Machine Learning

Module 1: Foundations of Data Science

• Introduction to Data Science:
• Definition and scope of data science
• Importance of data in today's world
• Applications of data science across industries

• Data Types and Structures:
• Numerical and categorical data
• Data structures (lists, tuples, dictionaries, sets)

• Statistical Concepts:
• Descriptive statistics (mean, median, mode, standard deviation, variance)
• Probability theory (probability distributions, Bayes' theorem)

• Python Programming:
• Fundamentals of Python programming (variables, data types, control flow)
• Data manipulation and analysis using libraries like NumPy and Pandas

Module 2: Data Collection and Cleaning

• Data Sources:
• Publicly available datasets (Kaggle, UCI Machine Learning Repository)
• Web scraping
• APIs

• Data Cleaning:
• Handling missing values (imputation techniques)
• Outlier detection and treatment
• Data normalization and standardization

• Data Visualization:
• Introduction to data visualization
• Creating various types of charts (bar, line, scatter, histograms, etc.)
• Using libraries like Matplotlib and Seaborn

Module 3: Exploratory Data Analysis (EDA)

• EDA Techniques:
• Summary statistics
• Data distribution analysis
• Correlation analysis
• Hypothesis testing

• EDA Tools:
• Jupyter Notebook

Module 4: Machine Learning Algorithms

• Supervised Learning:
• Regression (linear, logistic, decision trees, random forests)
• Classification (k-nearest neighbors, support vector machines, naive Bayes)

• Unsupervised Learning:
• Clustering (k-means, hierarchical clustering)
• Dimensionality reduction (PCA, t-SNE)

• Evaluation Metrics:
• Accuracy, precision, recall, F1-score
• Confusion matrix
• Cross-validation

Module 5: Deep Learning

• Neural Networks:
• Introduction to neural networks
• Feedforward neural networks
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)

• • Deep Learning Frameworks:
• TensorFlow
• PyTorch

Module 6: Natural Language Processing (NLP)

• Text Preprocessing:
• Tokenization, stemming, lemmatization

• NLP Tasks:
• Sentiment analysis
• Text classification
• Machine translation

Module 7: Big Data Analytics

• Hadoop and Spark:
• Introduction to big data
• Hadoop ecosystem (HDFS, MapReduce)
• Apache Spark (RDDs, DataFrames)

Module 8: Machine Learning Projects and Case Studies

• Project Selection:
• Identifying relevant problem statements
• Data acquisition and cleaning

• Project Execution:
• EDA, modeling, evaluation

• Presentation and Communication:
• Effective storytelling with data

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