Machine Learning in Python

Lecture Details

** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka video on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This Data Science tutorial will be covering the following topics:

1:23 Evolution of Data
2:14 What is Data Science?
3:02 Data Science Careers
3:36 Who is a Data Analyst
4:20 Who is a Data Scientist
5:14 Who is a Machine Learning Engineer
5:44 Data Scientist Salary Trends
6:37 Data Scientist Road Map
9:06 Data Analyst Skills
10:41 Data Scientist Skills
11:47 Machine Learning Engineer Skills
12:53 Data Science Peripherals
13:17 What is Data ?
15:23 Variables & Research
17:28 Population & Sampling
20:18 Measures of Center
20:29 Measures of Spread
21:28 Skewness
21:52 Confusion Matrix
22:56 Probability
25:12 What is Machine Learning?
25:45 Features of Machine Learning
26:22 How Machine Learning works?
27:11 Applications of Machine Learning
34:57 Machine Learning Market Trends
36:05 Machine Learning Life Cycle
39:01 Important Python Libraries
40:56 Types of Machine Learning
41:07 Supervised Learning
42:27 Unsupervised Learning
43:27 Reinforcement Learning
46:27 Supervised Learning Algorithms
48:01 Linear Regression
58:12 What is Logistic Regression?
1:01:22 What is Decision Tree?
1:11:10 What is Random Forest?
1:18:48 What is Naïve Bayes?
1:30:51 Unsupervised Learning Algorithms
1:31:55 What is Clustering?
1:34:02 Types of Clustering
1:35:00 What is K-Means Clustering?
1:47:31 Market Basket Analysis
1:48:35 Association Rule Mining
1:51:22 Apriori Algorithm
2:00:46 Reinforcement Learning Algorithms
2:03:22 Reward Maximization
2:06:35 Markov Decision Process
2:08:50 Q-Learning
2:18:19 Relationship Between AI and ML and DL
2:20:10 Limitations of Machine Learning
2:21:19 What is Deep Learning ?
2:22:04 Applications of Deep Learning
2:23:35 How Neuron Works?
2:24:17 Perceptron
2:25:12 Waits and Bias
2:25:36 Activation Functions
2:29:56 Perceptron Example
2:31:48 What is TensorFlow?
2:37:05 Perceptron Problems
2:38:15 Deep Neural Network
2:39:35 Training Network Weights
2:41:04 MNIST Data set
2:41:19 Creating a Neural Network
2:50:30 Data Science Course Masters Program

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About the Masters Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

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Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

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