This course aims to teach beginners the basics to advanced concepts of machine learning using Python. The learning outcomes include understanding the role of statistics in machine learning, learning supervised and unsupervised learning algorithms, and applying principal component analysis for dimensionality reduction. The course covers Python libraries for machine learning, NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization. The teaching method includes lectures, hands-on demonstrations, and practical examples. This course is intended for enthusiasts who want to start learning machine learning from scratch.
Overview
Syllabus
– What Is Machine learning? Introduction to Machine Learning
– Why Machine Learning?
– Road Map to Machine Learning
– How to Use Kaggle www.kaggle.com
- NumPy Python Tutorial How to Create NumPy Array
- How to Initialize NumPy Array
- How to check the shape of NumPy arrays
- How to Join NumPy Arrays
- NumPy Intersection & Difference
- NumPy Array Mathematics
- NumPy Matrix
- How to Transpose NumPy Matrix
- NumPy Matrix Multiplication
- NumPy Save & Load
- Python Pandas Tutorial
- Pandas Series Object
- Pandas Dataframe
- Matplotlib Python Tutorial
- Line plot
- Bar plot
- Scatter Plot
- Histogram
- Box Plot
- Violin Plot
- Pie Chart
- DoughNut Chart
- SeaBorn Line Plot
- SeaBorn Bar Plot
- SeaBorn ScatterPlot
- SeaBorn Histogram/Distplot
- SeaBorn JointPlot
- SeaBorn BoxPlot
– Role of Mathematics in Data Science
– What is data?
– What is Information?
– What is Statistics?
– What is Population?
– What is Sample?
– What are Parameters?
– Measures of Central Tendency
– Understanding Empirical Rule
– What is Mean, median, and mode?
– Measures of Spread Understanding Range, Inter Quartile Range & Box-plot
– Types of Machine Learning Supervised, Unsupervised & Reinforcement Learning
– How does a Machine Learning Model Learn?
– Supervised Machine Learning Mukesh Rao
– Python for Machine Learning
– Linear Regression Algorithm Hands-on
– What is Logistic Regression
– Linear Regression vs Logistic Regression
– Naïve Bayes Algorithm
– Diabetes Prediction using Naïve Bayes
– Decision Tree and Random Forest Algorithm
– Introduction to Support Vector Machines SVMs
– Kernel Functions
– Advantages & Disadvantages of SVMs
– K-NN Algorithm K-Nearest Neighbour Algorithm
– Introduction to Unsupervised Learning - Clustering
– Introduction to Principal Component Analysis
– PCA for Dimensionality Reduction
– Introduction to Hierarchical Clustering
– Types of Hierarchical Clustering
– How does Agglomerative hierarchical clustering work
– Euclidean Distance
– Manhattan Distance
– Minkowski Distance
– Jaccard Similarity Coefficient/Jaccard Index
– Cosine Similarity
– How to find an optimal number for clustering
– Applications Machine Learning
Taught by
Great Learning
