- Machine Learning on Time Series data
- How to improve your Machine Learning Models
- Implement Machine Learning algorithms
- Program in Python using the latest Python 3
- Machine Learning on Time Series data
- Learn to use the popular library Scikit & More
BENEFITS
Best Seller
Online Courses
24/7 Support
Lifetime Access
Get Certificate
Offer Curriculum
01 - Introduction
Available in
days
days
after you enroll
02 - Machine Learning 101
Available in
days
days
after you enroll
- 001 What Is Machine Learning_ (6:52)
- 002 AI_Machine Learning_Data Science (4:51)
- 003 Exercise_ Machine Learning Playground (6:16)
- 004 How Did We Get Here_ (6:03)
- 005 Exercise_ YouTube Recommendation Engine (4:25)
- 006 Types of Machine Learning (4:41)
- 008 What Is Machine Learning_ Round 2 (4:45)
- 009 Section Review (1:48)
03 - Machine Learning and Data Science Framework
Available in
days
days
after you enroll
- 001 Section Overview (3:08)
- 002 Introducing Our Framework (2:38)
- 003 6 Step Machine Learning Framework (4:59)
- 004 Types of Machine Learning Problems (10:32)
- 005 Types of Data (4:51)
- 006 Types of Evaluation (3:31)
- 007 Features In Data (5:22)
- 008 Modelling - Splitting Data (5:58)
- 009 Modelling - Picking the Model (4:35)
- 010 Modelling - Tuning (3:17)
- 011 Modelling - Comparison (9:32)
- 013 Experimentation (3:35)
- 014 Tools We Will Use (4:00)
04 - The 2 Paths
Available in
days
days
after you enroll
05 - Data Science Environment Setup
Available in
days
days
after you enroll
- 001 Section Overview (1:09)
- 002 Introducing Our Tools (3:28)
- 003 What is Conda_ (2:35)
- 004 Conda Environments (4:30)
- 005 Mac Environment Setup (17:26)
- 006 Mac Environment Setup 2 (14:11)
- 007 Windows Environment Setup (5:17)
- 008 Windows Environment Setup 2 (23:17)
- 011 Jupyter Notebook Walkthrough (10:20)
- 012 Jupyter Notebook Walkthrough 2 (16:18)
- 013 Jupyter Notebook Walkthrough 3 (8:10)
06 - Pandas_ Data Analysis
Available in
days
days
after you enroll
- 001 Section Overview (2:27)
- 003 Pandas Introduction (4:29)
- 004 Series, Data Frames and CSVs (13:21)
- 006 Describing Data with Pandas (9:48)
- 007 Selecting and Viewing Data with Pandas (11:08)
- 008 Selecting and Viewing Data with Pandas Part 2 (13:07)
- 009 Manipulating Data (13:57)
- 010 Manipulating Data 2 (9:57)
- 011 Manipulating Data 3 (10:12)
- 013 How To Download The Course Assignments (7:43)
07 - NumPy
Available in
days
days
after you enroll
- 001 Section Overview (2:41)
- 002 NumPy Introduction (5:17)
- 004 NumPy DataTypes and Attributes (14:05)
- 005 Creating NumPy Arrays (9:22)
- 006 NumPy Random Seed (7:17)
- 007 Viewing Arrays and Matrices (9:35)
- 008 Manipulating Arrays (11:32)
- 009 Manipulating Arrays 2 (9:44)
- 010 Standard Deviation and Variance (7:10)
- 011 Reshape and Transpose (7:26)
- 012 Dot Product vs Element Wise (11:45)
- 013 Exercise_ Nut Butter Store Sales (13:04)
- 014 Comparison Operators (3:33)
- 015 Sorting Arrays (6:20)
- 016 Turn Images Into NumPy Arrays (7:37)
08 - Matplotlib_ Plotting and Data Visualization
Available in
days
days
after you enroll
- 001 Section Overview (1:50)
- 002 Matplotlib Introduction (5:16)
- 003 Importing And Using Matplotlib (11:36)
- 004 Anatomy Of A Matplotlib Figure (9:19)
- 005 Scatter Plot And Bar Plot (10:09)
- 006 Histograms And Subplots (8:40)
- 007 Subplots Option 2 (4:15)
- 008 Quick Tip_ Data Visualizations (1:48)
- 009 Plotting From Pandas DataFrames (5:58)
