Machine Learning

Our Machine learning curriculum is designed for beginners and progressing to intermediate concepts. It introduces the essential theoretical concepts and practical applications, gradually building knowledge and skills in machine learning.

Highlights

Introduction to Machine Learning (ML)

Data Processing and Feature Engineering

Introduction to Supervised Learning

Working with Time-series and Text Data

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Course Details

1) Overview of Machine Learning

2) Fundamentals of dat

3) Python for AMachine Learning
1) Data Cleaning

2) Feature Engineering

3) Project 1: Data Cleaning and Preprocessing
1) Supervised Learning Basics

2) Linear Regression

3) Classification with k-Nearest Neighbors (k-NN)

4) Project 2: Predicting House Prices
1) Decision Trees and Random Forests

2) Support Vector Machines (SVM)

3) Project 3: Customer Churn Prediction
1) Evaluation Metrics for Classification

2) Cross Validation

3) Hyperparameter Tuning
1) Clustering with k-Means

2) Hierarchical Clustering

3) Dimensionality Reduction with PCA

4) Project 4: Customer Segmentation
1) Basics of Neural Networks

2) Training a Basic Neural Network

3) Introduction to Deep Learning Libraries
1) Convolutional Neural Networks (CNNs)

2) Project 5: Image Classification with CNN
1) Time Series Analysis

2) Natural Language Processing (NLP) Basics

3) Project 6: Sentiment Analysis