MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 164 lectures (18h 19m) | Size: 5.88 GB
Data Science, Machine Learning Python, Deep Learning, TensorFlow 2.0, NLP, Statistics for Data Science, Data Analysis !
What you'll learn:
Go from total beginners to confident machine learning engineer
Apply Machine Learning algorithm on 10+ dataset
Refresh all basic statistics & Probability Concepts
Get complete Environment ready with Google Colab Notebook
Machine Learning with different kind of ML System
Handle missing data, Grouping, Merging Joining and Concatenating Data wih Pandas Dataframe
Transform your data with One Hot Encoding & Feature scaling
Calculate Grades using Simple Linear Regression
Predict Restaurant Profit with Multiple Linear Regression
Apply SVR, SVM, Decision tree and Random Forest on Real Dataset
Apply different classification Algorithm
Classify Fashion clothes image with Artificial Neural Network + Keras
Build Credit Card Fraud Detection with Convolution Neural Network
Apply Natural Language Processing Technique like Tokenization, Stemming, Stop Words, Named Entity Recognition, Sentence Segmentation
Classify IMDB Review using Recurrent Neural Network - LSTM
Get Hands-on with Python Crash Course, Data analysis and Visualization with NumPy, Pandas & Matplotlib
Requirements
No prior knowledge or experience needed, only passion to learn
Description
According to an IBM report, Data Science jobs would likely grow by 30 percent. The estimated figure of job listing is 2,720,000 for Data Science in 2020
And according to the US Bureau of Labor Statistics, about 11 million jobs will be created by 2026
Data Science, Machine Learning and Artificial Intelligence are hottest and trending technologies across the globe, almost every multinational organization is working on it and they need a huge number people who can work on these technologies
By keeping all the industry requirements in mind we have designed this course, with this single course you can start your journey in the field of Data Science
In this course we tried to cover almost everything that is comes under the umbrella of Data Science,
Topics covered:
1) Machine Learning Overview: Types of Machine Learning System, Machine Learning vs Traditional system of Computing, Different Machine Learning Algorithm, Machine Learning Workflow
2) Statistics Basic: Data, Levels of Measurement, Measures of Central Tendency, Population vs Sample, Probability based Sampling methods, Non Probability based Sampling method, Measures of Dispersion, Quartiles and IQR
3) Probability: Introduction to Probability, Permutations, Combinations, Intersection, Union and Complement, Independent and Dependent Events, Conditional Probability, Addition and Multiplication Rules, Bayes' Theorem
4) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling
5) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regression
6) Classification Techniques: Logistic Regression, KNN, Support Vector Machine, Decision Tree, Random Forest Classification
7) Natural Language Processing: Tokenization, Stemming, Lemmatization, Stop Words, Vocabulary and Matching, Parts of Speech Tagging, Named Entity Recognition, Sentence Segmentation
8) Artificial Neural Networks (ANNs): The Neuron, Activation Function, Cost Function, Gradient Descent and Back-Propagation, Building the Artificial Neural Networks, Binary Classification with Artificial Neural Networks
9) Convolutional Neural Networks (CNNs): Theory behind Convolutional Neural Networks, Different layers in Convolutional Neural Networks, Building Convolutional Neural Networks, Credit Card Fraud Detection with CNN
10) Recurrent Neural Network (RNNs): Theory behind Recurrent Neural Networks, Vanishing Gradient Problem, Working of LSTM and GRU, IMDB Review Classification with RNN - LSTM
11) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations
12) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files
13) Data Visualization with Matplotlib: Functional Method, Object Oriented Method, Subplots Method, Figure size, Aspect ratio and DPI, Matplotlib properties, Different type of plots like Scatter Plot, Bar plot, Histogram, Pie Chart
14) Python Crash Course: Part 1: Data Types, Part 2: Python Statements, Part 3: Functions, Part 4: Object Oriented Programming
Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way !
Regards,
Vijay Gadhave
Who this course is for
Anyone who wants to learn Data Science and Machine Learning
Professionals who want to start a new career in Machine Learning
Anyone who is interested in Machine Learning and Data science