- 011 Plotting From Pandas DataFrames 2 (10:33)
- 012 Plotting from Pandas DataFrames 3 (8:32)
- 013 Plotting from Pandas DataFrames 4 (6:36)
- 014 Plotting from Pandas DataFrames 5 (8:29)
- 015 Plotting from Pandas DataFrames 6 (8:28)
- 016 Plotting from Pandas DataFrames 7 (11:20)
- 017 Customizing Your Plots (10:09)
- 018 Customizing Your Plots 2 (9:41)
- 019 Saving And Sharing Your Plots (4:14)
09 - Scikit-learn_ Creating Machine Learning Models
Available in
days
days
after you enroll
- 001 Section Overview (2:30)
- 002 Scikit-learn Introduction (6:41)
- 004 Refresher_ What Is Machine Learning_ (5:40)
- 006 Scikit-learn Cheatsheet (6:13)
- 007 Typical scikit-learn Workflow (23:14)
- 008 Optional_ Debugging Warnings In Jupyter (18:57)
- 009 Getting Your Data Ready_ Splitting Your Data (8:37)
- 010 Quick Tip_ Clean, Transform, Reduce (5:03)
- 011 Getting Your Data Ready_ Convert Data To Numbers (16:54)
- 013 Getting Your Data Ready_ Handling Missing Values With Pandas (12:22)
- 016 Getting Your Data Ready_ Handling Missing Values With Scikit-learn (17:29)
- 017 NEW_ Choosing The Right Model For Your Data (20:14)
- 018 NEW_ Choosing The Right Model For Your Data 2 (Regression) (11:21)
- 020 Quick Tip_ How ML Algorithms Work (1:25)
- 021 Choosing The Right Model For Your Data 3 (Classification) (12:45)
- 022 Fitting A Model To The Data (6:45)
- 023 Making Predictions With Our Model (8:25)
- 024 predict() vs predict_proba() (8:33)
- 025 NEW_ Making Predictions With Our Model (Regression) (8:48)
- 026 NEW_ Evaluating A Machine Learning Model (Score) Part 1 (9:41)
- 027 NEW_ Evaluating A Machine Learning Model (Score) Part 2 (6:47)
- 028 Evaluating A Machine Learning Model 2 (Cross Validation) (13:16)
- 029 Evaluating A Classification Model 1 (Accuracy) (4:46)
- 030 Evaluating A Classification Model 2 (ROC Curve) (9:04)
- 031 Evaluating A Classification Model 3 (ROC Curve) (7:44)
- 033 Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
- 034 NEW_ Evaluating A Classification Model 5 (Confusion Matrix) (14:23)
- 035 Evaluating A Classification Model 6 (Classification Report) (10:16)
- 036 NEW_ Evaluating A Regression Model 1 (R2 Score) (9:59)
- 037 NEW_ Evaluating A Regression Model 2 (MAE) (7:22)
- 038 NEW_ Evaluating A Regression Model 3 (MSE) (9:49)
- 040 NEW_ Evaluating A Model With Cross Validation and Scoring Parameter (25:19)
- 041 NEW_ Evaluating A Model With Scikit-learn Functions (14:02)
- 042 Improving A Machine Learning Model (11:16)
- 043 Tuning Hyperparameters (23:15)
- 044 Tuning Hyperparameters 2 (14:23)
- 045 Tuning Hyperparameters 3 (14:59)
- 047 Quick Tip_ Correlation Analysis (2:28)
- 048 Saving And Loading A Model (7:29)
- 049 Saving And Loading A Model 2 (6:20)
- 050 Putting It All Together (20:19)
- 051 Putting It All Together 2 (11:34)
11 - Milestone Project 1_ Supervised Learning (Classification)
Available in
days
days
after you enroll
- 001 Section Overview (2:09)
- 002 Project Overview (6:09)
- 003 Project Environment Setup (10:59)
- 004 Optional_ Windows Project Environment Setup (4:52)
- 005 Step 1~4 Framework Setup (12:06)
- 006 Getting Our Tools Ready (9:04)
- 007 Exploring Our Data (8:33)
- 008 Finding Patterns (10:03)
- 009 Finding Patterns 2 (16:48)
- 010 Finding Patterns 3 (13:37)
- 011 Preparing Our Data For Machine Learning (8:51)
- 012 Choosing The Right Models (10:15)
- 013 Experimenting With Machine Learning Models (6:31)
