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1
– What Is Machine learning? Introduction to Machine Learning
2
– Why Machine Learning?
3
– Road Map to Machine Learning
4
– How to Use Kaggle www.kaggle.com
5
- NumPy Python Tutorial How to Create NumPy Array
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- How to Initialize NumPy Array
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- How to check the shape of NumPy arrays
8
- How to Join NumPy Arrays
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- NumPy Intersection & Difference
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- NumPy Array Mathematics
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- NumPy Matrix
12
- How to Transpose NumPy Matrix
13
- NumPy Matrix Multiplication
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- NumPy Save & Load
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- Python Pandas Tutorial
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- Pandas Series Object
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- Pandas Dataframe
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- Matplotlib Python Tutorial
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- Line plot
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- Bar plot
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- Scatter Plot
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- Histogram
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- Box Plot
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- Violin Plot
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- Pie Chart
26
- DoughNut Chart
27
- SeaBorn Line Plot
28
- SeaBorn Bar Plot
29
- SeaBorn ScatterPlot
30
- SeaBorn Histogram/Distplot
31
- SeaBorn JointPlot
32
- SeaBorn BoxPlot
33
– Role of Mathematics in Data Science
34
– What is data?
35
– What is Information?
36
– What is Statistics?
37
– What is Population?
38
– What is Sample?
39
– What are Parameters?
40
– Measures of Central Tendency
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– Understanding Empirical Rule
42
– What is Mean, median, and mode?
43
– Measures of Spread Understanding Range, Inter Quartile Range & Box-plot
44
– Types of Machine Learning Supervised, Unsupervised & Reinforcement Learning
45
– How does a Machine Learning Model Learn?
46
– Supervised Machine Learning Mukesh Rao
47
– Python for Machine Learning
48
– Linear Regression Algorithm Hands-on
49
– What is Logistic Regression
50
– Linear Regression vs Logistic Regression
51
– Naïve Bayes Algorithm
52
– Diabetes Prediction using Naïve Bayes
53
– Decision Tree and Random Forest Algorithm
54
– Introduction to Support Vector Machines SVMs
55
– Kernel Functions
56
– Advantages & Disadvantages of SVMs
57
– K-NN Algorithm K-Nearest Neighbour Algorithm
58
– Introduction to Unsupervised Learning - Clustering
59
– Introduction to Principal Component Analysis
60
– PCA for Dimensionality Reduction
61
– Introduction to Hierarchical Clustering
62
– Types of Hierarchical Clustering
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– How does Agglomerative hierarchical clustering work
64
– Euclidean Distance
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– Manhattan Distance
66
– Minkowski Distance
67
– Jaccard Similarity Coefficient/Jaccard Index
68
– Cosine Similarity
69
– How to find an optimal number for clustering
70
– Applications Machine Learning
Description:
Embark on a comprehensive 10-hour machine learning journey, starting from the fundamentals and progressing to advanced concepts. Explore essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Dive into statistical concepts crucial for machine learning, including measures of central tendency and spread. Understand various types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Gain hands-on experience with popular algorithms such as Linear Regression, Logistic Regression, Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, and K-Nearest Neighbors. Delve into unsupervised learning techniques like clustering and dimensionality reduction with Principal Component Analysis. Learn about distance metrics, similarity coefficients, and hierarchical clustering methods. Conclude with practical applications of machine learning, equipping you with a solid foundation to tackle real-world problems. Read more

Machine Learning Full Course for Beginners

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#Computer Science #Machine Learning #Mathematics #Statistics & Probability #Programming #Programming Languages #Python #Supervised Learning #Unsupervised Learning #Data Science #Data Visualization #Seaborn #Matplotlib #pandas #NumPy #Principal Component Analysis
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– What Is Machine learning? Introduction to Machine Learning