Mentor Led

Machine Learning with Python (Mentor Led)

By CapaGenius Categories: Mentor Led

About Course

Experience the power of Machine Learning with Python in our comprehensive program designed to equip you with essential skills and knowledge in this dynamic field.

Our 8-week course covers everything from the fundamentals of Machine Learning and Python basics to advanced algorithms and techniques. You’ll learn how to preprocess data, perform exploratory data analysis, and implement machine learning algorithms using libraries like Scikit-Learn.

With hands-on projects and real-world applications, you’ll gain practical experience in dimensionality reduction, feature engineering, and even delve into the realm of deep learning for Natural Language Processing (NLP).

One of the highlights of the program is the capstone project, where you’ll apply your newfound skills to solve a real-world problem. In our sample capstone project, you’ll explore predictive maintenance using machine learning, giving you valuable experience that you can showcase to potential employers.

By enrolling in our program, you’ll not only gain valuable skills but also set yourself apart in the competitive job market. Whether you’re looking to start a career in data science or enhance your existing skills, our program offers the perfect opportunity to take your career to the next level. Don’t miss out on this chance to unlock your potential in the exciting field of Machine Learning with Python.

Course Content

Introduction to Machine Learning and Python Basics

Overview of Machine Learning
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
Introduction to Data Types, Variables, and Operators
Introduction to Control Structures: if, else, loops
Introduction to Functions and Modules
Introduction to Jupyter Notebooks

About Course

Course Content

Introduction to Machine Learning and Python Basics

Overview of Machine Learning

Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

Introduction to Data Types, Variables, and Operators

Introduction to Control Structures: if, else, loops

Introduction to Functions and Modules

Introduction to Jupyter Notebooks

Data Preprocessing and Exploratory Data Analysis (EDA)

Data Preprocessing on Handling Missing Data

Data Preprocessing on Data Cleaning and Transformation

Data Preprocessing on Feature Scaling

Data Preprocessing on Handling Categorical Data

Exploratory Data Analysis (EDA) on Summary Statistics

EDA on Data Visualization with Matplotlib and Seaborn Correlation Analysis

Introduction to Machine Learning Algorithms

Introduction to Scikit-Learn

Supervised Learning on Linear Regression

Supervised Learning on Logistic Regression

Supervised Learning on k-Nearest Neighbors (kNN)

Model Evaluation Techniques on Train-Test Split

Model Evaluation Techniques on Cross-Validation

Model Evaluation Techniques on Evaluation Metrics: RMSE, MAE, Accuracy, Precision, Recall, F1-score

Advanced Machine Learning Algorithms

Supervised Learning on Decision Trees

Supervised Learning on Random Forests

Supervised Learning on Support Vector Machines (SVM)

Unsupervised Learning on K-Means Clustering

Unsupervised Learning on Hierarchical Clustering

Model Selection and Hyperparameter Tuning

Dimensionality Reduction and Feature Engineering

Dimensionality Reduction Techniques on Principal Component Analysis (PCA)

Dimensionality Reduction Techniques on t-Distributed Stochastic Neighbor Embedding (t-SNE)

Feature Engineering on Feature Extraction

Feature Engineering on Feature Selection

Introduction to Deep Learning

Basics of Neural Networks on Perceptron

Basics of Neural Networks on Activation Functions

Basics of Neural Networks on Feedforward Neural Networks

Deep Learning for Natural Language Processing (NLP)

Basics of NLP

Word Embeddings (Word2Vec, GloVe)

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)

Sentiment Analysis and Text Generation

Project

Predictive Maintenance using Machine Learning