- 014 Tuning_Improving Our Model (13:49)
- 015 Tuning Hyperparameters (11:27)
- 016 Tuning Hyperparameters 2 (11:49)
- 017 Tuning Hyperparameters 3 (7:06)
- 019 Evaluating Our Model (11:00)
- 020 Evaluating Our Model 2 (5:55)
- 021 Evaluating Our Model 3 (8:50)
- 022 Finding The Most Important Features (16:07)
- 023 Reviewing The Project (9:13)
12 - Milestone Project 2_ Supervised Learning (Time Series Data)
Available in
days
days
after you enroll
- 001 Section Overview (1:07)
- 002 Project Overview (4:24)
- 004 Project Environment Setup (10:52)
- 005 Step 1~4 Framework Setup (8:36)
- 006 Exploring Our Data (14:16)
- 007 Exploring Our Data 2 (6:16)
- 008 Feature Engineering (15:24)
- 009 Turning Data Into Numbers (15:38)
- 010 Filling Missing Numerical Values (12:49)
- 011 Filling Missing Categorical Values (8:27)
- 012 Fitting A Machine Learning Model (7:16)
- 013 Splitting Data (10:01)
- 015 Custom Evaluation Function (11:13)
- 016 Reducing Data (10:36)
- 017 RandomizedSearchCV (9:32)
- 018 Improving Hyperparameters (8:11)
- 019 Preproccessing Our Data (13:15)
- 020 Making Predictions (9:18)
- 021 Feature Importance (13:50)
13 - Data Engineering
Available in
days
days
after you enroll
- 001 Data Engineering Introduction (3:24)
- 002 What Is Data_ (6:42)
- 003 What Is A Data Engineer_ (4:21)
- 004 What Is A Data Engineer 2_ (5:36)
- 005 What Is A Data Engineer 3_ (5:04)
- 006 What Is A Data Engineer 4_ (3:22)
- 007 Types Of Databases (6:50)
- 009 Optional_ OLTP Databases (10:54)
- 011 Hadoop, HDFS and MapReduce (4:22)
- 012 Apache Spark and Apache Flink (2:07)
- 013 Kafka and Stream Processing (4:33)
14 - Neural Networks_ Deep Learning, Transfer Learning and TensorFlow 2
Available in
days
days
after you enroll
- 001 Section Overview (2:06)
- 002 Deep Learning and Unstructured Data (13:36)
- 004 Setting Up Google Colab (7:17)
- 005 Google Colab Workspace (4:23)
- 006 Uploading Project Data (6:52)
- 007 Setting Up Our Data (4:40)
- 008 Setting Up Our Data 2 (1:32)
- 009 Importing TensorFlow 2 (12:43)
- 010 Optional_ TensorFlow 2.0 Default Issue (3:39)
- 011 Using A GPU (8:59)
- 012 Optional_ GPU and Google Colab (4:27)
- 013 Optional_ Reloading Colab Notebook (6:49)
- 014 Loading Our Data Labels (12:04)
- 015 Preparing The Images (12:32)
- 016 Turning Data Labels Into Numbers (12:12)
- 017 Creating Our Own Validation Set (9:18)
- 018 Preprocess Images (10:25)
- 019 Preprocess Images 2 (11:00)
- 020 Turning Data Into Batches (9:37)
- 021 Turning Data Into Batches 2 (17:54)
- 022 Visualizing Our Data (12:41)
- 023 Preparing Our Inputs and Outputs (6:38)
- 025 Building A Deep Learning Model (11:42)
- 026 Building A Deep Learning Model 2 (10:53)
- 027 Building A Deep Learning Model 3 (9:06)
- 028 Building A Deep Learning Model 4 (9:12)
- 029 Summarizing Our Model (4:52)
- 030 Evaluating Our Model (9:27)
- 031 Preventing Overfitting (4:20)
- 032 Training Your Deep Neural Network (19:09)
- 033 Evaluating Performance With TensorBoard (7:30)
- 034 Make And Transform Predictions (15:05)
- 035 Transform Predictions To Text (15:20)
- 036 Visualizing Model Predictions (14:46)
- 037 Visualizing And Evaluate Model Predictions 2 (15:52)
- 038 Visualizing And Evaluate Model Predictions 3 (10:40)
- 039 Saving And Loading A Trained Model (13:34)
- 040 Training Model On Full Dataset (15:02)
- 041 Making Predictions On Test Images (16:55)
- 042 Submitting Model to Kaggle (14:14)
