Become a Machine Learning Expert: Top Python Training Course(Scikit-learn and TensorFlow)

Introduction

Unlock the power of Artificial Intelligence with our comprehensive training course on Machine Learning with Python, focusing on the leading libraries Scikit-learn and TensorFlow. This essential program equips individuals and teams with the fundamental concepts and practical skills to build intelligent systems, analyze data, and make data-driven predictions. Our training course covers the core principles of machine learning algorithms and their implementation using Python's robust ecosystem, including Scikit-learn for classical machine learning and TensorFlow for deep learning applications. Whether you're a data scientist, analyst, or developer, this training course provides the knowledge and hands-on experience to harness the potential of machine learning.

This Machine Learning with Python training course emphasizes a practical, code-centric approach, guiding participants through the entire machine learning workflow, from data preprocessing and model selection to training, evaluation, and deployment. You'll learn to implement various supervised and unsupervised learning algorithms, build neural networks with TensorFlow, and evaluate model performance using appropriate metrics. By the end of this impactful training course, you'll possess the core skills to tackle real-world machine learning problems and contribute to innovative AI solutions.

Target Audience

• Data scientists and analysts looking to implement machine learning models
• Software developers interested in integrating AI into their applications
• Researchers seeking to leverage machine learning for data analysis and discovery
• Professionals aiming to transition into the field of artificial intelligence
• Students and academics in computer science, statistics, and related fields
• Individuals looking to understand the fundamentals of machine learning
• Teams aiming to adopt machine learning for business intelligence and automation

Duration:

• 5 Days

Course Objectives

1. Understand the fundamental concepts and types of machine learning algorithms.
2. Master data preprocessing techniques using Python libraries like Pandas and NumPy.
3. Learn to implement and evaluate various supervised learning algorithms with Scikit-learn.
4. Gain proficiency in implementing and evaluating unsupervised learning algorithms with Scikit-learn.
5. Understand the basics of neural networks and deep learning with TensorFlow.
6. Learn to build and train simple neural network models using TensorFlow.
7. Evaluate the performance of machine learning models using appropriate metrics.
8. Understand the basics of model selection, hyperparameter tuning, and deployment.

Course Content

1. Module: Introduction to Machine Learning with Python

• Defining machine learning and its applications in various domains in the module
• Overview of different types of machine learning: supervised, unsupervised, reinforcement learning within the Python module
• Introduction to the Python libraries for machine learning: Scikit-learn, TensorFlow, Pandas, NumPy, Matplotlib, Seaborn in the module
• Setting up the Python environment for machine learning development within the module
• The typical machine learning workflow: data preparation, model selection, training, evaluation, deployment in the module

2. Module: Data Preprocessing and Feature Engineering

• Handling missing values and outliers in datasets using Python libraries in the module
• Scaling and normalizing features for machine learning models within the Python module
• Encoding categorical features for use in machine learning algorithms in the module
• Feature selection techniques to identify the most relevant features within the Python module
• Creating new features from existing ones (feature engineering) to improve model performance in the module

3. Module: Supervised Learning: Classification

• Understanding the principles of classification algorithms in machine learning in the module
• Implementing and evaluating classification models using Scikit-learn: Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forests, K-Nearest Neighbors (KNN) within the Python module
• Understanding evaluation metrics for classification: accuracy, precision, recall, F1-score, AUC-ROC curve in the module
• Techniques for handling imbalanced datasets in classification problems within the Python module
• Model selection and hyperparameter tuning for classification algorithms using Scikit-learn in the module

4. Module: Supervised Learning: Regression

• Understanding the principles of regression algorithms in machine learning in the module
• Implementing and evaluating regression models using Scikit-learn: Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression within the Python module  
• Understanding evaluation metrics for regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared in the module  
• Techniques for handling multicollinearity in regression problems within the Python module
• Model selection and hyperparameter tuning for regression algorithms using Scikit-learn in the module

5. Module: Unsupervised Learning

• Understanding the principles of clustering algorithms in machine learning in the module
• Implementing and evaluating clustering algorithms using Scikit-learn: K-Means, DBSCAN, Hierarchical Clustering within the Python module
• Understanding the principles of dimensionality reduction techniques in machine learning in the module
• Implementing dimensionality reduction techniques using Scikit-learn: Principal Component Analysis (PCA), t-SNE within the Python module
• Applications of clustering and dimensionality reduction in real-world scenarios in the module

6. Module: Introduction to Neural Networks and Deep Learning with TensorFlow

• Understanding the basic architecture of neural networks: neurons, layers, activation functions in the module
• Introduction to the TensorFlow library for building and training neural networks within the Python module
• Building simple neural network models for classification and regression tasks using TensorFlow Keras API in the module
• Understanding the concepts of forward propagation, backpropagation, and gradient descent in the context of neural networks within the Python module
• Introduction to different types of neural network architectures: feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs) (brief overview) in the module

7. Module: Model Evaluation and Selection

• Understanding the importance of splitting data into training, validation, and test sets in the module
• Implementing cross-validation techniques for robust model evaluation using Scikit-learn within the Python module
• Choosing appropriate evaluation metrics based on the machine learning task in the module
• Techniques for model selection based on performance on validation data within the Python module
• Understanding the concepts of bias-variance tradeoff in machine learning in the module

8. Module: Model Deployment and Advanced Topics (Overview)

• Introduction to basic concepts of deploying machine learning models in real-world applications in the module
• Overview of different deployment options: cloud platforms, APIs, embedded systems within the Python module
• Introduction to hyperparameter tuning techniques using libraries like GridSearchCV and RandomizedSearchCV in Scikit-learn within the Python module
• Brief overview of advanced machine learning topics such as ensemble methods (e.g., Boosting, Bagging) and time series analysis within the Python module
• Ethical considerations and best practices in machine learning development and deployment in the module

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank