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# Machine Learning Full Course For Beginners | Machine Learning Tutorial | Machine Learning Course

### Syllabus

â€“ What Is Machine learning? Introduction to Machine Learning
â€“ Why Machine Learning?
â€“ Road Map to Machine Learning
â€“ 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
- 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
â€“ Role of Mathematics in Data Science
â€“ What is data?
â€“ What is Information?
â€“ What is Statistics?
â€“ What is Population?
â€“ What is Sample?
â€“ What are Parameters?
â€“ Measures of Central Tendency
â€“ Understanding Empirical Rule
â€“ What is Mean, median, and mode?
â€“ Measures of Spread Understanding Range, Inter Quartile Range & Box-plot
â€“ Types of Machine Learning Supervised, Unsupervised & Reinforcement Learning
â€“ How does a Machine Learning Model Learn?
â€“ Supervised Machine Learning Mukesh Rao
â€“ Python for Machine Learning
â€“ Linear Regression Algorithm Hands-on
â€“ What is Logistic Regression
â€“ Linear Regression vs Logistic Regression
â€“ NaÃ¯ve Bayes Algorithm
â€“ Diabetes Prediction using NaÃ¯ve Bayes
â€“ Decision Tree and Random Forest Algorithm
â€“ Introduction to Support Vector Machines SVMs
â€“ Kernel Functions
â€“ K-NN Algorithm K-Nearest Neighbour Algorithm
â€“ Introduction to Unsupervised Learning - Clustering
â€“ Introduction to Principal Component Analysis
â€“ PCA for Dimensionality Reduction
â€“ Introduction to Hierarchical Clustering
â€“ Types of Hierarchical Clustering
â€“ How does Agglomerative hierarchical clustering work
â€“ Euclidean Distance
â€“ Manhattan Distance
â€“ Minkowski Distance
â€“ Jaccard Similarity Coefficient/Jaccard Index
â€“ Cosine Similarity
â€“ How to find an optimal number for clustering
â€“ Applications Machine Learning

Great Learning

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