Full course content

• • Class 1 - Introduction to Data Science and Python Basics
• • Class 2 - Python Basics Variables, data types, loops, conditions, & functions
• • Class 3 - Data Acquisition Data sources, data formats, Methods to collect and clean data
• • Class 4 - Data Exploration Descriptive statistics, data visualization, & correlation analysis
• • Class 5 - Data Preparation Data cleaning, feature scaling, encoding categorical data
• • Class 6 - Overview of machine learning, types of machine learning algorithms, & supervised learning
• • Class 7 - Data cleaning project using Python & Pandas during the live session
• • Class 8 - Linear Regression Simple linear regression, multiple linear regression
• • Class 9 - Classification Logistic regression, K-Nearest Neighbors, & model evaluation
• • Class 10 - Decision Trees Introduction to decision trees, Gini index, & Information gain
• • Class 11 - Random Forest Introduction to random forests, bagging, and boosting
• • Class 12 - Supervised learning project using scikit-learn during the live session
• • Class 13 - Introduction to unsupervised learning, clustering algorithms, and K-Means clustering
• • Class 14 - Introduction to principal component analysis (PCA) and t-Distributed (t-SNE)
• • Class 15 - Introduction to NLP, tokenization, stemming, & lemmatization
• • Class 16 - Introduction to sentiment analysis, preprocessing, feature extraction
• • Class 17 - Introduction to text classification, bag-of-words model, and Naive Bayes
• • Class 18 - Text classification project using NLP techniques during the live session
• • Class 19 - Introduction to artificial neural networks, perceptron
• • Class 20 - Neural network project using TensorFlow during the live session
• • Class 21 - Introduction to CNN, convolutional layers, & pooling layers
• • Class 22 - CNN project using TensorFlow during the live session
• • Class 23 - Recurrent Neural Networks (RNN) Introduction to RNN, LSTM, and GRU
• • Class 24 - Introduction to time series analysis, trend, seasonality, and autocorrelation
• • Class 25 - RNN project using TensorFlow during the live session
• • Class 26 - Introduction to forecasting, moving average, exponential smoothing