- 043 Making Predictions On Our Images (15:15)
15 - Storytelling + Communication_ How To Present Your Work
Available in
days
days
after you enroll
16 - Career Advice + Extra Bits
Available in
days
days
after you enroll
17 - Learn Python
Available in
days
days
after you enroll
- 001 What Is A Programming Language (6:24)
- 002 Python Interpreter (7:04)
- 003 How To Run Python Code (4:53)
- 004 Our First Python Program (7:43)
- 005 Latest Version Of Python (1:58)
- 006 Python 2 vs Python 3 (6:41)
- 007 Exercise_ How Does Python Work_ (2:10)
- 008 Learning Python (2:05)
- 009 Python Data Types (4:46)
- 011 Numbers (11:09)
- 012 Math Functions (4:29)
- 013 DEVELOPER FUNDAMENTALS_ I (4:07)
- 014 Operator Precedence (3:10)
- 016 Optional_ bin() and complex (4:02)
- 017 Variables (13:12)
- 018 Expressions vs Statements (1:36)
- 019 Augmented Assignment Operator (2:49)
- 020 Strings (5:30)
- 021 String Concatenation (1:16)
- 022 Type Conversion (3:03)
- 023 Escape Sequences (4:23)
- 024 Formatted Strings (8:24)
- 025 String Indexes (8:57)
- 026 Immutability (3:13)
- 027 Built-In Functions + Methods (10:03)
- 028 Booleans (3:21)
- 029 Exercise_ Type Conversion (8:23)
- 030 DEVELOPER FUNDAMENTALS_ II (4:42)
- 031 Exercise_ Password Checker (7:21)
- 032 Lists (5:01)
- 033 List Slicing (7:48)
- 034 Matrix (4:11)
- 035 List Methods (10:28)
- 036 List Methods 2 (4:24)
- 037 List Methods 3 (4:52)
- 038 Common List Patterns (5:57)
- 039 List Unpacking (2:41)
- 040 None (1:51)
- 041 Dictionaries (6:21)
- 042 DEVELOPER FUNDAMENTALS_ III (2:40)
- 043 Dictionary Keys (3:37)
- 044 Dictionary Methods (4:37)
- 045 Dictionary Methods 2 (7:04)
- 046 Tuples (4:46)
- 047 Tuples 2 (3:14)
- 048 Sets (7:24)
- 049 Sets 2 (8:45)
18 - Learn Python Part 2
Available in
days
days
after you enroll
- 001 Breaking The Flow (2:35)
- 002 Conditional Logic (13:18)
- 003 Indentation In Python (4:38)
- 004 Truthy vs Falsey (5:18)
- 005 Ternary Operator (4:14)
- 006 Short Circuiting (4:02)
- 007 Logical Operators (6:56)
- 008 Exercise_ Logical Operators (7:47)
- 009 is vs == (7:36)
- 010 For Loops (7:01)
- 011 Iterables (6:44)
- 012 Exercise_ Tricky Counter (3:23)
- 013 range() (5:39)
- 014 enumerate() (4:37)
- 015 While Loops (6:28)
- 016 While Loops 2 (5:49)
- 017 break, continue, pass (4:15)
- 018 Our First GUI (8:49)
- 019 DEVELOPER FUNDAMENTALS_ IV (6:34)
- 020 Exercise_ Find Duplicates (3:55)
- 021 Functions (7:41)
- 022 Parameters and Arguments (4:25)
- 023 Default Parameters and Keyword Arguments (5:41)
- 024 return (13:11)
- 026 Methods vs Functions (4:33)
- 027 Docstrings (3:47)
- 028 Clean Code (4:38)
- 029 _args and __kwargs (7:57)
- 030 Exercise_ Functions (4:18)
- 031 Scope (3:38)
- 032 Scope Rules (6:55)
- 033 global Keyword (6:13)
- 034 nonlocal Keyword (3:21)
- 035 Why Do We Need Scope_ (3:38)
- 036 Pure Functions (9:23)
- 037 map() (6:30)
- 038 filter() (4:23)
- 039 zip() (3:28)
- 040 reduce() (7:32)
- 041 List Comprehensions (8:37)
- 042 Set Comprehensions (6:27)
- 043 Exercise_ Comprehensions (4:36)
- 045 Modules in Python (10:54)
- 047 Optional_ PyCharm (8:19)
- 048 Packages in Python (10:45)
- 049 Different Ways To Import (7:03)
CipSkills is a global platform for learning courses online. The school revolves around being sustainable by providing quality education around the globe and that too at affordable prices for people who have a strong longing to learn.
FOLLOW US
Offer Ending Soon