Basic Machine Learning with Python

Unlock the Power of Python in Machine Learning!
Why This Training?
Leverage your existing Python knowledge to step into the transformative world of Machine Learning. Dive deep into essential algorithms, data processing techniques, and hands-on projects!
Duration: 30 Hours (online / virtual live session)

Why Advanced Machine Learning with Python?

 Practical Synergy: Marry your Python prowess with ML to solve real-world challenges.
 Evolving Landscape: Stay ahead in the rapidly evolving tech space with a skill that's in high demand.
 Holistic Learning: Balanced blend of theory and practical application to ensure robust understanding.

Who Should Attend?

 Python developers eager to extend their skills to ML.
 Data enthusiasts looking for a structured pathway to advance in ML.
 IT professionals wanting to stay updated with the latest in tech.

Course Highlights

 Python & ML: Refresh your Python skills tailored for ML applications.
 Algorithms Deep Dive: Linear Regression, Logistic Regression, Decision Trees, Clustering, and more.
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 Hands-on Learning: Embark on a real-world project, guiding you from data preprocessing to model deployment.
 From Basics to Beyond: Begin with ML fundamentals and ascend to intermediate techniques.
 Expert Guidance: Led by industry professionals with vast experience in Python and ML.

Essential Software & Tools

Python Environment: Ensure the latest stable version of Python is installed.
Integrated Development Environment (IDE):
  • Jupyter Notebook: Preferably, as it's extensively used in data science and machine learning tasks.
  • Alternatively, PyCharm or another suitable IDE can be set up.
Key Libraries & Frameworks:
  • scikit-learn: Required for most machine learning exercises in the course.
  • pandas: Necessary for data manipulation.
  • NumPy: For mathematical operations.
  • Matplotlib & Seaborn: For visualization exercises.
  • TensorFlow or PyTorch (if the course touches on neural networks).


  • Download links or files will be provided before the start of the course. Make sure to download and keep datasets in an accessible location.

Hardware Requirements

A laptop or desktop capable of running Python and machine learning libraries without lag. Ideally, a computer with at least 8GB RAM and a modern multi-core processor.
A reliable, high-speed internet connection (especially if the training is online).
Webcam & Microphone
Essential for interactive sessions, discussions, and virtual collaboration if the training is online.
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Training Content

Basic Machine Learning with Python (Basic to Intermediate Level)

Total Duration: 30 hours (10 sessions, 3 hours each)

1. Introduction & Python Refresher (3 hours)

  • Overview of Machine Learning (45 minutes)
  • Python ecosystem for ML: Libraries and tools (45 minutes)
  • Efficient data handling with NumPy and pandas (45 minutes)
  • Basic data visualization with Matplotlib and Seaborn (45 minutes)

2. Fundamentals of ML & Data Preprocessing Part 1 (3 hours)

  • Training, Validation, Testing, Overfitting, Underfitting (45 minutes)
  • Supervised vs. Unsupervised Learning & Classification vs. Regression (45 minutes)
  • Data cleaning and normalization (45 minutes)
  • Handling missing values (45 minutes)

3. Data Preprocessing Part 2 & Beginning of ML Algorithms (3 hours)

  • Categorical data encoding (45 minutes)
  • Train-Test data split (45 minutes)
  • Introduction to Linear Regression: Theory and math (45 minutes)
  • Implementing Linear Regression with Python (45 minutes)

4. Deep Dive into ML Algorithms Part 1 (3 hours)

  • Logistic Regression: Theory and implementation (1.5 hours)
  • Decision Trees: Basics, theory, and implementation (1.5 hours)

5. Deep Dive into ML Algorithms Part 2 (3 hours)

  • K-Means Clustering: Theory and implementation (1.5 hours)
  • Introduction to Random Forests and ensemble methods (1.5 hours)

6. Model Evaluation, Fine-Tuning & Intermediate ML Techniques (3 hours)

  • Understanding key metrics and Cross-validation (1 hour)
  • Hyperparameter tuning (30 minutes)
  • Introduction to Neural Networks and perceptrons (1 hour)
  • Basics of implementing Neural Networks with TensorFlow/Keras (30 minutes)

7. Feature Engineering, Selection & Handling Imbalanced Data (3 hours)

  • Importance and techniques of feature engineering (1 hour)
  • Techniques for feature selection (1 hour)
  • Understanding and handling imbalanced datasets (1 hour)

8. Advanced Model Implementation & Real-World ML Project Part 1 (3 hours)

  • Delving deeper into Random Forests (1 hour)
  • Beginning a real-world project: Data preprocessing and visualization (2 hours)

9. Real-World ML Project Part 2 (3 hours)

  • Continuing with the real-world project: Model selection, training, and validation (2 hours)
  • Introduction to model deployment and maintenance considerations (1 hour)

10. Concluding Topics, Future Pathways & Feedback (3 hours)

  • Advanced topics overview: Deep Learning, Reinforcement Learning, etc. (1 hour)
  • Resources for further learning and practice (30 minutes)
  • Q&A and feedback session (1 hour)
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