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- NumPy Matrix Multiplication
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Machine Learning Full Course for Beginners
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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
- 6 - How to Initialize NumPy Array
- 7 - How to check the shape of NumPy arrays
- 8 - How to Join NumPy Arrays
- 9 - NumPy Intersection & Difference
- 10 - NumPy Array Mathematics
- 11 - NumPy Matrix
- 12 - How to Transpose NumPy Matrix
- 13 - NumPy Matrix Multiplication
- 14 - NumPy Save & Load
- 15 - Python Pandas Tutorial
- 16 - Pandas Series Object
- 17 - Pandas Dataframe
- 18 - Matplotlib Python Tutorial
- 19 - Line plot
- 20 - Bar plot
- 21 - Scatter Plot
- 22 - Histogram
- 23 - Box Plot
- 24 - Violin Plot
- 25 - 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
- 41 – 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
- 63 – How does Agglomerative hierarchical clustering work
- 64 – Euclidean Distance
- 65 – 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
