tuning (GridSearchCV)
2.5Unsupervised Learning & Clustering
- K-Means clustering
- Hierarchical clustering
- DBSCAN algorithm
- Dimensionality reduction (PCA)

Week 2
Core machine learning

Introduction to Machine Learning

Supervised Learning: Regression

Supervised Learning: Classification

Model Evaluation & Selection

Unsupervised Learning & Clustering

Hands-on project

Predictive modeling of house prices using Scikit-learn

Week 3 · 5 topics · 20 lessons

Deep learning & AI tools

By the end of this week: Learners will gain foundational knowledge of deep learning concepts, build neural networks using TensorFlow/Keras, and explore popular AI tools and APIs.

3.1Introduction to Deep Learning
- Neural network architecture basics
- Activation functions (ReLU, Sigmoid)
- Forward and backpropagation
- Loss functions and optimizers
3.2Building Neural Networks with Keras
- Sequential API model building
- Dense layers and compilation
- Training and evaluation workflow
- TensorBoard for visualization
3.3Convolutional Neural Networks (CNNs)
- Convolutional layers and filters
- Pooling layers (max, average)
- CNN architectures (e.g., LeNet)
- Image classification with Keras
3.4Recurrent Neural Networks (RNNs)
- Time series and sequential data
- Simple RNNs and limitations
- LSTMs and GRUs for memory
- NLP basics with RNNs
3.5AI APIs & Cloud ML Platforms
- Google Cloud AI Platform
- AWS SageMaker overview
- Azure Machine Learning services
- OpenAI API integration examples

Week 3
Deep learning & AI tools

Introduction to Deep Learning

Building Neural Networks with Keras

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

AI APIs & Cloud ML Platforms

Hands-on project

Image classification with a Convolutional Neural Network (CNN)

Week 4 · 5 topics · 20 lessons

Capstone

By the end of this week: Learners will integrate their knowledge to develop an end-to-end ML solution, effectively present their findings, and understand deployment considerations.

4.1Machine Learning Project Lifecycle
- Problem definition and scope
- Data acquisition and preparation
- Model development and testing
- Deployment and monitoring phases
4.2Advanced Model Improvement
- Ensemble methods (Bagging, Boosting)
- XGBoost, LightGBM
- Transfer learning for DL
- Data augmentation techniques
4.3Ethical AI & Responsible ML
- Fairness and bias in AI
- Transparency and interpretability (SHAP)
- Privacy concerns (GDPR, CCPA)
- AI safety and societal impact
4.4ML Model Deployment Basics
- Serialization (Pickle, Joblib)
- Flask API for model serving
- Docker for containerization
- Cloud deployment options
4.5Capstone Project & Presentation
- Project planning and execution
- Data analysis and model building
- Visualizing results effectively
- Presenting technical insights clearly

Week 4
Capstone

Machine Learning Project Lifecycle

Advanced Model Improvement

Ethical AI & Responsible ML

ML Model Deployment Basics

Capstone Project & Presentation

Hands-on project

End-to-end ML project with presentation

Final exam & certificate

Finish the 4 weeks, then take the online exam. Score 70% or more to pass.

Job-ready syllabus

Learn it. Build it. Explain it. Use it.

The syllabus goes beyond watching lessons. You practice the tasks employers expect, produce evidence of your skills, and prepare to discuss your work clearly.

Workplace skills

Apply Python for Data Science setup, NumPy for numerical computing, Pandas for data manipulation, Matplotlib & Seaborn visualization in guided business scenarios.

Portfolio proof

Complete 4 practical projects, including Exploratory Data Analysis (EDA) of a public dataset.

Professional practice

Present your work, respond to feedback, document decisions, and improve the final result.

Interview readiness

Review common role questions and explain your process, tools, trade-offs, and project outcomes.

Your completion pack

Weekly projects, trainer feedback, final assessment, and a certificate you can add to your résumé and LinkedIn profile.

Why learn from us?

Six reasons students pick M - IT Solutions

One-month programs built around live teaching, real projects and support that actually shows up.

Live online classes

Learn with a real instructor in real time — not pre-recorded videos you watch alone.

Hands-on projects

Every week ends with practical work you can show employers on day one.

1-on-1 trainer time

Stuck or want to go deeper? Book private sessions with a trainer whenever you need.

A real learning area

Lessons, progress tracking and exams all live in your account, on any device.

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