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Machine Learning Full Course for Beginners | Machine Learning Tutorial | Machine Learning Course
Hello and welcome to the Machine Learning Full Course for Beginners using python. In this video, you will learn from basics to advanced machine learning concepts from Great Learning’s top faculties, including professor Mukesh Rao, Bharani Akella & many other leading industry experts. If you are an enthusiast who wants to start with machine learning from scratch, this machine learning beginner video is the best to start with.
#machinelearningfullcourse #machinelearning #machinelearningbasics

Agenda:
• Python for Machine Learning
• Role of Statistics in Machine Learning
• Introduction to Machine Learning and its types
• How does a Machine learning model learn?
• Supervised and Unsupervised learning algorithms
• Principal component analysis for dimensionality reduction
• Application of Machine Learning

Topics Covered:
00:01:09 – What Is Machine learning? (Introduction to Machine Learning)
00:03:00 – Why Machine Learning?
00:04:22 – Road Map to Machine Learning
00:01:09 – How to Use Kaggle (www.kaggle.com)
Machine Learning with Python (Python Libraries for Machine Learning)
00:11:25 – NumPy Python Tutorial (How to Create NumPy Array)
00:14:58 – How to Initialize NumPy Array
00:22:19 – How to check the shape of NumPy arrays
00:24:42 – How to Join NumPy Arrays
00:28:15 – NumPy Intersection & Difference
00:31:50 – NumPy Array Mathematics
00:39:15 – NumPy Matrix
00:42:28 – How to Transpose NumPy Matrix
00:43:21 – NumPy Matrix Multiplication
00:45:45 – NumPy Save & Load
00:47:44 – Python Pandas Tutorial
00:48:09 – Pandas Series Object
00:58:44 – Pandas Dataframe
01:12:00 – Matplotlib Python Tutorial
01:12:12 – Line plot
01:26:32 – Bar plot
01:32:37 – Scatter Plot
01:40:35 – Histogram
01:46:16 – Box Plot
01:51:03 – Violin Plot
01:51:57 – Pie Chart
01:56:39 – DoughNut Chart
01:59:04 – SeaBorn Line Plot
02:07:27 – SeaBorn Bar Plot
02:15:15 – SeaBorn ScatterPlot
02:20:25 – SeaBorn Histogram/Distplot
02:26:52 – SeaBorn JointPlot
02:30:23 – SeaBorn BoxPlot
02:38:59 – Role of Mathematics in Data Science
02:40:23 – What is data?
02:42:34 – What is Information?
02:43:21 – What is Statistics?
02:43:58 – What is Population?
02:46:48 – What is Sample?
02:47:33 – What are Parameters?
02:47:55 – Measures of Central Tendency
02:51:10 – Understanding Empirical Rule
02:53:16 – What is Mean, median, and mode?
02:57:04 – Measures of Spread (Understanding Range, Inter Quartile Range & Box-plot)
03:12:56 – Types of Machine Learning (Supervised, Unsupervised & Reinforcement Learning)
03:27:43 – How does a Machine Learning Model Learn?
03:35:31 – Supervised Machine Learning (Mukesh Rao)
04:34:51 – Python for Machine Learning
04:46:40 – Linear Regression Algorithm (Hands-on)
05:21:13 – What is Logistic Regression
05:29:39 – Linear Regression vs Logistic Regression
05:40:15 – Naïve Bayes Algorithm
05:49:32 – Diabetes Prediction using Naïve Bayes
06:15:18 – Decision Tree and Random Forest Algorithm
07:55:01 – Introduction to Support Vector Machines (SVMs)
08:07:08 – Kernel Functions
08:11:56 – Advantages & Disadvantages of SVMs
08:31:37 – K-NN Algorithm (K-Nearest Neighbour Algorithm)
08:40:13 – Introduction to Unsupervised Learning – Clustering
08:48:35 – Introduction to Principal Component Analysis
09:09:39 – PCA for Dimensionality Reduction
09:15:27 – Introduction to Hierarchical Clustering
09:28:38 – Types of Hierarchical Clustering
09:34:02 – How does Agglomerative hierarchical clustering work
09:42:32 – Euclidean Distance
09:45:10 – Manhattan Distance
09:48:01 – Minkowski Distance
09:50:02 – Jaccard Similarity Coefficient/Jaccard Index
09:54:02 – Cosine Similarity
09:58:18 – How to find an optimal number for clustering
10:03:02 – Applications Machine Learning

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